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training models scripts #1

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483 changes: 483 additions & 0 deletions Untitled.ipynb
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133 changes: 133 additions & 0 deletions bard_code.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'mediapipe.framework.packet_pb2'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[3], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmediapipe\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mmp\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mmediapipe\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mframework\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mformats\u001b[39;00m \u001b[39mimport\u001b[39;00m landmark_pb2\n\u001b[1;32m----> 3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mmediapipe\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mframework\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpacket_pb2\u001b[39;00m \u001b[39mimport\u001b[39;00m Packet\n\u001b[0;32m 5\u001b[0m \u001b[39m# Define list of values\u001b[39;00m\n\u001b[0;32m 6\u001b[0m values \u001b[39m=\u001b[39m [\u001b[39m0.1\u001b[39m, \u001b[39m0.2\u001b[39m, \u001b[39m0.3\u001b[39m, \u001b[39m0.4\u001b[39m, \u001b[39m0.5\u001b[39m, \u001b[39m0.6\u001b[39m]\n",
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'mediapipe.framework.packet_pb2'"
]
}
],
"source": [
"import mediapipe as mp\n",
"from mediapipe.framework.formats import landmark_pb2\n",
"from mediapipe.framework.packet_pb2 import Packet\n",
"\n",
"# Define list of values\n",
"values = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]\n",
"\n",
"# Create NormalizedLandmarkList proto object\n",
"landmark_list = landmark_pb2.NormalizedLandmarkList()\n",
"for i in range(len(values)//2):\n",
" landmark = landmark_list.landmark.add()\n",
" landmark.x = values[2*i]\n",
" landmark.y = values[2*i+1]\n",
"\n",
"# Create Packet object with NormalizedLandmarkList proto\n",
"packet = Packet()\n",
"packet.Set(landmark_list.SerializeToString())\n",
"\n",
"# Create MediaPipe result object\n",
"result = mp.Results()\n",
"result.multi_hand_landmarks.append(packet)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "module 'mediapipe.python.solutions' has no attribute 'holistic_avatar'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[2], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mcv2\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[39m# Import the MediaPipe Holistic Avatar model.\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m holistic_avatar \u001b[39m=\u001b[39m mp\u001b[39m.\u001b[39;49msolutions\u001b[39m.\u001b[39;49mholistic_avatar\u001b[39m.\u001b[39mHolisticAvatar()\n\u001b[0;32m 5\u001b[0m \u001b[39m# Create a video capture object.\u001b[39;00m\n\u001b[0;32m 6\u001b[0m cap \u001b[39m=\u001b[39m cv2\u001b[39m.\u001b[39mVideoCapture(\u001b[39m0\u001b[39m)\n",
"\u001b[1;31mAttributeError\u001b[0m: module 'mediapipe.python.solutions' has no attribute 'holistic_avatar'"
]
}
],
"source": [
"import cv2\n",
"# Import the MediaPipe Holistic Avatar model.\n",
"holistic_avatar = mp.solutions.holistic_avatar.HolisticAvatar()\n",
"\n",
"# Create a video capture object.\n",
"cap = cv2.VideoCapture(0)\n",
"\n",
"# Start the MediaPipe Holistic Avatar model.\n",
"holistic_avatar.start()\n",
"\n",
"# Get the pose estimates from the model.\n",
"while True:\n",
" # Get a frame from the video capture object.\n",
" ret, frame = cap.read()\n",
"\n",
" # Get the pose estimates from the MediaPipe Holistic Avatar model.\n",
" pose = holistic_avatar.process(frame)\n",
"\n",
" # Pose the avatar using the pose estimates.\n",
" holistic_avatar.pose(pose)\n",
"\n",
" # Render the avatar.\n",
" holistic_avatar.render()\n",
"\n",
" # Display the frame.\n",
" cv2.imshow(\"MediaPipe Holistic Avatar\", frame)\n",
"\n",
" # If the user presses the \"q\" key, stop the program.\n",
" if cv2.waitKey(1) & 0xFF == ord(\"q\"):\n",
" break\n",
"\n",
"# Stop the MediaPipe Holistic Avatar model.\n",
"holistic_avatar.stop()\n",
"\n",
"# Close the video capture object.\n",
"cap.release()\n",
"\n",
"# Destroy all windows.\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.7"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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