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Merge pull request csubhasundar#183 from liuHongJie1217/main
#0 Create AI_face_landmark Demo
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‎Machine Learning/HumanKeypointsDetection/HumanKeypointDetection.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "21925825",
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"metadata": {},
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"outputs": [],
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"source": [
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"import mediapipe as mp\n",
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"import cv2 \n",
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"import time\n",
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"from tqdm import tqdm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "9072f485",
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"metadata": {},
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"outputs": [],
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"source": [
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"# import solutions\n",
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"mp_pose = mp.solutions.pose\n",
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"\n",
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"# import drawing functions\n",
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"mp_drawing = mp.solutions.drawing_utils\n",
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"\n",
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"#import model\n",
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"pose = mp_pose.Pose(static_image_mode=False, # Still pictures or continuous video frames\n",
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" model_complexity=2, # Select the human pose key point detection model, 0 has poor performance but fast, 2 has good performance but slow, and 1 is in between\n",
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" smooth_landmarks=True, # whether to smooth keypoints\n",
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" min_detection_confidence=0.5, # confidence threshold\n",
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" min_tracking_confidence=0.5) # tracking threshold"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "edf77017",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n"
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]
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}
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],
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"source": [
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"# Custom description of 33 key points of the body\n",
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"def process_frame(img):\n",
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" start_time = time.time()\n",
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" \n",
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" h, w = img.shape[0], img.shape[1]\n",
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" \n",
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" img_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
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" \n",
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" results = pose.process(img_RGB)\n",
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" \n",
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" if results.pose_landmarks:\n",
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" mp_drawing.draw_landmarks(img, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)\n",
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" \n",
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" for i in range(33): # Traverse 33 keypoints\n",
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" cx = int(results.pose_landmarks.landmark[i].x * w)\n",
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" cy = int(results.pose_landmarks.landmark[i].y * h)\n",
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" cz = results.pose_landmarks.landmark[i].z\n",
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"\n",
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" redius = 5\n",
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" if i == 0: #tip of the nose\n",
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" img = cv2.circle(img, (cx,cy), redius, (0,0,255), -1)\n",
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" elif i in [11,12]: #Shoulder\n",
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" img = cv2.circle(img, (cx,cy), redius, (223,155,6), -1)\n",
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" elif i in [23,24]: #hip joint\n",
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" img = cv2.circle(img, (cx,cy), redius, (1,240,255), -1)\n",
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" elif i in [13,14]: #elbow\n",
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" img = cv2.circle(img, (cx,cy), redius, (140,47,240), -1)\n",
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" elif i in [25,26]: #knee\n",
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" img = cv2.circle(img, (cx,cy), redius, (0,0,255), -1)\n",
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" elif i in [15,16,27,28]: #wrist and ankle\n",
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" img = cv2.circle(img, (cx,cy), redius, (223,155,60), -1)\n",
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" elif i in [17,19,21]: #left hand\n",
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" img = cv2.circle(img, (cx,cy), redius, (94,218,121), -1)\n",
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" elif i in [18,20,22]: #right hand\n",
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" img = cv2.circle(img, (cx,cy), redius, (16,144,247), -1)\n",
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" elif i in [27,29,31]: #left foot\n",
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" img = cv2.circle(img, (cx,cy), redius, (29,123,243), -1)\n",
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" elif i in [28,30,32]: #right foot\n",
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" img = cv2.circle(img, (cx,cy), redius, (193,182,255), -1)\n",
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" elif i in [9,10]: #mouth\n",
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" img = cv2.circle(img, (cx,cy), redius, (205,235,255), -1)\n",
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" elif i in [1,2,3,4,5,6,7,8]: #eyes and cheeks\n",
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" img = cv2.circle(img, (cx,cy), redius, (94,218,121), -1)\n",
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" else: #Other key points\n",
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" img = cv2.circle(img, (cx,cy), redius, (0,255,0), -1)\n",
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"\n",
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"# look_img(img)\n",
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" else:\n",
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" scaler = 1\n",
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" failuer_str = 'NO Person'\n",
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" img = cv2.putText(img, failuer_str, (25 * scaler, 100 * scaler), cv2.FONT_HERSHEY_SIMPLEX, 1.25 * scaler, 255,0,0)\n",
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" \n",
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" end_time = time.time()\n",
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" FPS = 1/(end_time - start_time)\n",
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" \n",
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" scaler = 1\n",
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" img = cv2.putText(img, 'FPS '+str(int(FPS)), (25 * scaler, 50 * scaler), cv2.FONT_HERSHEY_SIMPLEX, 1.25 * scaler, 223,155,6)\n",
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" \n",
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" return img"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "8c1743d8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Video frame-by-frame processing code template\n",
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"def generate_video(input_path):\n",
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" filehead = input_path.split('/')[-1]\n",
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" output_path = \"out-\" + filehead\n",
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" \n",
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" print('Video starts processing',input_path)\n",
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" \n",
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" # Get the total number of frames in the video\n",
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" cap = cv2.VideoCapture(input_path)\n",
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" frame_count = 0\n",
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" while(cap.isOpened()):\n",
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" success, frame = cap.read()\n",
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" frame_count += 1\n",
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" if not success:\n",
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" break\n",
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" cap.release()\n",
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" print('The total number of video frames is',frame_count)\n",
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" \n",
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" # cv2.namedWindow('Crack Detection and Measurement Video Processing')\n",
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" cap = cv2.VideoCapture(input_path)\n",
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" frame_size = (cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
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"\n",
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" # fourcc = int(cap.get(cv2.CAP_PROP_FOURCC))\n",
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" # fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
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" fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n",
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" fps = cap.get(cv2.CAP_PROP_FPS)\n",
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"\n",
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" out = cv2.VideoWriter(output_path, fourcc, fps, (int(frame_size[0]), int(frame_size[1])))\n",
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" \n",
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" # The progress bar is bound to the total number of frames of the video\n",
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" with tqdm(total=frame_count-1) as pbar:\n",
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" try:\n",
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" while(cap.isOpened()):\n",
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" success, frame = cap.read()\n",
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" if not success:\n",
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" break\n",
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"\n",
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" # process frame\n",
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" # frame_path = './temp_frame.png'\n",
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" # cv2.imwrite(frame_path, frame)\n",
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" try:\n",
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" frame = process_frame(frame)\n",
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" except:\n",
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" print('error')\n",
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" pass\n",
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" \n",
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" if success == True:\n",
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" # cv2.imshow('Video Processing', frame)\n",
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" out.write(frame)\n",
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"\n",
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" # The progress bar updates one frame\n",
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" pbar.update(1)\n",
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"\n",
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" # if cv2.waitKey(1) & 0xFF == ord('q'):\n",
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" # break\n",
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" except:\n",
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" print('Interrupted')\n",
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" pass\n",
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"\n",
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" cv2.destroyAllWindows()\n",
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" out.release()\n",
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" cap.release()\n",
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" print('Video saved', output_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "d779d98d",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Video starts processing material/material.mp4\n",
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"The total number of video frames is 527\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 526/526 [00:46<00:00, 11.29it/s]"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Video saved out-material.mp4\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"generate_video(input_path=\"material/material.mp4\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"file_extension": ".py",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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},
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"vscode": {
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"interpreter": {
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Machine Learning/HumanKeypointsDetection/HumanKeypointDetection.md‎

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# machine Learning
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https://en.wikipedia.org/wiki/Machine_learning
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# mediapipe
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https://google.github.io/mediapipe/
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# development environment
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- python version: 3.7.6
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- conda version: 4.10.3
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- Install jupyter notebook with Anaconda: conda install jupyter notebook
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- Dependency library version
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- cv2: 4.5.4-dev
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- mediapipe: pip install mediapipe
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- tqdm: pip install tqdm
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# Overview
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-> This example relies on Google's open source machine learning framework MediaPipe to try to learn
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-> In the material folder, the test material (materal.mp4) and the result of running through the model (out-material.mp4) are provided. You can realize more interesting scenes by modifying the material path in the code
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-> The material folder also provides information on various points of the human body. You can modify the points of the process_frame method in the code to achieve the human detection position that you need to pay attention to
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-> The project is divided into .ipynb and .py files. It is recommended that you use jupyter notebook to open .ipynb
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-> Please modify the material path (absolute path) when using the .py file to run. MacOs system may not be able to directly read the material file in the current directory due to security and privacy restrictions, so please put the file on the desktop and modify the absolute path in the code.
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-> The editor can use jupyter notebook or Vscode

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