test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-17T07:02:02.246187Z",
     "start_time": "2021-09-17T07:02:02.134087Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-17T07:02:03.314484Z",
     "start_time": "2021-09-17T07:02:02.476773Z"
    }
   },
   "outputs": [],
   "source": [
    "from track import detect\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-17T07:02:03.351244Z",
     "start_time": "2021-09-17T07:02:03.347399Z"
    }
   },
   "outputs": [],
   "source": [
    "def showFirstFrame(path):\n",
    "    cap = cv2.VideoCapture(path)\n",
    "    success, img = cap.read()\n",
    "    if success:\n",
    "        rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # this converts it into RGB\n",
    "        fig,ax = plt.subplots(figsize=(16,9))\n",
    "        ax.imshow(rgb_img)\n",
    "        plt.show()\n",
    "\n",
    "def showLastFrame(path):\n",
    "    cap = cv2.VideoCapture(path)\n",
    "    last_frame_num = cap.get(cv2.CAP_PROP_FRAME_COUNT)\n",
    "    cap.set(cv2.CAP_PROP_POS_FRAMES, last_frame_num-1)\n",
    "    success, img = cap.read()\n",
    "    if success:\n",
    "        rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # this converts it into RGB\n",
    "        fig,ax = plt.subplots(figsize=(16,9))\n",
    "        ax.imshow(rgb_img)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-17T07:02:03.389272Z",
     "start_time": "2021-09-17T07:02:03.386835Z"
    }
   },
   "outputs": [],
   "source": [
    "# show first and last frames for timestamps\n",
    "# showFirstFrame(path1)\n",
    "# showLastFrame(path2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-17T07:02:03.563946Z",
     "start_time": "2021-09-17T07:02:03.469473Z"
    }
   },
   "outputs": [],
   "source": [
    "showFirstFrame(r'/home/antoine/projects/Yolov5_DeepSort_Pytorch/data/raw/sep14/Z/TLC00001.AVI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-17T07:02:03.792263Z",
     "start_time": "2021-09-17T07:02:03.788956Z"
    }
   },
   "outputs": [],
   "source": [
    "#showLastFrame('/home/antoine/projects/Yolov5_DeepSort_Pytorch/data/raw/check2/TLC00005.AVI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-17T07:02:04.166135Z",
     "start_time": "2021-09-17T07:02:04.158828Z"
    }
   },
   "outputs": [],
   "source": [
    "# define the arguments of the function as a kwargs dict\n",
    "kwargs = {\n",
    "    \"yolo_weights\": r\"/home/antoine/projects/Yolov5_DeepSort_Pytorch/yolov5/weights/crowdhuman_yolov5m.pt\",\n",
    "    \"deep_sort_weights\": \"deep_sort_pytorch/deep_sort/deep/checkpoint/ckpt.t7\",\n",
    "    \"source\": r\"/home/antoine/projects/Yolov5_DeepSort_Pytorch/data/raw/sep14/Z\",\n",
    "    \"output\": r\"data/processed/sep14/Z/\",\n",
    "    \"img_size\": 640,\n",
    "    \"conf_thres\": 0.4,\n",
    "    \"iou_thres\": 0.5,\n",
    "    \"fourcc\": \"mp4\",\n",
    "    \"device\": \"\",\n",
    "    \"show_vid\": False,\n",
    "    \"save_vid\": True,\n",
    "    \"save_txt\": False,\n",
    "    \"classes\": 0,\n",
    "    \"agnostic_nms\": True,\n",
    "    \"augment\": True,\n",
    "    \"evaluate\": True,\n",
    "    \"config_deepsort\": \"deep_sort_pytorch/configs/deep_sort.yaml\",\n",
    "    \"W_line1\": 350,\n",
    "    \"W_line2\": 1200,\n",
    "    \"counting_direction\": -1,\n",
    "}\n",
    "\n",
    "# transform them into an instance of a class to be passed to function\n",
    "opt = type(\"option_object\", (object,), kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-17T11:13:16.931329Z",
     "start_time": "2021-09-17T07:02:04.713286Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/antoine/miniconda3/envs/test_pytorch19/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /opt/conda/conda-bld/pytorch_1623448278899/work/c10/core/TensorImpl.h:1156.)\n",
      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "progress bar show only progress for videos\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "processing TLC00000.AVI (file 1/16):  70%|██████████████████████████▋           | 14414/20480.0 [12:09<05:06, 19.77it/s]\n",
      "processing TLC00001.AVI (file 1/16):  30%|███████████▌                           | 6066/20480.0 [05:01<11:55, 20.14it/s]\n",
      "processing TLC00002.AVI (file 2/16): 301134it [3:53:57, 20.72it/s]                                                      "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results saved to /home/antoine/projects/Yolov5_DeepSort_Pytorch/data/processed/sep14/Z/\n",
      "Done. (15067.832s)\n",
      "total Pax= 645\n"
     ]
    }
   ],
   "source": [
    "list_detection_frames = detect(opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-17T11:13:17.001691Z",
     "start_time": "2021-09-17T11:13:16.982405Z"
    }
   },
   "outputs": [
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