Delete src

src/.ipynb_checkpoints/Webcam_Object_Detection-checkpoint.ipynb

@@ -1,158 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d9904ec5-391d-4967-9357-c8779d677142",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import required libraries\n",
-    "from ultralytics import YOLO\n",
-    "import gradio as gr\n",
-    "import cv2\n",
-    "import math\n",
-    "from items import classNames"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "1dbb6ae7-c844-4933-9a5c-f778bb1dfa83",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# detection function\n",
-    "def yolo_detect(feed, vid):\n",
-    "    video = vid\n",
-    "    # Load a pretrained YOLOv8n model\n",
-    "    model = YOLO('yolov8n.pt')\n",
-    "    \n",
-    "    # Run inference on the source\n",
-    "    results = model(video, stream=True, verbose=False) \n",
-    "    frames = list()\n",
-    "    \n",
-    "    # plot annotations\n",
-    "    for frame in results:\n",
-    "        boxes = frame.boxes\n",
-    "        single = frame.orig_img\n",
-    "        for box in boxes:\n",
-    "            # bounding box\n",
-    "            x1, y1, x2, y2 = box.xyxy[0]\n",
-    "            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) # convert to int values\n",
-    "\n",
-    "            # put box in cam\n",
-    "            cv2.rectangle(single, (x1, y1), (x2, y2), (255, 0, 255), 3)\n",
-    "\n",
-    "            # object details\n",
-    "            cv2.putText(single, classNames[int(box.cls[0])], (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 1)\n",
-    "            \n",
-    "        frames.append(single)\n",
-    "    cv2.destroyAllWindows()\n",
-    "    \n",
-    "    h, w, c = frames[1].shape\n",
-    "    \n",
-    "    out_file = \"output.avi\"\n",
-    "    fourcc=cv2.VideoWriter_fourcc('X', 'V', 'I', 'D')\n",
-    "    writer = out = cv2.VideoWriter(out_file, fourcc, 25.0, (w, h))\n",
-    "    for i in range(len(frames)):\n",
-    "        writer.write(frames[i])\n",
-    "    writer.release()\n",
-    "    return out_file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "692f5c49-67cd-4c11-8ee9-03dc7cb98809",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\faiza\\anaconda3\\envs\\hgace\\Lib\\site-packages\\gradio\\utils.py:833: UserWarning: Expected 1 arguments for function <function yolo_detect at 0x000001B002054860>, received 2.\n",
-      "  warnings.warn(\n",
-      "C:\\Users\\faiza\\anaconda3\\envs\\hgace\\Lib\\site-packages\\gradio\\utils.py:841: UserWarning: Expected maximum 1 arguments for function <function yolo_detect at 0x000001B002054860>, received 2.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7861\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\faiza\\anaconda3\\envs\\hgace\\Lib\\site-packages\\gradio\\routes.py\", line 442, in run_predict\n",
-      "    output = await app.get_blocks().process_api(\n",
-      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"C:\\Users\\faiza\\anaconda3\\envs\\hgace\\Lib\\site-packages\\gradio\\blocks.py\", line 1392, in process_api\n",
-      "    result = await self.call_function(\n",
-      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"C:\\Users\\faiza\\anaconda3\\envs\\hgace\\Lib\\site-packages\\gradio\\blocks.py\", line 1097, in call_function\n",
-      "    prediction = await anyio.to_thread.run_sync(\n",
-      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"C:\\Users\\faiza\\anaconda3\\envs\\hgace\\Lib\\site-packages\\anyio\\to_thread.py\", line 33, in run_sync\n",
-      "    return await get_asynclib().run_sync_in_worker_thread(\n",
-      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"C:\\Users\\faiza\\anaconda3\\envs\\hgace\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 877, in run_sync_in_worker_thread\n",
-      "    return await future\n",
-      "           ^^^^^^^^^^^^\n",
-      "  File \"C:\\Users\\faiza\\anaconda3\\envs\\hgace\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 807, in run\n",
-      "    result = context.run(func, *args)\n",
-      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"C:\\Users\\faiza\\anaconda3\\envs\\hgace\\Lib\\site-packages\\gradio\\utils.py\", line 703, in wrapper\n",
-      "    response = f(*args, **kwargs)\n",
-      "               ^^^^^^^^^^^^^^^^^^\n",
-      "TypeError: yolo_detect() takes 1 positional argument but 2 were given\n"
-     ]
-    }
-   ],
-   "source": [
-    "demo = gr.Interface(fn=yolo_detect, \n",
-    "                    inputs=[gr.PlayableVideo(source='webcam'), gr.Video(autoplay=True)],\n",
-    "                    outputs=[gr.Video(autoplay=True, format='avi')],\n",
-    "                    cache_examples=True, allow_flagging='never')\n",
-    "demo.queue()\n",
-    "demo.launch(inline=False, debug=True, show_api=False, quiet=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "120eca17-b44a-4cf9-86fc-651ddf791ffa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# demo.close()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

src/.ipynb_checkpoints/realtime-checkpoint.py

@@ -1,42 +0,0 @@
-# import libraries
-from ultralytics import YOLO
-import cv2
-import sys
-
-def realtime(video):
-    # Load the YOLOv8 model
-    model = YOLO('yolov8n.pt')
-    
-    # Open the video file
-    video_path = video
-    cap = cv2.VideoCapture(video_path)
-    cap.set(3, 720)
-    cap.set(4, 1280)
-    # Loop through the video frames
-    while cap.isOpened():
-        # Read a frame from the video
-        success, frame = cap.read()
-    
-        if success:
-            # Run YOLOv8 inference on the frame
-            results = model(frame, verbose=False)
-    
-            # Visualize the results on the frame
-            annotated_frame = results[0].plot()
-    
-            # Display the annotated frame
-            cv2.imshow("YOLOv8 Inference", annotated_frame)
-    
-            # Break the loop if 'q' is pressed
-            if cv2.waitKey(1) & 0xFF == ord("q"):
-                break
-        else:
-            # Break the loop if the end of the video is reached
-            break
-    
-    # Release the video capture object and close the display window
-    cap.release()
-    cv2.destroyAllWindows()
-    
-if __name__ == '__main__':
-    realtime(sys.argv[1])

src/Webcam_Object_Detection.ipynb

@@ -1,101 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d9904ec5-391d-4967-9357-c8779d677142",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import required libraries\n",
-    "from ultralytics import YOLO\n",
-    "import gradio as gr\n",
-    "import cv2\n",
-    "import math\n",
-    "from items import classNames"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1dbb6ae7-c844-4933-9a5c-f778bb1dfa83",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# detection function\n",
-    "def yolo_detect(feed, vid):\n",
-    "    video = vid\n",
-    "    # Load a pretrained YOLOv8n model\n",
-    "    model = YOLO('yolov8n.pt')\n",
-    "    \n",
-    "    # Run inference on the source\n",
-    "    results = model(video, stream=True, verbose=False) \n",
-    "    frames = list()\n",
-    "    \n",
-    "    # plot annotations\n",
-    "    for frame in results:\n",
-    "        boxes = frame.boxes\n",
-    "        single = frame.orig_img\n",
-    "        for box in boxes:\n",
-    "            # bounding box\n",
-    "            x1, y1, x2, y2 = box.xyxy[0]\n",
-    "            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) # convert to int values\n",
-    "\n",
-    "            # put box in cam\n",
-    "            cv2.rectangle(single, (x1, y1), (x2, y2), (255, 0, 255), 3)\n",
-    "\n",
-    "            # object details\n",
-    "            cv2.putText(single, classNames[int(box.cls[0])], (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 1)\n",
-    "            \n",
-    "        frames.append(single)\n",
-    "    cv2.destroyAllWindows()\n",
-    "    \n",
-    "    h, w, c = frames[1].shape\n",
-    "    \n",
-    "    out_file = \"output.avi\"\n",
-    "    fourcc=cv2.VideoWriter_fourcc('X', 'V', 'I', 'D')\n",
-    "    writer = out = cv2.VideoWriter(out_file, fourcc, 25.0, (w, h))\n",
-    "    for i in range(len(frames)):\n",
-    "        writer.write(frames[i])\n",
-    "    writer.release()\n",
-    "    return out_file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "692f5c49-67cd-4c11-8ee9-03dc7cb98809",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "demo = gr.Interface(fn=yolo_detect, \n",
-    "                    inputs=[gr.PlayableVideo(source='webcam'), gr.Video(autoplay=True)],\n",
-    "                    outputs=[gr.PlayableVideo(autoplay=True, format='avi')],\n",
-    "                    cache_examples=True, allow_flagging='never')\n",
-    "demo.queue()\n",
-    "demo.launch(inline=False, debug=True, show_api=False, quiet=True)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

src/app.py

@@ -1,66 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[ ]:
-
-
-# import required libraries
-from ultralytics import YOLO
-import gradio as gr
-import cv2
-import math
-from items import classNames
-
-
-# In[ ]:
-
-
-# detection function
-def yolo_detect(feed, vid):
-    video = vid
-    # Load a pretrained YOLOv8n model
-    model = YOLO('yolov8n.pt')
-    
-    # Run inference on the source
-    results = model(video, stream=True, verbose=False) 
-    frames = list()
-    
-    # plot annotations
-    for frame in results:
-        boxes = frame.boxes
-        single = frame.orig_img
-        for box in boxes:
-            # bounding box
-            x1, y1, x2, y2 = box.xyxy[0]
-            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) # convert to int values
-
-            # put box in cam
-            cv2.rectangle(single, (x1, y1), (x2, y2), (255, 0, 255), 3)
-
-            # object details
-            cv2.putText(single, classNames[int(box.cls[0])], (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 1)
-            
-        frames.append(single)
-    cv2.destroyAllWindows()
-    
-    h, w, c = frames[1].shape
-    
-    out_file = "output.avi"
-    fourcc=cv2.VideoWriter_fourcc('X', 'V', 'I', 'D')
-    writer = out = cv2.VideoWriter(out_file, fourcc, 25.0, (w, h))
-    for i in range(len(frames)):
-        writer.write(frames[i])
-    writer.release()
-    return out_file
-
-
-# In[ ]:
-
-
-demo = gr.Interface(fn=yolo_detect, 
-                    inputs=[gr.PlayableVideo(source='webcam'), gr.Video(autoplay=True)],
-                    outputs=[gr.PlayableVideo(autoplay=True, format='avi')],
-                    cache_examples=True, allow_flagging='never')
-demo.queue()
-demo.launch(inline=False, debug=True, show_api=False, quiet=True)
-

src/items.py

@@ -1,12 +0,0 @@
-# object classes
-classNames = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat",
-              "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat",
-              "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
-              "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat",
-              "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup",
-              "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli",
-              "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed",
-              "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone",
-              "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors",
-              "teddy bear", "hair drier", "toothbrush"
-              ]

src/output.avi

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:12a1475d91cadf7ae8c048c1fb342c8c4ebfe806cfb4de08705c3f53fb07f1d2
-size 88264158

src/realtime.py

@@ -1,42 +0,0 @@
-# import libraries
-from ultralytics import YOLO
-import cv2
-import sys
-
-def realtime(video):
-    # Load the YOLOv8 model
-    model = YOLO('yolov8n.pt')
-    
-    # Open the video file
-    video_path = video
-    cap = cv2.VideoCapture(video_path)
-    cap.set(3, 720)
-    cap.set(4, 1280)
-    # Loop through the video frames
-    while cap.isOpened():
-        # Read a frame from the video
-        success, frame = cap.read()
-    
-        if success:
-            # Run YOLOv8 inference on the frame
-            results = model(frame, verbose=False)
-    
-            # Visualize the results on the frame
-            annotated_frame = results[0].plot()
-    
-            # Display the annotated frame
-            cv2.imshow("YOLOv8 Inference", annotated_frame)
-    
-            # Break the loop if 'q' is pressed
-            if cv2.waitKey(1) & 0xFF == ord("q"):
-                break
-        else:
-            # Break the loop if the end of the video is reached
-            break
-    
-    # Release the video capture object and close the display window
-    cap.release()
-    cv2.destroyAllWindows()
-    
-if __name__ == '__main__':
-    realtime(sys.argv[1])

src/yolov8n.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:31e20dde3def09e2cf938c7be6fe23d9150bbbe503982af13345706515f2ef95
-size 6534387