NoahTest.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOskNb7USi3GJFzDTa/MwXb"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Download the test video"],"metadata":{"id":"H-RZ-eh_xsS0"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"5M1PTMDxvW51"},"outputs":[],"source":["!pip install pytube moviepy"]},{"cell_type":"code","source":["import os\n","import pytube\n","from moviepy.editor import *\n","\n","# Function to download a YouTube video\n","def download_video(url, output_path):\n","    yt = pytube.YouTube(url)\n","    stream = yt.streams.get_highest_resolution()\n","    stream.download(output_path=output_path)\n"],"metadata":{"id":"FregyfPmwD0Q"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# YouTube video URL\n","youtube_url = 'https://youtube.com/shorts/G3t_SbT4_k8?si=F8obLNelFhvbQPZL'\n","\n","# Output folder\n","output_folder = 'downloads'\n","\n","# Create the output folder if it doesn't exist\n","if not os.path.exists(output_folder):\n","    os.makedirs(output_folder)\n","\n","# Download the YouTube video to the output folder\n","download_video(youtube_url, output_folder)\n","\n","# Find the downloaded video file in the output folder\n","video_files = [f for f in os.listdir(output_folder) if f.endswith('.mp4')]\n","\n","if not video_files:\n","    print(\"No video files found in the output folder.\")\n","else:\n","    video_file = os.path.join(output_folder, video_files[0])\n","\n","    # Convert the video to MP3\n","\n","    print(\"Downloaded and converted to MP3 successfully!\")\n","\n","    # Clean up - delete the video file"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V6cxOEMUwJNj","executionInfo":{"status":"ok","timestamp":1706305818207,"user_tz":-60,"elapsed":4638,"user":{"displayName":"tim tensor","userId":"05709850039753462563"}},"outputId":"1deea838-cc68-4d74-8530-9ca3560f48e0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloaded and converted to MP3 successfully!\n"]}]},{"cell_type":"markdown","source":["# Extract part of the video"],"metadata":{"id":"Y16Dcqt6x3uR"}},{"cell_type":"code","source":["videos_folder = \"ultralytics\"\n","output_folder = \"ultralytics-out\"\n","!mkdir videos_folder output_folder\n","!mv /content/downloads/popup.mp4 videos_folder/"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Op0I752jyE08","executionInfo":{"status":"ok","timestamp":1706305872483,"user_tz":-60,"elapsed":621,"user":{"displayName":"tim tensor","userId":"05709850039753462563"}},"outputId":"f84bac39-5e47-4fac-82eb-1dcca06a0ed5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["mkdir: cannot create directory ‘videos_folder’: File exists\n","mkdir: cannot create directory ‘output_folder’: File exists\n"]}]},{"cell_type":"code","source":["#extract 10 seconds\n","!ffmpeg -ss 00:00:00 -to 00:00:05 -i /content/videos_folder/popup.mp4 -c copy output.mp4\n"],"metadata":{"id":"rTfDwFvQzTVW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from IPython.display import HTML\n","from base64 import b64encode\n","\n","# Open the video file in binary mode\n","mp4 = open('/content/output_folder/workouts_0.mp4','rb').read()\n","\n","# Encode the video in base64\n","data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n","\n","# Display the video\n","HTML(\"\"\"\n","<video width=400 controls>\n","      <source src=\"%s\" type=\"video/mp4\">\n","</video>\n","\"\"\" % data_url)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":221,"output_embedded_package_id":"1lc-OdSbB4PIbqyvmZUClS84TDEiErqje"},"id":"ApolEmnMyuL5","executionInfo":{"status":"ok","timestamp":1706306386557,"user_tz":-60,"elapsed":7498,"user":{"displayName":"tim tensor","userId":"05709850039753462563"}},"outputId":"79f60d8b-f01c-46aa-fcb2-ece7711f77af"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"cell_type":"markdown","source":[],"metadata":{"id":"CUzB4K9ZzvXs"}},{"cell_type":"markdown","source":["# Configure Ultralytics"],"metadata":{"id":"PFLkO_rGzw2M"}},{"cell_type":"code","source":["%pip install ultralytics\n","import ultralytics\n","ultralytics.checks()"],"metadata":{"id":"iijSckOJz0G1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import os\n","import math\n","from ultralytics import YOLO\n","from ultralytics.solutions import ai_gym\n","import cv2\n","import cv2\n","import os\n","model = YOLO(\"yolov8n-pose.pt\")"],"metadata":{"id":"fEuOMud00Iat"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def draw_dotted_line(image, pt1, pt2, color, thickness=1, gap=5):\n","    dist = ((pt1[0]-pt2[0])**2 + (pt1[1]-pt2[1])**2)**.5\n","    pts = []\n","    for i in np.arange(0, dist, gap):\n","        r = i / dist\n","        x = int((pt1[0]*(1-r) + pt2[0]*r) + .5)\n","        y = int((pt1[1]*(1-r) + pt2[1]*r) + .5)\n","        p = (x, y)\n","        pts.append(p)\n","\n","    for p in pts:\n","        cv2.circle(image, p, thickness, color, -1)\n","\n","\n","def getAngle(a, b, c):\n","    ang = math.degrees(math.atan2(c[1]-b[1], c[0]-b[0]) - math.atan2(a[1]-b[1], a[0]-b[0]))\n","    return ang + 360 if ang < 0 else ang\n","\n","\n"],"metadata":{"id":"AITNVBvKRQ6v","executionInfo":{"status":"ok","timestamp":1706374581074,"user_tz":-60,"elapsed":243,"user":{"displayName":"tim tensor","userId":"05709850039753462563"}}},"execution_count":28,"outputs":[]},{"cell_type":"code","source":["\n","\n","# Videos processing\n","videos_folder = \"videos_folder\"\n","output_folder = \"output_folder\"\n","annotated_frame = results[0].plot()\n","\n","\n","if not os.path.exists(output_folder):\n","    os.mkdir(output_folder)\n","for ind, vid in enumerate(os.listdir(videos_folder)):\n","\n","\n","    cap = cv2.VideoCapture(os.path.join(videos_folder, vid))\n","    #assert cap.isOpened(), \"Error reading video file\"\n","\n","    # Video Writer\n","    video_writer = cv2.VideoWriter(os.path.join(output_folder, \"workouts_{}.mp4\".format(ind)),\n","                                   cv2.VideoWriter_fourcc(*'mp4v'),\n","                                   int(cap.get(5)),\n","                                   (int(cap.get(3)), int(cap.get(4))))\n","\n","    frame_count = 0\n","    while cap.isOpened():\n","        success, im0 = cap.read()\n","        if not success:\n","            print(\"Video processing has been successfully completed.\")\n","            break\n","\n","        frame_count += 1\n","        results = model.predict(im0, conf=0.25,verbose=False)\n","        for result in results:\n","            kpts = result.keypoints\n","            # kpts_conf returns a confidence keypoint for each of the keypoints\n","            kpts_conf = kpts.conf\n","            # Define the indices of the keypoints you want to connect\n","            dotted_line_indices = [[5, 7, 9], [6, 8, 10], [12, 14, 16], [11, 13,15]]\n","            colors = [(0, 255, 0), (0, 255, 255), (255, 0, 0), (255, 255, 0),(0,255,125)] # Colors for the dotted lines\n","            color_waist = (255,255,255)\n","            waist_indices =[11,12]\n","            if kpts.conf is not None:\n","\n","              for i, index_triplet in enumerate(dotted_line_indices): # iterate over the index triplets for dotted lines\n","                  for j in range(len(index_triplet)-1): # iterate over the indices within each triplet\n","                      # Check if the index exists in kpts_conf\n","\n","                      if index_triplet[j] >= len(kpts.conf[0]) or index_triplet[j+1] >= len(kpts.conf[0]):\n","                        continue\n","                      if kpts.conf[0][index_triplet[j]] < 0.5 or kpts.conf[0][index_triplet[j+1]] < 0.5:\n","                        continue\n","\n","                      keypoint = kpts.xy[0, index_triplet[j]]\n","                      x, y = int(keypoint[0].item()), int(keypoint[1].item())\n","                      #t = cv2.circle(im0, (x, y), 5, colors[i], -1)\n","\n","                      # Get the next keypoint\n","                      keypoint_next = kpts.xy[0, index_triplet[j+1]]\n","                      x_next, y_next = int(keypoint_next[0].item()), int(keypoint_next[1].item())\n","\n","                      # Draw a dotted line between the current keypoint and the next one\n","                      draw_dotted_line(im0, (x, y), (x_next, y_next), colors[i], 2)\n","\n","                      if j == len(index_triplet) - 2:\n","                          cv2.arrowedLine(im0, (x, y), (x_next, y_next), colors[i], 2)\n","\n","              #https://donologue.tistory.com/398\n","              kpt8 = kpts.xy[0][8]\n","              kpt6 = kpts.xy[0][6]\n","\n","              kpt5 = kpts.xy[0][5]\n","              kpt7 = kpts.xy[0][7]\n","              kpt9 = kpts.xy[0][9]\n","\n","              distance1 = ((kpt7[0] - kpt5[0]) ** 2 + (kpt7[1] - kpt5[1]) ** 2) ** 0.5\n","              distance2 = ((kpt6[0] - kpt8[0]) ** 2 + (kpt6[1] - kpt8[1]) ** 2) ** 0.5\n","\n","              radius1 = distance1 / 2.5\n","              radius2 = distance2 / 2.5\n","\n","              cv2.circle(im0, (int(kpt7[0].item()), int(kpt7[1].item())), int(radius1), (255, 255, 255), 2)\n","              cv2.circle(im0, (int(kpt8[0].item()), int(kpt8[1].item())), int(radius2), (255, 255, 255), 2)\n","\n","              overlay = im0.copy()\n","              cv2.circle(overlay, (int(kpt7[0].item()), int(kpt7[1].item())), int(radius1), (255, 255, 255), -1)\n","              cv2.circle(overlay, (int(kpt8[0].item()), int(kpt8[1].item())), int(radius2), (255, 255, 255), -1)\n","              alpha = 0.4\n","              im0 = cv2.addWeighted(overlay, alpha, im0, 1 - alpha, 0)\n","\n","              percievedAngle = 360 - getAngle(kpt5,kpt7,kpt9)\n","\n","              x_str = str(int(percievedAngle))\n","              print(x_str)\n","              #cv2.putText(im0, x_str, (int(kpt7[0].item()), int(kpt7[1].item())), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n","              cv2.putText(im0, x_str, (int(kpt7[0].item()), int(kpt7[1].item())), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n","\n","        video_writer.write(im0)\n","    video_writer.release()\n","    cap.release()\n","\n","cv2.destroyAllWindows()"],"metadata":{"id":"jpxvCCRF0Rd0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(kpts.xy)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FthJYOQ62kWe","executionInfo":{"status":"ok","timestamp":1706376651876,"user_tz":-60,"elapsed":244,"user":{"displayName":"tim tensor","userId":"05709850039753462563"}},"outputId":"548a2c3e-64bf-4972-ca7e-9badc8afd998"},"execution_count":47,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([], size=(1, 0, 2))\n"]}]},{"cell_type":"code","source":["for result in results:\n","    kptstest = result.keypoints.conf[0][15]\n","    print(kptstest)\n","    nk = kpts.shape[1]\n","    conf_index = []\n","    for i in enumerate(dotted_line_indices):\n","        conf_4 = kpts\n","        indices = kpts[0][15]\n","\n"],"metadata":{"id":"wvkH4EnC_aUN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(kpts.xy[0][16])\n","print(kpts.xy)"],"metadata":{"id":"aLQ_-yu8GLiC"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Instagram videos"],"metadata":{"id":"8gldqPCfvc6Z"}},{"cell_type":"code","source":[" for result in results:\n","    kpts_conf = result.keypoints.conf\n","    print(kpts_conf)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NyJf2bNh2Ryk","executionInfo":{"status":"ok","timestamp":1706308477378,"user_tz":-60,"elapsed":218,"user":{"displayName":"tim tensor","userId":"05709850039753462563"}},"outputId":"d8ee61cd-e9bb-4215-e37a-7b33ebdd13e6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[0.9395, 0.9464, 0.7655, 0.8654, 0.2237, 0.9635, 0.8512, 0.9287, 0.6354, 0.9098, 0.6766, 0.6462, 0.4666, 0.1832, 0.0989, 0.0668, 0.0440]])\n"]}]},{"cell_type":"markdown","source":["# Configure ther way to vizaualize"],"metadata":{"id":"Jh-HbWhx2iHT"}},{"cell_type":"code","source":["import moviepy.editor\n","moviepy.editor.ipython_display(\"/content/output_folder/workouts_0.mp4\",autoplay=1)\n"],"metadata":{"id":"DiSpN2DU2oNZ"},"execution_count":null,"outputs":[]}]}