diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000..6a8fbff
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "MaskTheFace_New"]
+	path = MaskTheFace_New
+	url = https://github.com/aqeelanwar/MaskTheFace.git
diff --git a/MaskTheFace b/MaskTheFace
new file mode 160000
index 0000000..caf4d6c
--- /dev/null
+++ b/MaskTheFace
@@ -0,0 +1 @@
+Subproject commit caf4d6ca2328f6f97c5f1947db4f03b3197fb62a
diff --git a/MaskTheFaceNew/LICENSE b/MaskTheFaceNew/LICENSE
new file mode 100644
index 0000000..8750c6d
--- /dev/null
+++ b/MaskTheFaceNew/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Aqeel Anwar
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/MaskTheFaceNew/_config.yml b/MaskTheFaceNew/_config.yml
new file mode 100644
index 0000000..c419263
--- /dev/null
+++ b/MaskTheFaceNew/_config.yml
@@ -0,0 +1 @@
+theme: jekyll-theme-cayman
\ No newline at end of file
diff --git a/MaskTheFaceNew/dlib_models/shape_predictor_68_face_landmarks.dat b/MaskTheFaceNew/dlib_models/shape_predictor_68_face_landmarks.dat
new file mode 100644
index 0000000..e0ec20d
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diff --git a/MaskTheFaceNew/download_links.txt b/MaskTheFaceNew/download_links.txt
new file mode 100644
index 0000000..43bf3ad
--- /dev/null
+++ b/MaskTheFaceNew/download_links.txt
@@ -0,0 +1 @@
+mfr2: 1ukk0n_srRqcsotK2MjlFPj7L0sXcR2fH
\ No newline at end of file
diff --git a/MaskTheFaceNew/faq.md b/MaskTheFaceNew/faq.md
new file mode 100644
index 0000000..25ad87d
--- /dev/null
+++ b/MaskTheFaceNew/faq.md
@@ -0,0 +1,26 @@
+# MaskTheFace - Convert face dataset to masked dataset
+![cover_photo](images/MaskTheFace.png)
+## Frequently asked question (FAQ)
+
+### 1. dlib ImportError: DLL load failed
+If you are running MaskTheFace on windows, you might face dlib import error. You can solve this error by uninstalling dlib installed through requirements.txt and install from scratch. Use the following commands
+```
+pip uninstall dlib
+git clone https://github.com/davisking/dlib.git
+cd dlib
+python setup.py install
+```
+This will build and install the dlib python library. Make sure you are in virtual environment you plan on using MaskTheFace in when you run the above commands.
+
+### 2. fetch_dataset.py util import error
+fetch_dataset.py is supposed to be run from the parent folder i.e.
+
+```
+# Correct
+cd MaskTheFace
+python utils/fetch_dataset.py
+
+# Wrong
+cd MaskTheFace/utils
+python fetch_dataset.py
+```
\ No newline at end of file
diff --git a/MaskTheFaceNew/images/MaskTheFace.png b/MaskTheFaceNew/images/MaskTheFace.png
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diff --git a/MaskTheFaceNew/images/angle.png b/MaskTheFaceNew/images/angle.png
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diff --git a/MaskTheFaceNew/images/block_diag.png b/MaskTheFaceNew/images/block_diag.png
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diff --git a/MaskTheFaceNew/images/brightness.png b/MaskTheFaceNew/images/brightness.png
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diff --git a/MaskTheFaceNew/images/colors.png b/MaskTheFaceNew/images/colors.png
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diff --git a/MaskTheFaceNew/images/dataset.png b/MaskTheFaceNew/images/dataset.png
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diff --git a/MaskTheFaceNew/images/example1.png b/MaskTheFaceNew/images/example1.png
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diff --git a/MaskTheFaceNew/images/face_recognition.png b/MaskTheFaceNew/images/face_recognition.png
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diff --git a/MaskTheFaceNew/images/intensities.png b/MaskTheFaceNew/images/intensities.png
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diff --git a/MaskTheFaceNew/images/mask_no_mask.png b/MaskTheFaceNew/images/mask_no_mask.png
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diff --git a/MaskTheFaceNew/images/mask_types.png b/MaskTheFaceNew/images/mask_types.png
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diff --git a/MaskTheFaceNew/images/mfr2.png b/MaskTheFaceNew/images/mfr2.png
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index 0000000..31ede2b
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diff --git a/MaskTheFaceNew/images/multiface.png b/MaskTheFaceNew/images/multiface.png
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diff --git a/MaskTheFaceNew/images/run.png b/MaskTheFaceNew/images/run.png
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diff --git a/MaskTheFaceNew/images/textures.png b/MaskTheFaceNew/images/textures.png
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diff --git a/MaskTheFaceNew/mask.csv b/MaskTheFaceNew/mask.csv
new file mode 100644
index 0000000..025d6f8
--- /dev/null
+++ b/MaskTheFaceNew/mask.csv
@@ -0,0 +1,5421 @@
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diff --git a/MaskTheFaceNew/mask_the_face.py b/MaskTheFaceNew/mask_the_face.py
new file mode 100644
index 0000000..8729156
--- /dev/null
+++ b/MaskTheFaceNew/mask_the_face.py
@@ -0,0 +1,213 @@
+# Author: aqeelanwar
+# Created: 27 April,2020, 10:22 PM
+# Email: aqeel.anwar@gatech.edu
+
+import argparse
+import dlib
+from utils.aux_functions import *
+
+# Command-line input setup
+parser = argparse.ArgumentParser(
+    description="MaskTheFace - Python code to mask faces dataset"
+)
+parser.add_argument(
+    "--path",
+    type=str,
+    default="",
+    help="Path to either the folder containing images or the image itself",
+)
+parser.add_argument(
+    "--mask_type",
+    type=str,
+    default="surgical",
+    choices=["surgical", "N95", "KN95", "cloth",
+             "gas", "inpaint", "random", "all"],
+    help="Type of the mask to be applied. Available options: all, surgical_blue, surgical_green, N95, cloth",
+)
+
+parser.add_argument(
+    "--pattern",
+    type=str,
+    default="",
+    help="Type of the pattern. Available options in masks/textures",
+)
+
+parser.add_argument(
+    "--pattern_weight",
+    type=float,
+    default=0.5,
+    help="Weight of the pattern. Must be between 0 and 1",
+)
+
+parser.add_argument(
+    "--color",
+    type=str,
+    default="#0473e2",
+    help="Hex color value that need to be overlayed to the mask",
+)
+
+parser.add_argument(
+    "--color_weight",
+    type=float,
+    default=0.5,
+    help="Weight of the color intensity. Must be between 0 and 1",
+)
+
+parser.add_argument(
+    "--code",
+    type=str,
+    # default="cloth-masks/textures/check/check_4.jpg, cloth-#e54294, cloth-#ff0000, cloth, cloth-masks/textures/others/heart_1.png, cloth-masks/textures/fruits/pineapple.png, N95, surgical_blue, surgical_green",
+    default="",
+    help="Generate specific formats",
+)
+
+
+parser.add_argument(
+    "--verbose", dest="verbose", action="store_true", help="Turn verbosity on"
+)
+parser.add_argument(
+    "--write_original_image",
+    dest="write_original_image",
+    action="store_true",
+    help="If true, original image is also stored in the masked folder",
+)
+parser.set_defaults(feature=False)
+
+args = parser.parse_args()
+args.write_path = args.path + "_masked"
+
+# Set up dlib face detector and predictor
+args.detector = dlib.get_frontal_face_detector()
+path_to_dlib_model = "dlib_models/shape_predictor_68_face_landmarks.dat"
+if not os.path.exists(path_to_dlib_model):
+    download_dlib_model()
+
+args.predictor = dlib.shape_predictor(path_to_dlib_model)
+
+# Extract data from code
+mask_code = "".join(args.code.split()).split(",")
+args.code_count = np.zeros(len(mask_code))
+args.mask_dict_of_dict = {}
+
+
+for i, entry in enumerate(mask_code):
+    mask_dict = {}
+    mask_color = ""
+    mask_texture = ""
+    mask_type = entry.split("-")[0]
+    if len(entry.split("-")) == 2:
+        mask_variation = entry.split("-")[1]
+        if "#" in mask_variation:
+            mask_color = mask_variation
+        else:
+            mask_texture = mask_variation
+    mask_dict["type"] = mask_type
+    mask_dict["color"] = mask_color
+    mask_dict["texture"] = mask_texture
+    args.mask_dict_of_dict[i] = mask_dict
+
+# Check if path is file or directory or none
+is_directory, is_file, is_other = check_path(args.path)
+display_MaskTheFace()
+
+if is_directory:
+    path, dirs, files = os.walk(args.path).__next__()
+    file_count = len(files)
+    dirs_count = len(dirs)
+    if len(files) > 0:
+        print_orderly("Masking image files", 60)
+
+    # Process files in the directory if any
+    for f in tqdm(files):
+        image_path = path + "/" + f
+
+        write_path = path + "_masked"
+        if not os.path.isdir(write_path):
+            os.makedirs(write_path)
+
+        if is_image(image_path):
+            # Proceed if file is image
+            if args.verbose:
+                str_p = "Processing: " + image_path
+                tqdm.write(str_p)
+
+            split_path = f.rsplit(".")
+            masked_image, mask, mask_binary_array, original_image = mask_image(f,
+                                                                               image_path, args
+                                                                               )
+            for i in range(len(mask)):
+                w_path = (
+                    write_path
+                    + "/"
+                    + split_path[0]
+                    + "_"
+                    + mask[i]
+                    + "."
+                    + split_path[1]
+                )
+                img = masked_image[i]
+                cv2.imwrite(w_path, img)
+
+    print_orderly("Masking image directories", 60)
+
+    # Process directories withing the path provided
+    for d in tqdm(dirs):
+        dir_path = args.path + "/" + d
+        dir_write_path = args.write_path + "/" + d
+        if not os.path.isdir(dir_write_path):
+            os.makedirs(dir_write_path)
+        _, _, files = os.walk(dir_path).__next__()
+
+        # Process each files within subdirectory
+        for f in files:
+            image_path = dir_path + "/" + f
+            if args.verbose:
+                str_p = "Processing: " + image_path
+                tqdm.write(str_p)
+            write_path = dir_write_path
+            if is_image(image_path):
+                # Proceed if file is image
+                split_path = f.rsplit(".")
+                masked_image, mask, mask_binary, original_image = mask_image(f,
+                                                                             image_path, args
+                                                                             )
+                for i in range(len(mask)):
+                    w_path = (
+                        write_path
+                        + "/"
+                        + split_path[0]
+                        + "_"
+                        + mask[i]
+                        + "."
+                        + split_path[1]
+                    )
+                    w_path_original = write_path + "/" + f
+                    img = masked_image[i]
+                    # Write the masked image
+                    cv2.imwrite(w_path, img)
+                    if args.write_original_image:
+                        # Write the original image
+                        cv2.imwrite(w_path_original, original_image)
+
+            if args.verbose:
+                print(args.code_count)
+
+# Process if the path was a file
+elif is_file:
+    print("Masking image file")
+    image_path = args.path
+    write_path = args.path.rsplit(".")[0]
+    if is_image(image_path):
+        # Proceed if file is image
+        # masked_images, mask, mask_binary_array, original_image
+        masked_image, mask, mask_binary_array, original_image = mask_image(
+            f, image_path, args
+        )
+        for i in range(len(mask)):
+            w_path = write_path + "_" + \
+                mask[i] + "." + args.path.rsplit(".")[1]
+            img = masked_image[i]
+            cv2.imwrite(w_path, img)
+else:
+    print("Path is neither a valid file or a valid directory")
+print("Processing Done")
diff --git a/MaskTheFaceNew/masks/masks.cfg b/MaskTheFaceNew/masks/masks.cfg
new file mode 100644
index 0000000..b1f2bf6
--- /dev/null
+++ b/MaskTheFaceNew/masks/masks.cfg
@@ -0,0 +1,209 @@
+[surgical]
+template:   masks/templates/surgical.png
+mask_a:     21, 97
+mask_b:     307, 22
+mask_c:     600, 99
+mask_d:     25, 322
+mask_e:     295, 470
+mask_f:     600, 323
+
+[surgical_left]
+template:   masks/templates/surgical_left.png
+mask_a:     39, 27
+mask_b:     130, 9
+mask_c:     567, 20
+mask_d:     87, 207
+mask_e:     168, 302
+mask_f:     568, 202
+
+[surgical_right]
+template:   masks/templates/surgical_right.png
+mask_a:     3, 20
+mask_b:     440, 9
+mask_c:     531, 27
+mask_d:     2, 202
+mask_e:     402, 302
+mask_f:     483, 207
+
+[surgical_green]
+template:   masks/templates/surgical_green.png
+mask_a:     21, 97
+mask_b:     307, 22
+mask_c:     600, 99
+mask_d:     25, 322
+mask_e:     295, 470
+mask_f:     600, 323
+
+[surgical_green_left]
+template:   masks/templates/surgical_green_left.png
+mask_a:     39, 27
+mask_b:     130, 9
+mask_c:     567, 20
+mask_d:     87, 207
+mask_e:     168, 302
+mask_f:     568, 202
+
+[surgical_green_right]
+template:   masks/templates/surgical_green_right.png
+mask_a:     3, 20
+mask_b:     440, 9
+mask_c:     531, 27
+mask_d:     2, 202
+mask_e:     402, 302
+mask_f:     483, 207
+
+[surgical_blue]
+template:   masks/templates/surgical_blue.png
+mask_a:     21, 97
+mask_b:     307, 22
+mask_c:     600, 99
+mask_d:     25, 322
+mask_e:     295, 470
+mask_f:     600, 323
+
+[surgical_blue_left]
+template:   masks/templates/surgical_blue_left.png
+mask_a:     39, 27
+mask_b:     130, 9
+mask_c:     567, 20
+mask_d:     87, 207
+mask_e:     168, 302
+mask_f:     568, 202
+
+[surgical_blue_right]
+template:   masks/templates/surgical_blue_right.png
+mask_a:     3, 20
+mask_b:     440, 9
+mask_c:     531, 27
+mask_d:     2, 202
+mask_e:     402, 302
+mask_f:     483, 207
+
+
+[N95]
+template:   masks/templates/N95.png
+mask_a:     15, 119
+mask_b:     327, 5
+mask_c:     640, 93
+mask_d:     13, 285
+mask_e:     351, 518
+mask_f:     645, 285
+
+;[N95_left]
+;template:   masks/N95_left.png
+;mask_a:     176, 121
+;mask_b:     313, 46
+;mask_c:     799, 135
+;mask_d:     97, 438
+;mask_e:     329, 627
+;mask_f:     791, 401
+
+[N95_right]
+template:   masks/templates/N95_right.png
+mask_c:     979, 331
+mask_b:     806, 172
+mask_a:     12, 222
+mask_f:     907, 762
+mask_e:     577, 875
+mask_d:     -4, 632
+
+[N95_left]
+template:   masks/templates/N95_left.png
+mask_a:     193, 331
+mask_b:     366, 172
+mask_c:     1160, 222
+mask_d:     265, 762
+mask_e:     595, 875
+mask_f:     1176, 632
+
+
+[cloth_left]
+template:   masks/templates/cloth_left.png
+mask_a:     65, 93
+mask_b:     162, 15
+mask_c:     672, 75
+mask_d:     114, 296
+mask_e:     207, 443
+mask_f:     671, 341
+
+[cloth_right]
+template:   masks/templates/cloth_right.png
+mask_a:     98, 93
+mask_b:     608, 15
+mask_c:     705, 75
+mask_d:     99, 296
+mask_e:     563, 443
+mask_f:     656, 341
+
+[cloth]
+template:   masks/templates/cloth.png
+mask_a:     122, 90
+mask_b:     405, 7
+mask_c:     686, 79
+mask_d:     165, 323
+mask_e:     406, 509
+mask_f:     653, 311
+
+[gas]
+template: masks/templates/gas.png
+mask_a: 330, 431
+mask_b: 873, 117
+mask_c: 1494, 434
+mask_d: 430, 754
+mask_e: 869, 1100
+mask_f: 1400, 710
+
+[gas_left]
+template: masks/templates/gas_left.png
+mask_a: 239, 238
+mask_b: 317, 42
+mask_c: 965, 239
+mask_d: 224, 404
+mask_e: 337, 502
+mask_f: 963, 406
+
+[gas_right]
+template: masks/templates/gas_right.png
+mask_c: 621, 238
+mask_b: 543, 60
+mask_a: -105, 239
+mask_f: 636, 404
+mask_e: 523, 502
+mask_d: -103, 406
+
+[KN95]
+template: masks/templates/KN95.png
+mask_a: 20, 47
+mask_b: 410, 5
+mask_c: 760, 55
+mask_d: 75, 340
+mask_e: 398, 600
+mask_f: 671, 320
+
+[KN95_left]
+template: masks/templates/KN95_left.png
+mask_a: 52, 258
+mask_b: 207, 100
+mask_c: 730, 80
+mask_d: 210, 408
+mask_e: 335, 604
+mask_f: 770, 270
+
+[KN95_right]
+template: masks/templates/KN95_right.png
+mask_c: 664, 258
+mask_b: 509, 100
+mask_a: -14, 80
+mask_f: 506, 408
+mask_e: 381, 604
+mask_d: -54, 270
+
+
+[empty]
+[empty_left]
+[empty_right]
+
+[inpaint]
+[inpaint_left]
+[inpaint_right]
+
diff --git a/MaskTheFaceNew/masks/templates/KN95.png b/MaskTheFaceNew/masks/templates/KN95.png
new file mode 100644
index 0000000..2100470
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diff --git a/MaskTheFaceNew/masks/templates/KN95_left.png b/MaskTheFaceNew/masks/templates/KN95_left.png
new file mode 100644
index 0000000..a5696a9
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diff --git a/MaskTheFaceNew/masks/templates/KN95_right.png b/MaskTheFaceNew/masks/templates/KN95_right.png
new file mode 100644
index 0000000..0bd0c79
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diff --git a/MaskTheFaceNew/masks/templates/N95.png b/MaskTheFaceNew/masks/templates/N95.png
new file mode 100644
index 0000000..a07b9e2
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diff --git a/MaskTheFaceNew/masks/templates/N95_left.png b/MaskTheFaceNew/masks/templates/N95_left.png
new file mode 100644
index 0000000..aee1c5c
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diff --git a/MaskTheFaceNew/masks/templates/N95_right.png b/MaskTheFaceNew/masks/templates/N95_right.png
new file mode 100644
index 0000000..2eddf8e
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diff --git a/MaskTheFaceNew/masks/templates/cloth.png b/MaskTheFaceNew/masks/templates/cloth.png
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index 0000000..1c24374
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diff --git a/MaskTheFaceNew/masks/templates/cloth_left.png b/MaskTheFaceNew/masks/templates/cloth_left.png
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index 0000000..fabe069
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diff --git a/MaskTheFaceNew/masks/templates/cloth_right.png b/MaskTheFaceNew/masks/templates/cloth_right.png
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index 0000000..3197a64
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diff --git a/MaskTheFaceNew/masks/templates/gas.png b/MaskTheFaceNew/masks/templates/gas.png
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index 0000000..b46250f
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diff --git a/MaskTheFaceNew/masks/templates/gas_left.png b/MaskTheFaceNew/masks/templates/gas_left.png
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index 0000000..c33d3c3
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diff --git a/MaskTheFaceNew/masks/templates/gas_right.png b/MaskTheFaceNew/masks/templates/gas_right.png
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index 0000000..2ee33fd
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diff --git a/MaskTheFaceNew/masks/templates/surgical.png b/MaskTheFaceNew/masks/templates/surgical.png
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index 0000000..f1ee12c
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diff --git a/MaskTheFaceNew/masks/templates/surgical_blue.png b/MaskTheFaceNew/masks/templates/surgical_blue.png
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index 0000000..860eb15
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diff --git a/MaskTheFaceNew/masks/templates/surgical_blue_left.png b/MaskTheFaceNew/masks/templates/surgical_blue_left.png
new file mode 100644
index 0000000..e8c0292
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diff --git a/MaskTheFaceNew/masks/templates/surgical_blue_right.png b/MaskTheFaceNew/masks/templates/surgical_blue_right.png
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index 0000000..24f317e
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diff --git a/MaskTheFaceNew/masks/templates/surgical_green.png b/MaskTheFaceNew/masks/templates/surgical_green.png
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index 0000000..6d90826
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diff --git a/MaskTheFaceNew/masks/templates/surgical_green_left.png b/MaskTheFaceNew/masks/templates/surgical_green_left.png
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+# MaskTheFace - Convert face dataset to masked dataset
+![cover_photo](images/MaskTheFace.png)
+## What is MaskTheFace?
+MaskTheFace is computer vision-based script to mask faces in images. It uses a dlib based face landmarks detector to identify the face tilt and six key features of the face necessary for applying mask. Based on the face tilt, corresponding mask template is selected from the library of mask. The template mask is then transformed based on the six key features to fit perfectly on the face. The complete block diagram can be seen below. MaskTheFace provides a number of masks to select from. It is difficult to collect mask dataset under various conditions. MaskTheFace can be used to convert any existing face dataset to masked-face dataset. MaskTheFace identifies all the faces within an image, and applies the user selected masks to them taking into account various limitations such as face angle, mask fit, lighting conditions etc. A single image, or entire directory of images can be used as input to code.
+![block_diagram](images/block_diag.png)
+
+## How to install MaskTheFace
+It’s advisable to [make a new virtual environment](https://towardsdatascience.com/setting-up-python-platform-for-machine-learning-projects-cfd85682c54b) with Python 3.6 and install the dependencies. Following steps can be taken to download get started with MaskTheFace
+### Clone the repository
+```
+git clone https://github.com/aqeelanwar/MaskTheFace.git
+```
+
+### Install required packages
+The provided requirements.txt file can be used to install all the required packages. Use the following command
+
+```
+cd MaskTheFace
+pip install –r requirements.txt
+```
+
+This will install the required packages in the activated Python environment.
+
+## How to run MaskTheFace
+
+```
+cd MaskTheFace
+# Generic
+python mask_the_face.py --path <path-to-file-or-dir> --mask_type <type-of-mask> --verbose --write_original_image
+
+# Example
+python mask_the_face.py --path 'data/office.jpg' --mask_type 'N95' --verbose --write_original_image
+```
+![cover_photo](images/run.png)
+### Arguments
+|    Argument    |                                                                                                       Explanation                                                                                                       |
+|:--------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+|      path      |                                                                            Path to the image file or a folder containing images to be masked                                                                            |
+|    mask_type   | Select the mask to be applied. Available options are 'N95', 'surgical_blue', 'surgical_green', 'cloth', 'empty' and 'inpaint'. The details of these mask types can be seen in the image above. More masks will be added |
+|     pattern    |                                 Selects the pattern to be applied to the mask-type selected above. The textures are available in the masks/textures folder. User can add more textures.                                 |
+| pattern_weight |                                   Selects the intensity of the pattern to be applied on the mask. The value should be between 0 (no texture strength) to 1 (maximum texture strength)                                   |
+|      color     |                                                         Selects the color to be applied to the mask-type selected above. The colors are provided as hex values.                                                         |
+|  color_weight  |                                      Selects the intensity of the color to be applied on the mask. The value should be between 0 (no color strength) to 1 (maximum color strength)                                      |
+|      code      |                                                              Can be used to create specific mask formats at random. More can be found in the section below.                                                             |
+|     verbose    |                                                                          If set to True, will be used to display useful messages during masking                                                                         |
+|write_original_image|                   If used, the original unmasked image will also be saved in the masked image folder along with processed masked image                                                                              |
+
+### Explanation:
+#### 1. _--code_
+The --code argument can be used to apply different type of masks uniformly at random to the images in the dataset.
+```
+# Generic format
+--code < masktype1 >-< color as hex or texture file path >, < masktype2 >-< color as hex or texture file path>, ...
+
+# Example
+--code cloth-masks/textures/check/check_4.jpg, cloth-#e54294, cloth-#ff0000, cloth, cloth-masks/textures/others/heart_1.png, cloth-masks/textures/fruits/pineapple.png, N95
+```
+The example above will apply 7 different masks, uniform at random, to the selected face dataset (via --path argument).
+## Frequently Asked Questions (FAQ)
+Click [here](faq.md) to troubleshoot errors faced while installing and running MaskTheFace
+
+## Supported Masks:
+### Mask Types:
+Currently MaskTheFace supports the following 4 mask types
+1. Surgical
+2. N95
+3. KN95
+4. Cloth
+5. Gas
+
+![mask_types](images/mask_types.png)
+
+New masks are being added. Users, can also add custom masks following the guidelines provided.
+
+### Mask Variations:
+Each of the mask types mentioned above can be varied in the following terms to create even more masks
+#### 1. Textures/Patterns variations:
+MaskTheFace provides 24 existing patterns that can be applied to mask types above to create more variations of the graph. Moreover, users can easily add custom patterns following the guidelines provided.
+![mask_textures](images/textures.png)
+#### 2. Color variations:
+MaskTheFace provided script to modify existing mask types in terms of colors to generate variations of existing graphs.
+![mask_colors](images/colors.png)
+####  3. Intensity variations:
+MaskTheFace provided script to modify existing mask types in terms of intensity to generate variations of existing graphs.
+![mask_colors](images/intensities.png)
+
+
+## Features:
+### Support for multiple mask types
+![cover_photo](images/example1.png)
+### Support for both single and multi-face images:
+![cover_photo](images/multiface.png)
+### Wide face angle coverage
+![cover_photo](images/angle.png)
+### Brightness corrected mask application
+![cover_photo](images/brightness.png)
+### Bulk masking on datasets
+![cover_photo](images/dataset.png)
+
+
+---
+
+# MFR2 - Masked Faces in Real-World for Face Recognition
+Masked faces in real world for face recognition (MFR2) is a small dataset with 53 identities of celebrities and politicians with a total of 269 images that are collected from the internet. Each identity has on an average of 5 images. The dataset contains both masked and unmasked faces of the identities.
+The dataset is processed in terms of face alignment and image dimensions. Each image has a dimension of (160x160x3). Sample images from the MFR2 data-set and the mask distribution can be seen below.
+
+![mfr2](images/mfr2.png)
+
+## Download MFR2
+The dataset can be downloaded using the following command
+
+```
+cd MaskTheFace
+python utils/fetch_dataset.py --dataset mfr2
+```
+This will download and extract the mfr2 dataset in the datasets folder.
+
+```
+# path to mfr2
+MaskTheFace/datasets/mfr2
+```
+
+Alternatively, you can download mfr2.zip file from [here](https://drive.google.com/file/d/1ukk0n_srRqcsotK2MjlFPj7L0sXcR2fH/view?usp=sharing)
+
+## Contents of MFR2
+The downloaded dataset folder contains
+1. Folders with images of identities
+2. mfr2_labels.txt: A label text file with identity name, image number, and type of mask ground truth
+3. pairs.txt: A text file containing 848 positive and negative pairs to be used for testing
+
+---
+
+# Example usage
+
+## 1. Face recognition with masks
+Face recognition trained to usual face images has proven to give good accuracy. In the recent ongoing outbreak of Covid19, people have been advised to use face masks. With the majority of people using face masks, the face recognition system fails to perform. Due to limited mask images, there is not enough masked data available to train a new system. MaskTheFace can be used to create masked data set from an unmasked dataset which is then used to either fine-tune an existing or train a new face recognition system.
+
+### Example
+The paper below uses MaskTheFace for the application of masked face recognition and reports an increase of ∼38% in the true positive rate for the Facenet system. They also test the accuracy of the re-trained system on the MFR2 dataset and report similar accuracy.
+[https://arxiv.org/pdf/2008.11104.pdf](https://arxiv.org/pdf/2008.11104.pdf)
+
+
+
+## 2. Monitoring if people are using masks
+MaskTheFace generated datasets can be used to monitor if people are using face masks.
+
+### Example
+The detector above was trained on 2000 images (1000 mask, 1000 without mask) from the VGGface2 dataset. The masked images contained 4 different types of masks. A VGG16 network was trained on these images which achieved a 98.9% accuracy on the test dataset.
+
+![cover_photo](images/mask_no_mask.png)
+
+## 3. Classify masks
+MaskTheFace generated dataset can be used to classify among masks using a deep network.
+
+## Citation
+If you find this repository useful, please use following citation
+```
+@misc{anwar2020masked,
+title={Masked Face Recognition for Secure Authentication},
+author={Aqeel Anwar and Arijit Raychowdhury},
+year={2020},
+eprint={2008.11104},
+archivePrefix={arXiv},
+primaryClass={cs.CV}
+} 
+```
+
+
+## License
+This project is licensed under the MIT License - see the [LICENSE.md](LICENSE) file for details
diff --git a/MaskTheFaceNew/ree.py b/MaskTheFaceNew/ree.py
new file mode 100644
index 0000000..e69aa80
--- /dev/null
+++ b/MaskTheFaceNew/ree.py
@@ -0,0 +1,118 @@
+import cv2
+
+image = cv2.imread("normal_faces/00006c07d2b033d1.jpg")
+
+mask = [328, 317, 417, 326, 472, 367, 451, 406, 385, 417, 325, 377]
+
+cols = "mask_xtl,mask_ytl,mask_xtm,mask_ytm,mask_xtr,mask_ytr,mask_xbr,mask_ybr,mask_xbm,mask_ybm,mask_xbl,mask_ybl".split(
+    ',')
+
+y_cols = [i for i in cols if 'y' in i]
+x_cols = [i for i in cols if 'x' in i]
+ymin = min([mask[cols.index(i)] for i in y_cols])
+ymax = max([mask[cols.index(i)] for i in y_cols])
+xmin = min([mask[cols.index(i)] for i in x_cols])
+xmax = max([mask[cols.index(i)] for i in x_cols])
+
+cv2.circle(image, (xmin, ymin), 3, (0, 0, 255), -1)
+cv2.circle(image, (xmax, ymax), 3, (0, 0, 255), -1)
+
+# def handle_mouse_click(e, x, y, flags, param):
+
+#     if e == cv2.EVENT_LBUTTONDOWN:
+#         cv2.circle(image, (x, y), 3, (0, 0, 255), -1)
+#         cv2.imshow("Image", image)
+#         print("X: " + str(x))
+#         print("Y: " + str(y))
+
+# points1 = [[229.59984, 260.40552],
+#            [292.75168, 243.79257],
+#            [345.43906, 258.6347],
+#            [342.63953, 294.09265],
+#            [295.93002, 320.3842],
+#            [241.45047, 304.22742]]
+
+# points2 = [[548.568, 149.71957],
+#            [613.12213, 131.57373],
+#            [670.7916, 151.63983],
+#            [662.09875, 193.59552],
+#            [611.0434, 217.80844],
+#            [560.1271, 197.22649]]
+
+# points3 = [[705.8756, 298.95682],
+#            [747.1592, 288.6646],
+#            [791.6697, 306.06146],
+#            [779.6672, 334.3117],
+#            [743.137, 342.89935],
+#            [713.72723, 327.10797]]
+
+# points4 = [[476.02835, 433.00665],
+#            [532.4949, 434.4976],
+#            [578.76514, 456.88272],
+#            [569.63574, 484.41766],
+#            [517.46655, 492.923],
+#            [473.2537, 465.87402]]
+
+# all_points = [points1, points2, points3, points4]
+
+# c = []
+# for points in all_points:
+#     for i in range(len(points)):
+#         point = points[i]
+#         cv2.circle(image, (int(point[0]), int(point[1])), 3, (0, 0, i*40), -1)
+# cv2.circle(image, (733, 413), 3, (0, 0, 255), -1)
+# cv2.circle(image, (759, 411), 3, (0, 0, 255), -1)
+# cv2.circle(image, (850, 428), 3, (0, 0, 255), -1)
+# cv2.circle(image, (841, 467), 3, (0, 0, 255), -1)
+# cv2.circle(image, (758, 488), 3, (0, 0, 255), -1)
+# cv2.circle(image, (741, 464), 3, (0, 0, 255), -1)
+
+# 384, 348, 705, 669
+
+# 597,182,726,311
+
+
+# cv2.circle(image, (357, 348), 3, (0, 0, 255), -1)
+# cv2.circle(image, (400, 391), 3, (0, 0, 255), -1)
+
+# cv2.circle(image, (549, 295), 3, (0, 0, 255), -1)
+# cv2.circle(image, (592, 338), 3, (0, 0, 255), -1)
+
+# cv2.circle(image, (636, 285), 3, (0, 0, 255), -1)
+# cv2.circle(image, (688, 337), 3, (0, 0, 255), -1)
+
+# cv2.circle(image, (717, 349), 3, (0, 0, 255), -1)
+# cv2.circle(image, (769, 401), 3, (0, 0, 255), -1)
+
+# 338	434
+# 382	477
+
+# 348	357
+# 391	400
+
+# 295	549
+# 338	592
+
+# 285	636
+# 337	688
+
+# 349	717
+# 401	769
+
+
+# cv2.circle(image, (554, 82), 3, (0, 0, 255), -1)
+# cv2.circle(image, (683, 211), 3, (0, 0, 255), -1)
+
+# cv2.circle(image, (703, 255), 3, (0, 0, 255), -1)
+# cv2.circle(image, (792, 345), 3, (0, 0, 255), -1)
+
+# cv2.circle(image, (454, 383), 3, (0, 0, 255), -1)
+# cv2.circle(image, (583, 512), 3, (0, 0, 255), -1)
+
+cv2.imshow("Image", image)
+
+# cv2.setMouseCallback("Image", handle_mouse_click)
+
+cv2.waitKey(0)
+
+cv2.destroyAllWindows()
diff --git a/MaskTheFaceNew/requirements.txt b/MaskTheFaceNew/requirements.txt
new file mode 100644
index 0000000..c869f33
--- /dev/null
+++ b/MaskTheFaceNew/requirements.txt
@@ -0,0 +1,13 @@
+certifi==2020.4.5.1
+click==7.1.2
+dlib==19.19.0
+dotmap==1.3.14
+face-recognition==1.3.0
+face-recognition-models==0.3.0
+numpy==1.19.2
+opencv-python==4.2.0.34
+Pillow==7.1.2
+tqdm==4.46.0
+wincertstore==0.2
+imutils==0.5.3
+requests==2.24.0
diff --git a/MaskTheFaceNew/utils/__init__.py b/MaskTheFaceNew/utils/__init__.py
new file mode 100644
index 0000000..9c57dd3
--- /dev/null
+++ b/MaskTheFaceNew/utils/__init__.py
@@ -0,0 +1,3 @@
+# Author: Aqeel Anwar(ICSRL)
+# Created: 7/30/2020, 7:43 AM
+# Email: aqeel.anwar@gatech.edu
\ No newline at end of file
diff --git a/MaskTheFaceNew/utils/aux_functions.py b/MaskTheFaceNew/utils/aux_functions.py
new file mode 100644
index 0000000..dbc720b
--- /dev/null
+++ b/MaskTheFaceNew/utils/aux_functions.py
@@ -0,0 +1,733 @@
+# Author: aqeelanwar
+# Created: 27 April,2020, 10:21 PM
+# Email: aqeel.anwar@gatech.edu
+
+from configparser import ConfigParser
+import cv2
+import math
+import os
+from PIL import Image, ImageDraw
+from tqdm import tqdm
+from utils.read_cfg import read_cfg
+from utils.fit_ellipse import *
+import random
+from utils.create_mask import texture_the_mask, color_the_mask
+from imutils import face_utils
+import requests
+from zipfile import ZipFile
+from tqdm import tqdm
+import bz2
+import shutil
+import csv
+
+f = open("mask.csv", "w")
+
+fieldnames = ['img', 'img_height', 'img_width', 'face_xtl', 'face_ytl', 'face_xbr', 'face_ybr', 'mask_xtl', 'mask_ytl',
+              'mask_xtm', 'mask_ytm', 'mask_xtr', 'mask_ytr', 'mask_xbr', 'mask_ybr', 'mask_xbm', 'mask_ybm', 'mask_xbl', 'mask_ybl']
+
+writer = csv.writer(f)
+writer.writerow(fieldnames)
+
+# csv_row = []
+
+
+def download_dlib_model():
+    print_orderly("Get dlib model", 60)
+    dlib_model_link = "http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2"
+    print("Downloading dlib model...")
+    with requests.get(dlib_model_link, stream=True) as r:
+        print("Zip file size: ", np.round(
+            len(r.content) / 1024 / 1024, 2), "MB")
+        destination = (
+            "dlib_models" + os.path.sep + "shape_predictor_68_face_landmarks.dat.bz2"
+        )
+        if not os.path.exists(destination.rsplit(os.path.sep, 1)[0]):
+            os.mkdir(destination.rsplit(os.path.sep, 1)[0])
+        print("Saving dlib model...")
+        with open(destination, "wb") as fd:
+            for chunk in r.iter_content(chunk_size=32678):
+                fd.write(chunk)
+    print("Extracting dlib model...")
+    with bz2.BZ2File(destination) as fr, open(
+        "dlib_models/shape_predictor_68_face_landmarks.dat", "wb"
+    ) as fw:
+        shutil.copyfileobj(fr, fw)
+    print("Saved: ", destination)
+    print_orderly("done", 60)
+
+    os.remove(destination)
+
+
+def get_line(face_landmark, image, type="eye", debug=False):
+    pil_image = Image.fromarray(image)
+    d = ImageDraw.Draw(pil_image)
+    left_eye = face_landmark["left_eye"]
+    right_eye = face_landmark["right_eye"]
+    left_eye_mid = np.mean(np.array(left_eye), axis=0)
+    right_eye_mid = np.mean(np.array(right_eye), axis=0)
+    eye_line_mid = (left_eye_mid + right_eye_mid) / 2
+
+    if type == "eye":
+        left_point = left_eye_mid
+        right_point = right_eye_mid
+        mid_point = eye_line_mid
+
+    elif type == "nose_mid":
+        nose_length = (
+            face_landmark["nose_bridge"][-1][1] -
+            face_landmark["nose_bridge"][0][1]
+        )
+        left_point = [left_eye_mid[0], left_eye_mid[1] + nose_length / 2]
+        right_point = [right_eye_mid[0], right_eye_mid[1] + nose_length / 2]
+        # mid_point = (
+        #     face_landmark["nose_bridge"][-1][1] + face_landmark["nose_bridge"][0][1]
+        # ) / 2
+
+        mid_pointY = (
+            face_landmark["nose_bridge"][-1][1] +
+            face_landmark["nose_bridge"][0][1]
+        ) / 2
+        mid_pointX = (
+            face_landmark["nose_bridge"][-1][0] +
+            face_landmark["nose_bridge"][0][0]
+        ) / 2
+        mid_point = (mid_pointX, mid_pointY)
+
+    elif type == "nose_tip":
+        nose_length = (
+            face_landmark["nose_bridge"][-1][1] -
+            face_landmark["nose_bridge"][0][1]
+        )
+        left_point = [left_eye_mid[0], left_eye_mid[1] + nose_length]
+        right_point = [right_eye_mid[0], right_eye_mid[1] + nose_length]
+        mid_point = (
+            face_landmark["nose_bridge"][-1][1] +
+            face_landmark["nose_bridge"][0][1]
+        ) / 2
+
+    elif type == "bottom_lip":
+        bottom_lip = face_landmark["bottom_lip"]
+        bottom_lip_mid = np.max(np.array(bottom_lip), axis=0)
+        shiftY = bottom_lip_mid[1] - eye_line_mid[1]
+        left_point = [left_eye_mid[0], left_eye_mid[1] + shiftY]
+        right_point = [right_eye_mid[0], right_eye_mid[1] + shiftY]
+        mid_point = bottom_lip_mid
+
+    elif type == "perp_line":
+        bottom_lip = face_landmark["bottom_lip"]
+        bottom_lip_mid = np.mean(np.array(bottom_lip), axis=0)
+
+        left_point = eye_line_mid
+        left_point = face_landmark["nose_bridge"][0]
+        right_point = bottom_lip_mid
+
+        mid_point = bottom_lip_mid
+
+    elif type == "nose_long":
+        nose_bridge = face_landmark["nose_bridge"]
+        left_point = [nose_bridge[0][0], nose_bridge[0][1]]
+        right_point = [nose_bridge[-1][0], nose_bridge[-1][1]]
+
+        mid_point = left_point
+
+    # d.line(eye_mid, width=5, fill='red')
+    y = [left_point[1], right_point[1]]
+    x = [left_point[0], right_point[0]]
+    # cv2.imshow('h', image)
+    # cv2.waitKey(0)
+    eye_line = fit_line(x, y, image)
+    d.line(eye_line, width=5, fill="blue")
+
+    # Perpendicular Line
+    # (midX, midY) and (midX - y2 + y1, midY + x2 - x1)
+    y = [
+        (left_point[1] + right_point[1]) / 2,
+        (left_point[1] + right_point[1]) / 2 + right_point[0] - left_point[0],
+    ]
+    x = [
+        (left_point[0] + right_point[0]) / 2,
+        (left_point[0] + right_point[0]) / 2 - right_point[1] + left_point[1],
+    ]
+    perp_line = fit_line(x, y, image)
+    if debug:
+        d.line(perp_line, width=5, fill="red")
+        pil_image.show()
+    return eye_line, perp_line, left_point, right_point, mid_point
+
+
+def get_points_on_chin(line, face_landmark, chin_type="chin"):
+    chin = face_landmark[chin_type]
+    points_on_chin = []
+    for i in range(len(chin) - 1):
+        chin_first_point = [chin[i][0], chin[i][1]]
+        chin_second_point = [chin[i + 1][0], chin[i + 1][1]]
+
+        flag, x, y = line_intersection(
+            line, (chin_first_point, chin_second_point))
+        if flag:
+            points_on_chin.append((x, y))
+
+    return points_on_chin
+
+
+def plot_lines(face_line, image, debug=False):
+    pil_image = Image.fromarray(image)
+    if debug:
+        d = ImageDraw.Draw(pil_image)
+        d.line(face_line, width=4, fill="white")
+        pil_image.show()
+
+
+def line_intersection(line1, line2):
+    # mid = int(len(line1) / 2)
+    start = 0
+    end = -1
+    line1 = ([line1[start][0], line1[start][1]],
+             [line1[end][0], line1[end][1]])
+
+    xdiff = (line1[0][0] - line1[1][0], line2[0][0] - line2[1][0])
+    ydiff = (line1[0][1] - line1[1][1], line2[0][1] - line2[1][1])
+    x = []
+    y = []
+    flag = False
+
+    def det(a, b):
+        return a[0] * b[1] - a[1] * b[0]
+
+    div = det(xdiff, ydiff)
+    if div == 0:
+        return flag, x, y
+
+    d = (det(*line1), det(*line2))
+    x = det(d, xdiff) / div
+    y = det(d, ydiff) / div
+
+    segment_minX = min(line2[0][0], line2[1][0])
+    segment_maxX = max(line2[0][0], line2[1][0])
+
+    segment_minY = min(line2[0][1], line2[1][1])
+    segment_maxY = max(line2[0][1], line2[1][1])
+
+    if (
+        segment_maxX + 1 >= x >= segment_minX - 1
+        and segment_maxY + 1 >= y >= segment_minY - 1
+    ):
+        flag = True
+
+    return flag, x, y
+
+
+def fit_line(x, y, image):
+    if x[0] == x[1]:
+        x[0] += 0.1
+    coefficients = np.polyfit(x, y, 1)
+    polynomial = np.poly1d(coefficients)
+    x_axis = np.linspace(0, image.shape[1], 50)
+    y_axis = polynomial(x_axis)
+    eye_line = []
+    for i in range(len(x_axis)):
+        eye_line.append((x_axis[i], y_axis[i]))
+
+    return eye_line
+
+
+def get_six_points(face_landmark, image):
+    _, perp_line1, _, _, m = get_line(face_landmark, image, type="nose_mid")
+    face_b = m
+
+    perp_line, _, _, _, _ = get_line(face_landmark, image, type="perp_line")
+    points1 = get_points_on_chin(perp_line1, face_landmark)
+    points = get_points_on_chin(perp_line, face_landmark)
+    if not points1:
+        face_e = tuple(np.asarray(points[0]))
+    elif not points:
+        face_e = tuple(np.asarray(points1[0]))
+    else:
+        face_e = tuple((np.asarray(points[0]) + np.asarray(points1[0])) / 2)
+    # face_e = points1[0]
+    nose_mid_line, _, _, _, _ = get_line(
+        face_landmark, image, type="nose_long")
+
+    angle = get_angle(perp_line, nose_mid_line)
+    # print("angle: ", angle)
+    nose_mid_line, _, _, _, _ = get_line(face_landmark, image, type="nose_tip")
+    points = get_points_on_chin(nose_mid_line, face_landmark)
+    if len(points) < 2:
+        face_landmark = get_face_ellipse(face_landmark)
+        # print("extrapolating chin")
+        points = get_points_on_chin(
+            nose_mid_line, face_landmark, chin_type="chin_extrapolated"
+        )
+        if len(points) < 2:
+            points = []
+            points.append(face_landmark["chin"][0])
+            points.append(face_landmark["chin"][-1])
+    face_a = points[0]
+    face_c = points[-1]
+    # cv2.imshow('j', image)
+    # cv2.waitKey(0)
+    nose_mid_line, _, _, _, _ = get_line(
+        face_landmark, image, type="bottom_lip")
+    points = get_points_on_chin(nose_mid_line, face_landmark)
+    face_d = points[0]
+    face_f = points[-1]
+
+    six_points = np.float32([face_a, face_b, face_c, face_f, face_e, face_d])
+
+    return six_points, angle
+
+
+def get_angle(line1, line2):
+    delta_y = line1[-1][1] - line1[0][1]
+    delta_x = line1[-1][0] - line1[0][0]
+    perp_angle = math.degrees(math.atan2(delta_y, delta_x))
+    if delta_x < 0:
+        perp_angle = perp_angle + 180
+    if perp_angle < 0:
+        perp_angle += 360
+    if perp_angle > 180:
+        perp_angle -= 180
+
+    # print("perp", perp_angle)
+    delta_y = line2[-1][1] - line2[0][1]
+    delta_x = line2[-1][0] - line2[0][0]
+    nose_angle = math.degrees(math.atan2(delta_y, delta_x))
+
+    if delta_x < 0:
+        nose_angle = nose_angle + 180
+    if nose_angle < 0:
+        nose_angle += 360
+    if nose_angle > 180:
+        nose_angle -= 180
+    # print("nose", nose_angle)
+
+    angle = nose_angle - perp_angle
+    return angle
+
+
+def mask_face(image, face_location, six_points, angle, args, type="surgical"):
+    debug = False
+
+    # Find the face angle
+    threshold = 13
+    if angle < -threshold:
+        type += "_right"
+    elif angle > threshold:
+        type += "_left"
+
+    face_height = face_location[2] - face_location[0]
+    face_width = face_location[1] - face_location[3]
+    # image = image_raw[
+    #              face_location[0]-int(face_width/2): face_location[2]+int(face_width/2),
+    #              face_location[3]-int(face_height/2): face_location[1]+int(face_height/2),
+    #              :,
+    #              ]
+    # cv2.imshow('win', image)
+    # cv2.waitKey(0)
+    # Read appropriate mask image
+    w = image.shape[0]
+    h = image.shape[1]
+    if not "empty" in type and not "inpaint" in type:
+        cfg = read_cfg(config_filename="masks/masks.cfg",
+                       mask_type=type, verbose=False)
+    else:
+        if "left" in type:
+            str = "surgical_blue_left"
+        elif "right" in type:
+            str = "surgical_blue_right"
+        else:
+            str = "surgical_blue"
+        cfg = read_cfg(config_filename="masks/masks.cfg",
+                       mask_type=str, verbose=False)
+    img = cv2.imread(cfg.template, cv2.IMREAD_UNCHANGED)
+
+    # Process the mask if necessary
+    if args.pattern:
+        # Apply pattern to mask
+        img = texture_the_mask(img, args.pattern, args.pattern_weight)
+
+    if args.color:
+        # Apply color to mask
+        img = color_the_mask(img, args.color, args.color_weight)
+
+    mask_line = np.float32(
+        [cfg.mask_a, cfg.mask_b, cfg.mask_c, cfg.mask_f, cfg.mask_e, cfg.mask_d]
+    )
+    # Warp the mask
+    M, mask = cv2.findHomography(mask_line, six_points)
+    dst_mask = cv2.warpPerspective(img, M, (h, w))
+    dst_mask_points = cv2.perspectiveTransform(mask_line.reshape(-1, 1, 2), M)
+    # REE
+    # print(dst_mask_points)
+    for point in dst_mask_points:
+        csv_row.append(int(point[0][0]))
+        csv_row.append(int(point[0][1]))
+    writer.writerow(csv_row)
+    # fieldnames = ['img', 'img_height', 'img_width', 'face_xtl', 'face_ytl', 'face_xbr', 'face_ybr', 'mask_xtl', 'mask_ytl',
+    #       'mask_xtm', 'mask_ytm', 'mask_xtr', 'mask_ytr', 'mask_xbr', 'mask_ybr', 'mask_xbm', 'mask_ybm', 'mask_xbl', 'mask_ybl']
+
+    mask = dst_mask[:, :, 3]
+    face_height = face_location[2] - face_location[0]
+    face_width = face_location[1] - face_location[3]
+    image_face = image[
+        face_location[0] + int(face_height / 2): face_location[2],
+        face_location[3]: face_location[1],
+        :,
+    ]
+
+    image_face = image
+
+    # Adjust Brightness
+    mask_brightness = get_avg_brightness(img)
+    img_brightness = get_avg_brightness(image_face)
+    delta_b = 1 + (img_brightness - mask_brightness) / 255
+    dst_mask = change_brightness(dst_mask, delta_b)
+
+    # Adjust Saturation
+    mask_saturation = get_avg_saturation(img)
+    img_saturation = get_avg_saturation(image_face)
+    delta_s = 1 - (img_saturation - mask_saturation) / 255
+    dst_mask = change_saturation(dst_mask, delta_s)
+
+    # Apply mask
+    mask_inv = cv2.bitwise_not(mask)
+    img_bg = cv2.bitwise_and(image, image, mask=mask_inv)
+    img_fg = cv2.bitwise_and(dst_mask, dst_mask, mask=mask)
+    out_img = cv2.add(img_bg, img_fg[:, :, 0:3])
+    if "empty" in type or "inpaint" in type:
+        out_img = img_bg
+    # Plot key points
+
+    if "inpaint" in type:
+        out_img = cv2.inpaint(out_img, mask, 3, cv2.INPAINT_TELEA)
+        # dst_NS = cv2.inpaint(img, mask, 3, cv2.INPAINT_NS)
+
+    if debug:
+        for i in six_points:
+            cv2.circle(out_img, (i[0], i[1]), radius=4,
+                       color=(0, 0, 255), thickness=-1)
+
+        for i in dst_mask_points:
+            cv2.circle(
+                out_img, (i[0][0], i[0][1]), radius=4, color=(0, 255, 0), thickness=-1
+            )
+
+    return out_img, mask
+
+
+def draw_landmarks(face_landmarks, image):
+    pil_image = Image.fromarray(image)
+    d = ImageDraw.Draw(pil_image)
+    for facial_feature in face_landmarks.keys():
+        d.line(face_landmarks[facial_feature], width=5, fill="white")
+    pil_image.show()
+
+
+def get_face_ellipse(face_landmark):
+    chin = face_landmark["chin"]
+    x = []
+    y = []
+    for point in chin:
+        x.append(point[0])
+        y.append(point[1])
+
+    x = np.asarray(x)
+    y = np.asarray(y)
+
+    a = fitEllipse(x, y)
+    center = ellipse_center(a)
+    phi = ellipse_angle_of_rotation(a)
+    axes = ellipse_axis_length(a)
+    a, b = axes
+
+    arc = 2.2
+    R = np.arange(0, arc * np.pi, 0.2)
+    xx = center[0] + a * np.cos(R) * np.cos(phi) - b * np.sin(R) * np.sin(phi)
+    yy = center[1] + a * np.cos(R) * np.sin(phi) + b * np.sin(R) * np.cos(phi)
+    chin_extrapolated = []
+    for i in range(len(R)):
+        chin_extrapolated.append((xx[i], yy[i]))
+    face_landmark["chin_extrapolated"] = chin_extrapolated
+    return face_landmark
+
+
+def get_avg_brightness(img):
+    img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    h, s, v = cv2.split(img_hsv)
+    return np.mean(v)
+
+
+def get_avg_saturation(img):
+    img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    h, s, v = cv2.split(img_hsv)
+    return np.mean(v)
+
+
+def change_brightness(img, value=1.0):
+    img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    h, s, v = cv2.split(img_hsv)
+    v = value * v
+    v[v > 255] = 255
+    v = np.asarray(v, dtype=np.uint8)
+    final_hsv = cv2.merge((h, s, v))
+    img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
+    return img
+
+
+def change_saturation(img, value=1.0):
+    img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    h, s, v = cv2.split(img_hsv)
+    s = value * s
+    s[s > 255] = 255
+    s = np.asarray(s, dtype=np.uint8)
+    final_hsv = cv2.merge((h, s, v))
+    img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
+    return img
+
+
+def check_path(path):
+    is_directory = False
+    is_file = False
+    is_other = False
+    if os.path.isdir(path):
+        is_directory = True
+    elif os.path.isfile(path):
+        is_file = True
+    else:
+        is_other = True
+
+    return is_directory, is_file, is_other
+
+
+def shape_to_landmarks(shape):
+    face_landmarks = {}
+    face_landmarks["left_eyebrow"] = [
+        tuple(shape[17]),
+        tuple(shape[18]),
+        tuple(shape[19]),
+        tuple(shape[20]),
+        tuple(shape[21]),
+    ]
+    face_landmarks["right_eyebrow"] = [
+        tuple(shape[22]),
+        tuple(shape[23]),
+        tuple(shape[24]),
+        tuple(shape[25]),
+        tuple(shape[26]),
+    ]
+    face_landmarks["nose_bridge"] = [
+        tuple(shape[27]),
+        tuple(shape[28]),
+        tuple(shape[29]),
+        tuple(shape[30]),
+    ]
+    face_landmarks["nose_tip"] = [
+        tuple(shape[31]),
+        tuple(shape[32]),
+        tuple(shape[33]),
+        tuple(shape[34]),
+        tuple(shape[35]),
+    ]
+    face_landmarks["left_eye"] = [
+        tuple(shape[36]),
+        tuple(shape[37]),
+        tuple(shape[38]),
+        tuple(shape[39]),
+        tuple(shape[40]),
+        tuple(shape[41]),
+    ]
+    face_landmarks["right_eye"] = [
+        tuple(shape[42]),
+        tuple(shape[43]),
+        tuple(shape[44]),
+        tuple(shape[45]),
+        tuple(shape[46]),
+        tuple(shape[47]),
+    ]
+    face_landmarks["top_lip"] = [
+        tuple(shape[48]),
+        tuple(shape[49]),
+        tuple(shape[50]),
+        tuple(shape[51]),
+        tuple(shape[52]),
+        tuple(shape[53]),
+        tuple(shape[54]),
+        tuple(shape[60]),
+        tuple(shape[61]),
+        tuple(shape[62]),
+        tuple(shape[63]),
+        tuple(shape[64]),
+    ]
+
+    face_landmarks["bottom_lip"] = [
+        tuple(shape[54]),
+        tuple(shape[55]),
+        tuple(shape[56]),
+        tuple(shape[57]),
+        tuple(shape[58]),
+        tuple(shape[59]),
+        tuple(shape[48]),
+        tuple(shape[64]),
+        tuple(shape[65]),
+        tuple(shape[66]),
+        tuple(shape[67]),
+        tuple(shape[60]),
+    ]
+
+    face_landmarks["chin"] = [
+        tuple(shape[0]),
+        tuple(shape[1]),
+        tuple(shape[2]),
+        tuple(shape[3]),
+        tuple(shape[4]),
+        tuple(shape[5]),
+        tuple(shape[6]),
+        tuple(shape[7]),
+        tuple(shape[8]),
+        tuple(shape[9]),
+        tuple(shape[10]),
+        tuple(shape[11]),
+        tuple(shape[12]),
+        tuple(shape[13]),
+        tuple(shape[14]),
+        tuple(shape[15]),
+        tuple(shape[16]),
+    ]
+    return face_landmarks
+
+
+def rect_to_bb(rect):
+    x1 = rect.left()
+    x2 = rect.right()
+    y1 = rect.top()
+    y2 = rect.bottom()
+    return (x1, x2, y2, x1)
+
+
+def mask_image(f, image_path, args):
+    # Read the image
+    image = cv2.imread(image_path)
+    original_image = image.copy()
+    # gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    gray = image
+    face_locations = args.detector(gray, 1)
+    # print(face_locations)
+    mask_type = args.mask_type
+    verbose = args.verbose
+    if args.code:
+        ind = random.randint(0, len(args.code_count) - 1)
+        mask_dict = args.mask_dict_of_dict[ind]
+        mask_type = mask_dict["type"]
+        args.color = mask_dict["color"]
+        args.pattern = mask_dict["texture"]
+        args.code_count[ind] += 1
+
+    elif mask_type == "random":
+        available_mask_types = get_available_mask_types()
+        mask_type = random.choice(available_mask_types)
+
+    if verbose:
+        tqdm.write("Faces found: {:2d}".format(len(face_locations)))
+    # Process each face in the image
+    masked_images = []
+    mask_binary_array = []
+    mask = []
+    for (i, face_location) in enumerate(face_locations):
+        # REE
+        global csv_row
+        csv_row = [f]
+        height, width, channels = image.shape
+        csv_row.append(height)
+        csv_row.append(width)
+        csv_row.append(face_location.left())
+        csv_row.append(face_location.top())
+        csv_row.append(face_location.right())
+        csv_row.append(face_location.bottom())
+
+        # fieldnames = ['img', 'img_height', 'img_width', 'face_xtl', 'face_ytl', 'face_xbr', 'face_ybr', 'mask_xtl', 'mask_ytl',
+        #               'mask_xtm', 'mask_ytm', 'mask_xtr', 'mask_ytr', 'mask_xbr', 'mask_ybr', 'mask_xbm', 'mask_ybm', 'mask_xbl', 'mask_ybl']
+        shape = args.predictor(gray, face_location)
+        shape = face_utils.shape_to_np(shape)
+        face_landmarks = shape_to_landmarks(shape)
+        face_location = rect_to_bb(face_location)
+        # draw_landmarks(face_landmarks, image)
+        six_points_on_face, angle = get_six_points(face_landmarks, image)
+        mask = []
+        if mask_type != "all":
+            if len(masked_images) > 0:
+                image = masked_images.pop(0)
+            image, mask_binary = mask_face(
+                image, face_location, six_points_on_face, angle, args, type=mask_type
+            )
+
+            # compress to face tight
+            face_height = face_location[2] - face_location[0]
+            face_width = face_location[1] - face_location[3]
+            masked_images.append(image)
+            mask_binary_array.append(mask_binary)
+            mask.append(mask_type)
+        else:
+            available_mask_types = get_available_mask_types()
+            for m in range(len(available_mask_types)):
+                if len(masked_images) == len(available_mask_types):
+                    image = masked_images.pop(m)
+                img, mask_binary = mask_face(
+                    image,
+                    face_location,
+                    six_points_on_face,
+                    angle,
+                    args,
+                    type=available_mask_types[m],
+                )
+                masked_images.insert(m, img)
+                mask_binary_array.insert(m, mask_binary)
+            mask = available_mask_types
+            cc = 1
+
+    return masked_images, mask, mask_binary_array, original_image
+
+
+def is_image(path):
+    try:
+        extensions = path[-4:]
+        image_extensions = ["png", "PNG", "jpg", "JPG"]
+
+        if extensions[1:] in image_extensions:
+            return True
+        else:
+            print("Please input image file. png / jpg")
+            return False
+    except:
+        return False
+
+
+def get_available_mask_types(config_filename="masks/masks.cfg"):
+    parser = ConfigParser()
+    parser.optionxform = str
+    parser.read(config_filename)
+    available_mask_types = parser.sections()
+    available_mask_types = [
+        string for string in available_mask_types if "left" not in string
+    ]
+    available_mask_types = [
+        string for string in available_mask_types if "right" not in string
+    ]
+
+    return available_mask_types
+
+
+def print_orderly(str, n):
+    # print("")
+    hyphens = "-" * int((n - len(str)) / 2)
+    str_p = hyphens + " " + str + " " + hyphens
+    hyphens_bar = "-" * len(str_p)
+    print(hyphens_bar)
+    print(str_p)
+    print(hyphens_bar)
+
+
+def display_MaskTheFace():
+    with open("utils/display.txt", "r") as file:
+        for line in file:
+            cc = 1
+            print(line, end="")
diff --git a/MaskTheFaceNew/utils/create_mask.py b/MaskTheFaceNew/utils/create_mask.py
new file mode 100644
index 0000000..8a3db91
--- /dev/null
+++ b/MaskTheFaceNew/utils/create_mask.py
@@ -0,0 +1,118 @@
+# Author: aqeelanwar
+# Created: 6 July,2020, 12:14 AM
+# Email: aqeel.anwar@gatech.edu
+
+from PIL import ImageColor
+import cv2
+import numpy as np
+
+COLOR = [
+    "#fc1c1a",
+    "#177ABC",
+    "#94B6D2",
+    "#A5AB81",
+    "#DD8047",
+    "#6b425e",
+    "#e26d5a",
+    "#c92c48",
+    "#6a506d",
+    "#ffc900",
+    "#ffffff",
+    "#000000",
+    "#49ff00",
+]
+
+
+def color_the_mask(mask_image, color, intensity):
+    assert 0 <= intensity <= 1, "intensity should be between 0 and 1"
+    RGB_color = ImageColor.getcolor(color, "RGB")
+    RGB_color = (RGB_color[2], RGB_color[1], RGB_color[0])
+    orig_shape = mask_image.shape
+    bit_mask = mask_image[:, :, 3]
+    mask_image = mask_image[:, :, 0:3]
+
+    color_image = np.full(mask_image.shape, RGB_color, np.uint8)
+    mask_color = cv2.addWeighted(mask_image, 1 - intensity, color_image, intensity, 0)
+    mask_color = cv2.bitwise_and(mask_color, mask_color, mask=bit_mask)
+    colored_mask = np.zeros(orig_shape, dtype=np.uint8)
+    colored_mask[:, :, 0:3] = mask_color
+    colored_mask[:, :, 3] = bit_mask
+    return colored_mask
+
+
+def texture_the_mask(mask_image, texture_path, intensity):
+    assert 0 <= intensity <= 1, "intensity should be between 0 and 1"
+    orig_shape = mask_image.shape
+    bit_mask = mask_image[:, :, 3]
+    mask_image = mask_image[:, :, 0:3]
+    texture_image = cv2.imread(texture_path)
+    texture_image = cv2.resize(texture_image, (orig_shape[1], orig_shape[0]))
+
+    mask_texture = cv2.addWeighted(
+        mask_image, 1 - intensity, texture_image, intensity, 0
+    )
+    mask_texture = cv2.bitwise_and(mask_texture, mask_texture, mask=bit_mask)
+    textured_mask = np.zeros(orig_shape, dtype=np.uint8)
+    textured_mask[:, :, 0:3] = mask_texture
+    textured_mask[:, :, 3] = bit_mask
+
+    return textured_mask
+
+
+
+# cloth_mask = cv2.imread("masks/templates/cloth.png", cv2.IMREAD_UNCHANGED)
+# # cloth_mask = color_the_mask(cloth_mask, color=COLOR[0], intensity=0.5)
+# path = "masks/textures"
+# path, dir, files = os.walk(path).__next__()
+# first_frame = True
+# col_limit = 6
+# i = 0
+# # img_concat_row=[]
+# img_concat = []
+# # for f in files:
+# #     if "._" not in f:
+# #         print(f)
+# #         i += 1
+# #         texture_image = cv2.imread(os.path.join(path, f))
+# #         m = texture_the_mask(cloth_mask, texture_image, intensity=0.5)
+# #         if first_frame:
+# #             img_concat_row = m
+# #             first_frame = False
+# #         else:
+# #             img_concat_row = cv2.hconcat((img_concat_row, m))
+# #
+# #             if i % col_limit == 0:
+# #                 if len(img_concat) > 0:
+# #                     img_concat = cv2.vconcat((img_concat, img_concat_row))
+# #                 else:
+# #                     img_concat = img_concat_row
+# #                 first_frame = True
+#
+# ## COlor the mask
+# thresholds = np.arange(0.1,0.9,0.05)
+# for intensity in thresholds:
+#     c=COLOR[2]
+#     # intensity = 0.5
+#     if "._" not in c:
+#         print(intensity)
+#         i += 1
+#         # texture_image = cv2.imread(os.path.join(path, f))
+#         m = color_the_mask(cloth_mask, c, intensity=intensity)
+#         if first_frame:
+#             img_concat_row = m
+#             first_frame = False
+#         else:
+#             img_concat_row = cv2.hconcat((img_concat_row, m))
+#
+#             if i % col_limit == 0:
+#                 if len(img_concat) > 0:
+#                     img_concat = cv2.vconcat((img_concat, img_concat_row))
+#                 else:
+#                     img_concat = img_concat_row
+#                 first_frame = True
+#
+#
+# cv2.imshow("k", img_concat)
+# cv2.imwrite("combine_N95_left.png", img_concat)
+# cv2.waitKey(0)
+# cc = 1
diff --git a/MaskTheFaceNew/utils/display.txt b/MaskTheFaceNew/utils/display.txt
new file mode 100644
index 0000000..246fcea
--- /dev/null
+++ b/MaskTheFaceNew/utils/display.txt
@@ -0,0 +1,6 @@
+ __  __           _ _______ _          ______
+|  \/  |         | |__   __| |        |  ____|
+| \  / | __ _ ___| | _| |  | |__   ___| |__ __ _  ___ ___
+| |\/| |/ _` / __| |/ / |  | '_ \ / _ \  __/ _` |/ __/ _ \
+| |  | | (_| \__ \   <| |  | | | |  __/ | | (_| | (_|  __/
+|_|  |_|\__,_|___/_|\_\_|  |_| |_|\___|_|  \__,_|\___\___|
diff --git a/MaskTheFaceNew/utils/fetch_dataset.py b/MaskTheFaceNew/utils/fetch_dataset.py
new file mode 100644
index 0000000..1c0357c
--- /dev/null
+++ b/MaskTheFaceNew/utils/fetch_dataset.py
@@ -0,0 +1,92 @@
+# Author: Aqeel Anwar(ICSRL)
+# Created: 7/30/2020, 1:44 PM
+# Email: aqeel.anwar@gatech.edu
+
+# Code resued from https://stackoverflow.com/questions/38511444/python-download-files-from-google-drive-using-url
+# Make sure you run this from parent folder and not from utils folder i.e.
+# python utils/fetch_dataset.py
+
+import requests, os
+from zipfile import ZipFile
+import argparse
+import urllib
+
+parser = argparse.ArgumentParser(
+    description="Download dataset - Python code to download associated datasets"
+)
+parser.add_argument(
+    "--dataset",
+    type=str,
+    default="mfr2",
+    help="Name of the dataset - Details on available datasets can be found at GitHub Page",
+)
+args = parser.parse_args()
+
+
+def download_file_from_google_drive(id, destination):
+    URL = "https://docs.google.com/uc?export=download"
+
+    session = requests.Session()
+
+    response = session.get(URL, params={"id": id}, stream=True)
+    token = get_confirm_token(response)
+
+    if token:
+        params = {"id": id, "confirm": token}
+        response = session.get(URL, params=params, stream=True)
+
+    save_response_content(response, destination)
+
+
+def get_confirm_token(response):
+    for key, value in response.cookies.items():
+        if key.startswith("download_warning"):
+            return value
+
+    return None
+
+
+def save_response_content(response, destination):
+    CHUNK_SIZE = 32768
+    print(destination)
+    with open(destination, "wb") as f:
+        for chunk in response.iter_content(CHUNK_SIZE):
+            if chunk:  # filter out keep-alive new chunks
+                f.write(chunk)
+
+
+def download(t_url):
+    response = urllib.request.urlopen(t_url)
+    data = response.read()
+    txt_str = str(data)
+    lines = txt_str.split("\\n")
+    return lines
+
+
+def Convert(lst):
+    it = iter(lst)
+    res_dct = dict(zip(it, it))
+    return res_dct
+
+
+if __name__ == "__main__":
+    # Fetch the latest download_links.txt file from GitHub
+    link = "https://raw.githubusercontent.com/aqeelanwar/MaskTheFace/master/datasets/download_links.txt"
+    links_dict = Convert(
+        download(link)[0]
+        .replace(":", "\n")
+        .replace("b'", "")
+        .replace("'", "")
+        .replace(" ", "")
+        .split("\n")
+    )
+    file_id = links_dict[args.dataset]
+    destination = "datasets/_.zip"
+    print("Downloading: ", args.dataset)
+    download_file_from_google_drive(file_id, destination)
+    print("Extracting: ", args.dataset)
+    with ZipFile(destination, "r") as zipObj:
+        # Extract all the contents of zip file in current directory
+        zipObj.extractall(destination.rsplit(os.path.sep, 1)[0])
+
+    os.remove(destination)
diff --git a/MaskTheFaceNew/utils/fit_ellipse.py b/MaskTheFaceNew/utils/fit_ellipse.py
new file mode 100644
index 0000000..c17e120
--- /dev/null
+++ b/MaskTheFaceNew/utils/fit_ellipse.py
@@ -0,0 +1,64 @@
+# Author: aqeelanwar 
+# Created: 4 May,2020, 1:30 AM
+# Email: aqeel.anwar@gatech.edu
+
+import numpy as np
+from numpy.linalg import eig, inv
+
+def fitEllipse(x,y):
+    x = x[:,np.newaxis]
+    y = y[:,np.newaxis]
+    D =  np.hstack((x*x, x*y, y*y, x, y, np.ones_like(x)))
+    S = np.dot(D.T,D)
+    C = np.zeros([6,6])
+    C[0,2] = C[2,0] = 2; C[1,1] = -1
+    E, V =  eig(np.dot(inv(S), C))
+    n = np.argmax(np.abs(E))
+    a = V[:,n]
+    return a
+
+def ellipse_center(a):
+    b,c,d,f,g,a = a[1]/2, a[2], a[3]/2, a[4]/2, a[5], a[0]
+    num = b*b-a*c
+    x0=(c*d-b*f)/num
+    y0=(a*f-b*d)/num
+    return np.array([x0,y0])
+
+
+def ellipse_angle_of_rotation( a ):
+    b,c,d,f,g,a = a[1]/2, a[2], a[3]/2, a[4]/2, a[5], a[0]
+    return 0.5*np.arctan(2*b/(a-c))
+
+
+def ellipse_axis_length( a ):
+    b,c,d,f,g,a = a[1]/2, a[2], a[3]/2, a[4]/2, a[5], a[0]
+    up = 2*(a*f*f+c*d*d+g*b*b-2*b*d*f-a*c*g)
+    down1=(b*b-a*c)*( (c-a)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))
+    down2=(b*b-a*c)*( (a-c)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))
+    res1=np.sqrt(up/down1)
+    res2=np.sqrt(up/down2)
+    return np.array([res1, res2])
+
+def ellipse_angle_of_rotation2( a ):
+    b,c,d,f,g,a = a[1]/2, a[2], a[3]/2, a[4]/2, a[5], a[0]
+    if b == 0:
+        if a > c:
+            return 0
+        else:
+            return np.pi/2
+    else:
+        if a > c:
+            return np.arctan(2*b/(a-c))/2
+        else:
+            return np.pi/2 + np.arctan(2*b/(a-c))/2
+
+# a = fitEllipse(x,y)
+# center = ellipse_center(a)
+# #phi = ellipse_angle_of_rotation(a)
+# phi = ellipse_angle_of_rotation2(a)
+# axes = ellipse_axis_length(a)
+#
+# print("center = ",  center)
+# print("angle of rotation = ",  phi)
+# print("axes = ", axes)
+
diff --git a/MaskTheFaceNew/utils/point_the_mask.py b/MaskTheFaceNew/utils/point_the_mask.py
new file mode 100644
index 0000000..e232487
--- /dev/null
+++ b/MaskTheFaceNew/utils/point_the_mask.py
@@ -0,0 +1,68 @@
+# Author: aqeelanwar
+# Created: 2 May,2020, 2:49 AM
+# Email: aqeel.anwar@gatech.edu
+
+from tkinter import filedialog
+from tkinter import *
+import cv2, os
+
+mouse_pts = []
+
+
+def get_mouse_points(event, x, y, flags, param):
+    global mouseX, mouseY, mouse_pts
+    if event == cv2.EVENT_LBUTTONDOWN:
+        mouseX, mouseY = x, y
+        cv2.circle(mask_im, (x, y), 10, (0, 255, 255), 10)
+        if "mouse_pts" not in globals():
+            mouse_pts = []
+        mouse_pts.append((x, y))
+        # print("Point detected")
+        # print((x,y))
+
+
+root = Tk()
+filename = filedialog.askopenfilename(
+    initialdir="/",
+    title="Select file",
+    filetypes=(("PNG files", "*.PNG"), ("png files", "*.png"), ("All files", "*.*")),
+)
+root.destroy()
+filename_split = os.path.split(filename)
+folder = filename_split[0]
+file = filename_split[1]
+file_split = file.split(".")
+new_filename = folder + "/" + file_split[0] + "_marked." + file_split[-1]
+mask_im = cv2.imread(filename)
+cv2.namedWindow("Mask")
+cv2.setMouseCallback("Mask", get_mouse_points)
+
+while True:
+    cv2.imshow("Mask", mask_im)
+    cv2.waitKey(1)
+    if len(mouse_pts) == 6:
+        cv2.destroyWindow("Mask")
+        break
+    first_frame_display = False
+points = mouse_pts
+print(points)
+print("----------------------------------------------------------------")
+print("Copy the following code and paste it in masks.cfg")
+print("----------------------------------------------------------------")
+name_points = ["a", "b", "c", "d", "e", "f"]
+
+mask_title = "[" + file_split[0] + "]"
+print(mask_title)
+print("template: ", filename)
+for i in range(len(mouse_pts)):
+    name = (
+        "mask_"
+        + name_points[i]
+        + ": "
+        + str(mouse_pts[i][0])
+        + ","
+        + str(mouse_pts[i][1])
+    )
+    print(name)
+
+cv2.imwrite(new_filename, mask_im)
diff --git a/MaskTheFaceNew/utils/read_cfg.py b/MaskTheFaceNew/utils/read_cfg.py
new file mode 100644
index 0000000..780f289
--- /dev/null
+++ b/MaskTheFaceNew/utils/read_cfg.py
@@ -0,0 +1,50 @@
+# Author: Aqeel Anwar(ICSRL)
+# Created: 9/20/2019, 12:43 PM
+# Email: aqeel.anwar@gatech.edu
+
+from configparser import ConfigParser
+from dotmap import DotMap
+
+
+def ConvertIfStringIsInt(input_string):
+    try:
+        float(input_string)
+
+        try:
+            if int(input_string) == float(input_string):
+                return int(input_string)
+            else:
+                return float(input_string)
+        except ValueError:
+            return float(input_string)
+
+    except ValueError:
+        return input_string
+
+
+def read_cfg(config_filename="masks/masks.cfg", mask_type="surgical", verbose=False):
+    parser = ConfigParser()
+    parser.optionxform = str
+    parser.read(config_filename)
+    cfg = DotMap()
+    section_name = mask_type
+
+    if verbose:
+        hyphens = "-" * int((80 - len(config_filename)) / 2)
+        print(hyphens + " " + config_filename + " " + hyphens)
+
+    # for section_name in parser.sections():
+
+    if verbose:
+        print("[" + section_name + "]")
+    for name, value in parser.items(section_name):
+        value = ConvertIfStringIsInt(value)
+        if name != "template":
+            cfg[name] = tuple(int(s) for s in value.split(","))
+        else:
+            cfg[name] = value
+        spaces = " " * (30 - len(name))
+        if verbose:
+            print(name + ":" + spaces + str(cfg[name]))
+
+    return cfg
diff --git a/creator/.gitignore b/creator/.gitignore
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+++ b/creator/.gitignore
@@ -0,0 +1 @@
+data/images/rs*
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+06ddc2679447f14c.jpg,1024,819,58,280,724,946,163,639,414,549,718,576,730,758,443,889,193,801
+06de27a81c7ccb7b.jpg,681,1024,612,239,741,368,596,327,689,308,714,313,697,365,684,379,624,366
+06de27a81c7ccb7b.jpg,681,1024,625,625,677,677,636,654,644,651,673,654,668,668,648,678,642,673
+06de3d4adc4a9524.jpg,1024,768,291,217,513,440,333,317,369,317,546,365,504,433,368,429,346,394
+06deff708ca83c7c.jpg,683,1024,382,168,511,297,390,238,442,218,516,230,513,280,446,301,398,276
+06df0dfcff36d23a.jpg,1024,686,384,265,474,355,392,315,425,302,476,309,472,342,428,354,399,338
+06df0dfcff36d23a.jpg,1024,686,255,236,344,325,261,278,301,276,334,294,327,319,291,330,260,309
+06e039d4d15e8a10.jpg,1024,768,205,134,526,455,224,323,391,276,499,318,467,387,379,430,278,414
+06e10eed043f5145.jpg,683,1024,617,187,724,295,619,241,668,224,718,237,716,273,671,296,627,278
+06e1c84b62809448.jpg,683,1024,390,118,613,341,391,144,526,202,567,229,514,291,458,307,360,187
+06e4cdbdd0e41260.jpg,683,1024,448,222,634,407,422,323,559,290,607,289,595,377,566,414,437,378
+06e4cdbdd0e41260.jpg,683,1024,425,6,461,42,423,26,443,21,457,26,455,37,442,44,427,40
+06e504a45afa4aae.jpg,683,1024,520,310,564,353,525,334,544,324,566,328,566,345,547,357,529,350
+06e504a45afa4aae.jpg,683,1024,463,310,506,353,456,337,486,332,498,332,494,350,487,356,459,347
+06e555e52592e8b7.jpg,768,1024,585,262,660,337,594,307,621,292,659,306,647,334,623,341,603,331
+06e6661d745e7434.jpg,768,1024,139,247,247,355,152,316,194,293,252,296,252,338,201,366,160,349
+06e6661d745e7434.jpg,768,1024,712,225,841,354,728,297,771,272,836,275,838,322,786,351,741,333
+06e6661d745e7434.jpg,768,1024,402,235,509,343,398,309,463,277,494,266,497,312,477,339,401,330
+06e675da0e94451f.jpg,1024,768,294,259,402,367,281,335,364,312,391,315,369,360,359,369,312,363
+06e69302d8b35a2f.jpg,613,1024,354,96,483,225,360,179,423,149,479,175,468,224,426,251,381,235
+06e69302d8b35a2f.jpg,613,1024,712,211,841,340,724,297,782,267,851,287,836,340,785,363,742,343
+06e69302d8b35a2f.jpg,613,1024,139,77,324,263,117,177,255,175,296,184,268,253,237,278,127,230
+06e736cfd0bb6dfa.jpg,683,1024,426,128,533,235,437,206,473,171,528,155,551,192,509,245,453,241
+06e736cfd0bb6dfa.jpg,683,1024,712,110,841,239,720,181,765,149,821,140,838,175,795,222,736,218
+06e736cfd0bb6dfa.jpg,683,1024,223,140,330,247,238,206,281,186,333,191,336,228,291,255,247,240
+06e736cfd0bb6dfa.jpg,683,1024,330,187,438,295,342,243,388,222,444,226,446,263,396,291,351,275
+06e736cfd0bb6dfa.jpg,683,1024,603,106,693,196,600,150,642,138,688,146,690,176,643,197,602,179
diff --git a/creator/data/labels/test.csv b/creator/data/labels/test.csv
new file mode 100644
index 0000000..2c2d80f
--- /dev/null
+++ b/creator/data/labels/test.csv
@@ -0,0 +1,11 @@
+img,img_height,img_width,face_xtl,face_ytl,face_xbr,face_ybr,mask_xtl,mask_ytl,mask_xtm,mask_ytm,mask_xtr,mask_ytr,mask_xbr,mask_ybr,mask_xbm,mask_ybm,mask_xbl,mask_ybl
+000004f4400f6ec5.jpg,685,1024,597,182,726,311,586,249,660,244,699,263,691,289,649,309,593,295
+0000271195f2c007.jpg,768,1024,384,348,705,669,364,526,554,504,647,563,616,637,519,686,399,654
+00006c07d2b033d1.jpg,1024,1024,339,268,468,397,328,317,417,326,472,367,451,406,385,417,325,377
+00006c07d2b033d1.jpg,1024,1024,612,268,741,397,610,336,685,321,748,345,737,387,678,409,620,386
+0000b366aaf9672a.jpg,682,1024,446,184,508,246,458,214,470,211,517,212,516,233,477,251,467,240
+0000b366aaf9672a.jpg,682,1024,308,211,370,274,323,250,336,246,380,241,382,257,344,278,333,270
+0000b86e2fd18333.jpg,691,1024,712,354,841,483,733,413,759,411,850,428,841,467,758,488,741,464
+0000bee39176697a.jpg,727,1024,315,196,404,285,319,240,359,225,411,234,408,270,361,288,324,270
+0000bee39176697a.jpg,727,1024,187,271,262,345,186,310,223,297,261,304,262,331,227,351,191,338
+0000bee39176697a.jpg,727,1024,614,98,769,253,647,187,672,181,779,189,762,243,681,268,663,245
diff --git a/creator/resizer.py b/creator/resizer.py
index f293b57..af9dd54 100644
--- a/creator/resizer.py
+++ b/creator/resizer.py
@@ -2,6 +2,31 @@
 import cv2
 import os
 import numpy as np
+import random
+
+class MaskCSVRow:
+    def __init__(self, img, img_height, img_width, face_xtl, face_ytl, face_xbr, face_ybr,
+                 mask_xtl, mask_ytl, mask_xtm, mask_ytm, mask_xtr, mask_ytr, mask_xbr,
+                 mask_ybr,mask_xbm,mask_ybm,mask_xbl,mask_ybl):
+        self.img = img
+        self.img_height = img_height
+        self.img_width = img_width
+        self.face_xtl = face_xtl
+        self.face_ytl = face_ytl
+        self.face_xbr = face_xbr
+        self.face_ybr = face_ybr
+        self.mask_xtl = mask_xtl
+        self.mask_ytl = mask_ytl
+        self.mask_xtm = mask_xtm
+        self.mask_ytm = mask_ytm
+        self.mask_xtr = mask_xtr
+        self.mask_ytr = mask_ytr
+        self.mask_xbr = mask_xbr
+        self.mask_ybr = mask_ybr
+        self.mask_xbm = mask_xbm
+        self.mask_ybm = mask_ybm
+        self.mask_xbl = mask_xbl
+        self.mask_ybl = mask_ybl
 
 class CSVFile:
     def __init__(self, filename):
@@ -27,8 +52,31 @@ def get_unique_keys(self) -> List[str]:
         return ret
 
     def get_all_where_key_equals(self, key):
-        return [(line[0], line[1], int(line[2]), int(line[3]), int(line[4]), int(line[5])) for line in self.data if line[0] == key]
+        return [MaskCSVRow(
+            line[0], # img
+            int(line[1]), # img_height
+            int(line[2]), # img_width
+            int(line[3]), # face_xtl
+            int(line[4]), # face_ytl
+            int(line[5]), # face_xbr
+            int(line[6]), # face_ybr
+            int(line[7]), # mask_xtl
+            int(line[8]), # mask_ytl
+            int(line[9]), # mask_xtm
+            int(line[10]), # mask_ytm
+            int(line[11]), # mask_xtr
+            int(line[12]), # mask_ytr
+            int(line[13]), # mask_xbr
+            int(line[14]), # mask_ybr
+            int(line[15]), # mask_xbm
+            int(line[16]), # mask_ybm
+            int(line[17]), # mask_xbl
+            int(line[18]) # mask_ybl
+                ) for line in self.data if line[0] == key]
+
 
+def is_for_training():
+    return random.randrange(1, 80) > 20
 
 class Resizer:
     def __init__(self, width: int, height: int):
@@ -40,40 +88,62 @@ def __init__(self, width: int, height: int):
         # "scale" means if we have 180x180 picture, it will be scaled up to 2000x2000 (if w & h are 2000)
         # "fit" means we will just keep it in the top left corner and add black bars
         # aspect ratio is always preserved
-        self.mode = "scale" # "keep og"
-        self.background = (0, 0, 0) # BGR not RGB
+        self.mode = "scale"
+        self.background = (0, 0, 0) # allegedly BGR not RGB
         # Technically Configurable but less commonly changed
-        self.data_dir = "./data"
-        self.labels_dir = self.data_dir + "/labels"
-        self.images_dir = self.data_dir + "/images"
-        self.normal_dir = self.images_dir + "/normal_faces"  # ground truth
-        self.masks_dir = self.images_dir + "/masks" # pictures of masks that are overlayed
-        self.masked_dir = self.images_dir + "/masked_faces" # edited photos with masks
-        self.resized_truth_dir = self.images_dir + "/rs_truth" # resized ground truth
-        self.resized_masked_dir = self.images_dir + "/rs_masked" # resized pics with edited masks
-        self.resized_masks_dir = self.images_dir + "/rs_masks" # resized pics with the mask (what gets removed)
-
-        if not os.path.exists(self.resized_truth_dir):
-            os.makedirs(self.resized_truth_dir)
-        if not os.path.exists(self.resized_masked_dir):
-            os.makedirs(self.resized_masked_dir)
-        if not os.path.exists(self.resized_masks_dir):
-            os.makedirs(self.resized_masks_dir)
+        self.output_csv = "./data/images/rs_data.csv"
+        self.mask_labels = "../MaskTheFaceNew/mask.csv"
+        self.normal_dir = "../MaskTheFaceNew/normal_faces"  # ground truth
+        self.masked_dir = "../MaskTheFaceNew/normal_faces_masked" # edited photos with masks
+        self.resized_truth_training_dir =  "./data/images/rs_truth_training" # resized ground truth
+        self.resized_truth_test_dir =      "./data/images/rs_truth_test" # resized ground truth
+        self.resized_masks_training_dir =  "./data/images/rs_masks_training" # resized pics with the mask (what gets removed)
+        self.resized_masks_test_dir =      "./data/images/rs_masks_test" # resized pics with the mask (what gets removed)
+        self.resized_masked_training_dir = "./data/images/rs_masked_training" # resized pics with edited masks
+        self.resized_masked_test_dir =     "./data/images/rs_masked_test" # resized pics with edited masks
+
+        for dir in [self.resized_truth_training_dir, self.resized_truth_test_dir, self.resized_masks_training_dir,
+                    self.resized_masks_test_dir, self.resized_masked_training_dir, self.resized_masked_test_dir]:
+            if not os.path.exists(dir):
+                os.makedirs(dir)
+
 
     def do_all(self):
-        labels_masked_faces = CSVFile(self.labels_dir + '/masked_faces.csv')
+        labels_masked_faces = CSVFile(self.mask_labels)
         labels_masked_faces.open()
         unique_keys = labels_masked_faces.get_unique_keys()
+
+        csv_output = open(self.output_csv, 'w')
+        csv_output.write("image_id,mask_num,xmin,ymin,xmax,ymax\n")
+
         for u_key in unique_keys:
+            # this image is first when alphanumerically sorted in windows
+            #if u_key != "00a0d634ad200ced.jpg":
+            #    continue
             print(u_key)
+            image_id = u_key.split('.')[0]
+            # What we read
             normal_face_image_path = self.normal_dir + "/" + u_key
-            masked_face_image_path = self.masked_dir + "/" + u_key
-            resized_truth_path = self.resized_truth_dir + "/" + u_key
-            resized_mask_channel_path = self.resized_masks_dir + "/" + u_key
-            # IMREAD_UNCHANGED: preserve alpha channel
+            masked_face_image_path = self.masked_dir + "/" + image_id + '_surgical.jpg'
+            # What we'll write
+            resized_truth_training_path = self.resized_truth_training_dir + "/" + u_key
+            resized_truth_test_path = self.resized_truth_test_dir + "/" + u_key
+            resized_masked_training_path = self.resized_masked_training_dir + "/" + image_id + '_surgical.jpg'
+            resized_masked_test_path = self.resized_masked_test_dir + "/" + image_id + '_surgical.jpg'
+
+            def gen_mask_channel_path(is_training, num):
+                """
+                num: some images have multiple masks, so there is 1 mask channel image per mask per image
+                """
+                if is_training:
+                    return f"{self.resized_masks_training_dir}/{image_id}_{num}.jpg"
+                else:
+                    return f"{self.resized_masks_test_dir}/{image_id}_{num}.jpg"
+            # Load pictures into memory
             normal_face_image = cv2.imread(normal_face_image_path, cv2.IMREAD_COLOR)
             masked_face_image = cv2.imread(masked_face_image_path, cv2.IMREAD_COLOR)
             if self.mode == "scale":
+                is_training_image = is_for_training()
                 dimensions = normal_face_image.shape
                 og_height = dimensions[1]
                 og_width = dimensions[0]
@@ -87,24 +157,110 @@ def do_all(self):
                 scaling_factor = self.height / og_height if image_orientation == "vertical" else self.width / og_width
                 scaled_height = int(og_height * scaling_factor)
                 scaled_width = int(og_width * scaling_factor)
+                # resize image
                 im_resized_truth = cv2.resize(normal_face_image, (scaled_height, scaled_width), interpolation=cv2.INTER_AREA)
+                # create the background
                 background = np.zeros((self.width, self.height, 3), np.uint8)
                 background[:] = self.background
+                # paste image onto background (black bars for aspect ratio)
                 y_offset = 0
                 x_offset= 0
                 background[y_offset:y_offset+im_resized_truth.shape[0], x_offset:x_offset+im_resized_truth.shape[1]] = im_resized_truth
                 # output_truth = cv2.addWeighted(background, 1.0, im_resized_truth, 1.0, 0)
-                cv2.imwrite(resized_truth_path, background)
+                cv2.imwrite(resized_truth_training_path if is_training_image else resized_truth_test_path, background)
+
+                # create mask channel image
+                mask_bounding_boxes = labels_masked_faces.get_all_where_key_equals(u_key)
+                mask_num = 1
+                for mask in mask_bounding_boxes:
+                    new_mask_channel = np.zeros((og_width, og_height, 3), np.uint8)
+                    new_mask_channel[:] = (0, 0, 0)
+                    minx = min(mask.mask_xtl, mask.mask_xbl, mask.mask_xtm, mask.mask_xbm)
+                    miny = min(mask.mask_ytl, mask.mask_ytr, mask.mask_ytm)
+                    maxx = max(mask.mask_xbr, mask.mask_xtr)
+                    maxy = max(mask.mask_ybl, mask.mask_ybm, mask.mask_ybr)
+                    # opencv2's top-down logic
+                    cv2.rectangle(new_mask_channel,
+                                  (minx, miny),
+                                  (maxx, maxy),
+                                  (255, 255, 255), -1)
+                    # resize image
+                    new_mask_channel = cv2.resize(new_mask_channel, (scaled_height, scaled_width), interpolation=cv2.INTER_AREA)
+                    # create the background
+                    background = np.zeros((self.width, self.height, 3), np.uint8)
+                    background[:] = self.background
+                    # paste image onto background (black bars for aspect ratio)
+                    # yes we're pasting a mostly black image onto black
+                    y_offset = 0
+                    x_offset= 0
+                    background[y_offset:y_offset+new_mask_channel.shape[0], x_offset:x_offset+new_mask_channel.shape[1]] = new_mask_channel
+                    path = gen_mask_channel_path(is_training_image, mask_num)
+                    cv2.imwrite(path, background)
+                    csv_output.write(f"{image_id},{mask_num},{int(minx * scaling_factor)},{int(miny * scaling_factor)},{int(maxx * scaling_factor)},{int(maxy * scaling_factor)}\n")
+                    mask_num += 1
+
+                # resize image
+                masked_face_image = cv2.resize(masked_face_image, (scaled_height, scaled_width), interpolation=cv2.INTER_AREA)
+                # create the background
+                background = np.zeros((self.width, self.height, 3), np.uint8)
+                background[:] = self.background
+                # paste image onto background (black bars for aspect ratio)
+                y_offset = 0
+                x_offset= 0
+                background[y_offset:y_offset+masked_face_image.shape[0], x_offset:x_offset+masked_face_image.shape[1]] = masked_face_image
+                cv2.imwrite(resized_masked_training_path if is_training_image else resized_masked_test_path, background)
+
+        csv_output.close()
+
+class ResizedDataReader():
+    def __init__(self, path='./rs_data.csv'):
+        self.path = path
+        self.lines = []
+        self.headers = []
+        self.data = []
+
+    def read_all(self) -> None:
+        fp = open(self.path, 'r')
+        self.lines = fp.readlines()
+        self.lines = [line.rstrip() for line in self.lines]
+        self.headers = self.lines[0].split(',')
+        self.data = [
+            [
+                row_elems[0], # image_id
+                int(row_elems[1]), # mask_num
+                int(row_elems[2]), # xmin
+                int(row_elems[3]), # ymin
+                int(row_elems[4]), # xmax
+                int(row_elems[5]) # ymax
+            ] for row_elems in [line.split(',') for line in self.lines[1:]]
+        ]
+        fp.close()
+
+    def get_num_masks(self, image_id) -> int:
+        return len([row for row in self.data if row[0] == image_id])
+
+    def get_mask_coords(self, image_id, mask_num) -> List[int]:
+        """
+        Returns [xmin, ymin, xmax, ymax], all values will be -1 if mask info not found
+        :param image_id:
+        :param mask_num:
+        :return:
+        """
+        rows = [row for row in self.data if row[0] == image_id and row[1] == mask_num]
+        if len(rows) != 1:
+            return [-1, -1, -1, -1]
+        return rows[0][2:]
 
-                # create mask image
-                mask_channel = np.zeros((og_width, og_height, 3), np.uint8)
-                mask_channel[:] = (0, 0, 0)
-                for mask in labels_masked_faces.get_all_where_key_equals(u_key):
-                    (xmin, ymin, xmax, ymax) = (mask[2], mask[3], mask[4], mask[5])
-                    # TODO: might need to do height - y because of opencv2's top-down logic
-                    cv2.rectangle(mask_channel, (xmin, ymin), (xmax, ymax), (255, 255, 255), -1)
-                cv2.imwrite(resized_mask_channel_path, mask_channel)
+    def print_faulty(self) -> List[str]:
+        faulty = [row for row in self.data if row[2] < 0 or row[3] < 0 or row[4] > 800 or row[5] > 800]
+        for row in faulty:
+            print(f"Image ID {row[0]} has mask {row[1]} with bounds X {row[2]} - {row[4]} Y {row[3]} - {row[5]}")
 
 if __name__ == "__main__":
-    resizer = Resizer(800, 800)
-    resizer.do_all()
+    #resizer = Resizer(800, 800)
+    #resizer.do_all()
+    reader = ResizedDataReader('./data/images/rs_data.csv')
+    reader.read_all()
+    print(reader.get_num_masks('0001c3e14fe71e81'))
+    print(reader.get_mask_coords('0001c3e14fe71e81', 2))
+    reader.print_faulty()
diff --git a/creator/tfrecord.py b/creator/tfrecord.py
index 8bcf878..af2dc6c 100644
--- a/creator/tfrecord.py
+++ b/creator/tfrecord.py
@@ -15,13 +15,13 @@ def create_tf_example(filename, rows):
     filename = filename.encode('utf8')
     image_format = b'jpeg'
 
-    xmins = [i["xmin"] for i in rows]  # List of normalized left x coordinates in bounding box (1 per box)
-    xmaxs = [i["xmax"] for i in rows]  # List of normalized right x coordinates in bounding box
+    xmins = [min(i['mask_xtl'], i['mask_xbl'], i['mask_xtm'], i['mask_xbm']) for i in rows]  # List of normalized left x coordinates in bounding box (1 per box)
+    xmaxs = [max(i['mask_xbr'], i['mask_xtr']) for i in rows]  # List of normalized right x coordinates in bounding box
     # (1 per box)
-    ymins = [i["ymin"] for i in rows]  # List of normalized top y coordinates in bounding box (1 per box)
-    ymaxs = [i["ymax"] for i in rows]  # List of normalized bottom y coordinates in bounding box
+    ymins = [min(i['mask_ytl'], i['mask_ytr'], i['mask_ytm']) for i in rows]  # List of normalized top y coordinates in bounding box (1 per box)
+    ymaxs = [max(i['mask_ybl'], i['mask_ybm'], i['mask_ybr']) for i in rows]  # List of normalized bottom y coordinates in bounding box
     # (1 per box)
-    classes_text = [i["label"].encode("utf8") for i in rows]  # List of string class name of bounding box (1 per box)
+    classes_text = ['mask'.encode("utf8") for i in rows]  # List of string class name of bounding box (1 per box)
     classes = [1 for _ in rows]  # List of integer class id of bounding box (1 per box)
 
     tf_example = tf.train.Example(features=tf.train.Features(feature={
@@ -44,16 +44,16 @@ def create_tf_example(filename, rows):
 def main():
     images = {}
     writer = tf.compat.v1.python_io.TFRecordWriter("data/tf/masks.tfrecord")
-    df = pd.read_csv("data/labels/masked_faces.csv")
+    df = pd.read_csv("../MaskTheFaceNew/mask.csv")
     for index, row in df.iterrows():
-        key = row["key"]
+        key = row["img"]
         try:
             images[key].append(row)
         except KeyError:
             images.update({key: [row]})
     examples = []
     for image in images.keys():
-        example = create_tf_example("data/images/masked_faces/" + key, images[image])
+        example = create_tf_example("../MaskTheFaceNew/normal_faces_masked/" + key.split('.')[0] + "_surgical.jpg", images[image])
         examples.append(example)
 
     for example in examples: