slot-detection.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-08 15:49:08.556456: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import cv2\n",
    "import uuid\n",
    "import os\n",
    "import time\n",
    "import tensorflow as tf\n",
    "import PIL.Image as Image\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'out_height' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb Cell 4\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#W3sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m path \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(\u001b[39m\"\u001b[39m\u001b[39mimages\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39mimages\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39m100.jpeg\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#W3sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m IMG_SIZE \u001b[39m=\u001b[39m \u001b[39m220\u001b[39m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#W3sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m resize \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mkeras\u001b[39m.\u001b[39mlayers\u001b[39m.\u001b[39mResizing(out_height, out_width)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#W3sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m zoom \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mkeras\u001b[39m.\u001b[39mlayers\u001b[39m.\u001b[39mRandomZoom(\u001b[39m0.3\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#W3sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m flip \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mkeras\u001b[39m.\u001b[39mlayers\u001b[39m.\u001b[39mRandomFlip(\u001b[39m\"\u001b[39m\u001b[39mhorizontal_and_vertical\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'out_height' is not defined"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import layers\n",
    "path = os.path.join(\"images\",\"train\",\"images\",\"100.jpeg\")\n",
    "IMG_SIZE = 220\n",
    "\n",
    "resize = tf.keras.layers.Resizing(out_height, out_width)\n",
    "zoom = tf.keras.layers.RandomZoom(0.3)\n",
    "flip = tf.keras.layers.RandomFlip(\"horizontal_and_vertical\")\n",
    "rotate = tf.keras.layers.RandomRotation(0.2)\n",
    "crop = tf.keras.layers.RandomCrop(out_height, out_width)\n",
    "translation = tf.keras.layers.RandomTranslation(height_factor=0.2, width_factor=0.2)\n",
    "\n",
    "brightness = tf.keras.layers.RandomBrightness([-0.8,0.8])\n",
    "contrast = tf.keras.layers.RandomContrast(0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annotate.ipynb  \u001b[0m\u001b[01;34mimages\u001b[0m/  result_img.jpeg       \u001b[01;34mslot-images\u001b[0m/  \u001b[01;34myolov5\u001b[0m/\n",
      "\u001b[01;34mdatasets\u001b[0m/       \u001b[01;34mlabels\u001b[0m/  slot-detection.ipynb  \u001b[01;34mtest_env\u001b[0m/     yolov5s.pt\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rotation\n",
    "directory = \"images/train/images\"\n",
    "\n",
    "for i in os.listdir(directory):\n",
    "    image = Image.open(f\"{directory}/{i}\")\n",
    "    rotated_image = image.rotate(90)\n",
    "    rotated_image.save(f\"images/rotated_images/90/{i}\", format=\"JPEG\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annotate.ipynb  images.zip       slot-detection.ipynb  \u001b[0m\u001b[01;34myolov5\u001b[0m/\n",
      "\u001b[01;34mdatasets\u001b[0m/       \u001b[01;34mlabels\u001b[0m/          \u001b[01;34mslot-images\u001b[0m/          yolov5s.pt\n",
      "\u001b[01;34mimages\u001b[0m/         result_img.jpeg  \u001b[01;34mtest_env\u001b[0m/\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Brightness and contrast\n",
    "directory = \"images/train/images\"\n",
    "\n",
    "for i in os.listdir(directory):\n",
    "        image = tf.keras.preprocessing.image.load_img(\"images/train/images/\"+i)\n",
    "        image = tf.keras.preprocessing.image.img_to_array(image)\n",
    "\n",
    "        i = i[:-5]\n",
    "        # #brightness\n",
    "        # bright_image = tf.image.random_brightness(image,0.8)\n",
    "        # bright_image = tf.keras.backend.eval(bright_image)\n",
    "        # bright_image = Image.fromarray(bright_image.astype('uint8'))\n",
    "        # bright_image.save(f\"images/augmented_images/images/{i}_bright.jpeg\")\n",
    "        \n",
    "        #contrast\n",
    "        contrast_image = tf.image.random_contrast(image,0.5,1)\n",
    "        contrast_image = tf.keras.backend.eval(contrast_image)\n",
    "        contrast_image = Image.fromarray(contrast_image.astype('uint8'))\n",
    "        contrast_image.save(f\"images/augmented_images/images/{i}_contrast.jpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/cps/Documents/slot-detection/Slot-Detection\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_directory = \"images/train/labels\"\n",
    "\n",
    "def add_tags(file_name):\n",
    "    file = file_name\n",
    "    file_name = file_name[:-4]\n",
    "    bright = file_name + \"_bright\" \n",
    "    contrast = file_name + \"_contrast\"\n",
    "\n",
    "    with open(label_directory+\"/\"+file, \"r\") as f:\n",
    "        content = f.read()\n",
    "\n",
    "\n",
    "    with open(\"images/augmented_images/labels/\" + bright +\".txt\", \"w\") as f:\n",
    "        f.write(content.upper())\n",
    "\n",
    "    with open(\"images/augmented_images/labels/\" + contrast+\".txt\", \"w\") as f:\n",
    "        f.write(content.lower())\n",
    "\n",
    "\n",
    "for i in os.listdir(label_directory):\n",
    "    add_tags(i) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_directory = \"images/rotated_images/180_labels\"\n",
    "\n",
    "def add_tags(file_name):\n",
    "    file = file_name\n",
    "    file_name = file_name[:-4]\n",
    "    bright = file_name + \"_180\" \n",
    "    contrast = file_name + \"_180\"\n",
    "\n",
    "    with open(label_directory+\"/\"+file, \"r\") as f:\n",
    "        content = f.read()\n",
    "\n",
    "\n",
    "    with open(\"images/rotated_images/180_labels/\" + bright +\".txt\", \"w\") as f:\n",
    "        f.write(content.upper())\n",
    "\n",
    "    with open(\"images/rotated_images/180_labels/\" + contrast +\".txt\", \"w\") as f:\n",
    "        f.write(content.lower())\n",
    "\n",
    "\n",
    "for i in os.listdir(label_directory):\n",
    "    add_tags(i) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/cps/Documents/slot-detection/Slot-Detection\n"
     ]
    }
   ],
   "source": [
    "cd Documents/slot-detection/Slot-Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rename_files(directory):\n",
    "    for file in os.listdir(directory):\n",
    "        if file.endswith(\".jpeg\"):\n",
    "            new_file = f\"{file[:-4]}_1.jpeg\"\n",
    "            os.rename(os.path.join(directory, file), os.path.join(directory, new_file))\n",
    "        elif file.endswith(\".png\"):\n",
    "            new_file = f\"{file[:-4]}_1.png\"\n",
    "            os.rename(os.path.join(directory, file), os.path.join(directory, new_file))\n",
    "\n",
    "rename_files(\"images/augmented_images/images/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "again\n",
    "- resizing\n",
    "- rescaling\n",
    "- zoom\n",
    "- crop\n",
    "same\n",
    "- brightness\n",
    "- contrast\n",
    "- hue?\n",
    "- saturation?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isValidSpace(cropped_image):\n",
    "\n",
    "    lower_black = np.array([0,0,0], dtype=\"uint8\")\n",
    "    upper_black = np.array([120,150,150],dtype = \"uint8\")\n",
    "\n",
    "    if(DEBUG):\n",
    "        print(\"Got Image: \")\n",
    "        plt.imshow(cropped_image)\n",
    "        plt.show()\n",
    "    cropped_image = np.array(cropped_image)\n",
    "\n",
    "\n",
    "    mask = cv2.inRange(cropped_image, lower_black, upper_black)\n",
    "    zero_count = 0\n",
    "    one_count = 0\n",
    "    for i in mask:\n",
    "        zero_count += list(i).count(0)\n",
    "        one_count += list(i).count(255)\n",
    "    \n",
    "    if DEBUG:\n",
    "        print(mask)\n",
    "        print(f\"The output is : {zero_count > one_count} \\n zero_count: {zero_count}, one_count: {one_count}\")\n",
    "\n",
    "    if( zero_count > one_count):\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Starting (x,y) coord of board = 464, 16\n",
    "\n",
    "# side length of board = 850 pixels\n",
    "# square length = 95 pixels \n",
    "\n",
    "# distance between two squares = 70 \n",
    "\n",
    "\n",
    "def annotate_image(name):\n",
    "\n",
    "    txt_file_name = f\"{name.split('.')[0]}.txt\"\n",
    "\n",
    "    img = cv2.imread(f\"dataset/{name}\")\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    start_point = [553, 112] # upper left coord of 1st square\n",
    "    side_length = 73 # side length of square\n",
    "\n",
    "    square_distance = 76\n",
    "\n",
    "    image = img\n",
    "\n",
    "    file = open(f\"annotated/{txt_file_name}\",\"w\")\n",
    "\n",
    "    if(DEBUG):\n",
    "        print(\"Orignal Image: \")\n",
    "        plt.imshow(img)\n",
    "        plt.show()\n",
    "\n",
    "    for j in range(5):\n",
    "\n",
    "        for i in range(5):\n",
    "            new_image=  []\n",
    "\n",
    "            for i in range(start_point[1], start_point[1] +  side_length ):\n",
    "                single_row = []\n",
    "                for j in range(start_point[0], start_point[0] + side_length):\n",
    "                    single_row.append(image[i][j])\n",
    "                \n",
    "                new_image.append(single_row)\n",
    "            \n",
    "\n",
    "            if(isValidSpace(new_image)):\n",
    "                image = cv2.rectangle(image, start_point, (start_point[0] + side_length, start_point[1] + side_length), (255,0,0), 2)\n",
    "                file.write(f\"15  {int((start_point[0] + start_point[0] + 73)/2)/1655} {int((start_point[1] + start_point[1] + 73)/2)/931} {side_length/1655} {side_length/931}\")\n",
    "                file.write(\"\\n\")\n",
    "\n",
    "            start_point[0] += side_length + square_distance \n",
    "        start_point[1]  += side_length + square_distance\n",
    "        start_point[0] -= 5*(side_length+ square_distance)\n",
    "\n",
    "    # plt.imshow(image)\n",
    "    # plt.show()\n",
    "\n",
    "    cv2.imwrite(f'annotated/{name}',cv2.cvtColor(image,cv2.COLOR_RGB2BGR))\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def list_png_files(directory):\n",
    "    png_files = []\n",
    "    for root, dirnames, filenames in os.walk(directory):\n",
    "        for filename in fnmatch.filter(filenames, '*.png'):\n",
    "            png_files.append(os.path.join(root, filename))\n",
    "    return png_files\n",
    "\n",
    "def list_jpeg_files(directory):\n",
    "    png_files = []\n",
    "    for root, dirnames, filenames in os.walk(directory):\n",
    "        for filename in fnmatch.filter(filenames, '*.jpeg'):\n",
    "            png_files.append(os.path.join(root, filename))\n",
    "    return png_files\n",
    "# Example usage\n",
    "directory = 'dataset/'\n",
    "png_files = list_png_files(directory)\n",
    "for file in png_files:\n",
    "    filename = file.split('/')[-1]\n",
    "    print(filename)\n",
    "    annotate_image(filename)\n",
    "\n",
    "# annotate_image('20.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yolov5 Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.hub.load(\"ultralytics/yolov5\", \"yolov5s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = \"https://ultralytics.com/images/zidane.jpg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = model(img)\n",
    "results.print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb Cell 8\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#X10sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     results \u001b[39m=\u001b[39m model(frame)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#X10sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m     cv2\u001b[39m.\u001b[39mimshow(\u001b[39m\"\u001b[39m\u001b[39mYOLO\u001b[39m\u001b[39m\"\u001b[39m,np\u001b[39m.\u001b[39msqueeze(results\u001b[39m.\u001b[39mrender()))\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#X10sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m     \u001b[39mif\u001b[39;00m cv2\u001b[39m.\u001b[39;49mwaitKey(\u001b[39m10\u001b[39;49m) \u001b[39m&\u001b[39m \u001b[39m0XFF\u001b[39m \u001b[39m==\u001b[39m \u001b[39mord\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mq\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#X10sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m         \u001b[39mbreak\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#X10sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m cap\u001b[39m.\u001b[39mrelease()\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "while cap.isOpened():\n",
    "    ret, frame = cap.read()\n",
    "    results = model(frame)\n",
    "    cv2.imshow(\"YOLO\",np.squeeze(results.render()))\n",
    "\n",
    "    if cv2.waitKey(10) & 0XFF == ord(\"q\"):\n",
    "        break\n",
    "cap.release()\n",
    "cv2.destroyAllWindows() "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Yolo Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/cps/Documents/slot-detection/Slot-Detection/yolov5\n"
     ]
    }
   ],
   "source": [
    "cd yolov5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=dataset.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=32, imgsz=720, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=Adam, sync_bn=False, workers=20, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0mskipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5\n",
      "YOLOv5 🚀 2023-8-2 Python-3.8.10 torch-2.0.1+cu117 CUDA:0 (NVIDIA TITAN RTX, 24212MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "Overriding model.yaml nc=80 with nc=16\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     56637  models.yolo.Detect                      [16, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "Model summary: 214 layers, 7062781 parameters, 7062781 gradients, 16.1 GFLOPs\n",
      "\n",
      "Transferred 343/349 items from yolov5s.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "WARNING ⚠️ --img-size 720 must be multiple of max stride 32, updating to 736\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m Adam(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cps/Documents/slot-detection/Slot-Detection/images/all/lab\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /home/cps/Documents/slot-detection/Slot-Detection/images/val/label\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m6.41 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp49/labels.jpg... \n",
      "Image sizes 736 train, 736 val\n",
      "Using 20 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp49\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      9.12G     0.1286    0.09476    0.07752        519        736:  WARNING ⚠️ TensorBoard graph visualization failure Sizes of tensors must match except in dimension 1. Expected size 46 but got size 45 for tensor number 1 in the list.\n",
      "       0/49      9.12G    0.06928    0.07731   0.006624        336        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318     0.0313      0.424     0.0293    0.00827\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      9.12G    0.05112    0.06665  0.0001901        383        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.258      0.473      0.296     0.0808\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      9.12G    0.04804    0.06664  0.0002283        483        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318       0.41       0.34      0.356      0.132\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      9.12G    0.04238    0.06536  0.0001965        383        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318          0          0          0          0\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      9.12G    0.04116    0.06354  0.0001687        441        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318    0.00294     0.0611    0.00167   0.000643\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      9.12G    0.03861    0.05949  0.0001477        489        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.192      0.495      0.204      0.131\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      9.12G    0.03375    0.05709  0.0001328        386        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.474      0.355      0.442      0.265\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      9.12G     0.0356    0.05725  0.0001451        591        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.488      0.469      0.491      0.376\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      9.12G    0.03332    0.05466  0.0001331        371        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.341      0.495      0.433      0.233\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      9.12G    0.03273    0.05572  0.0002885        388        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318     0.0535      0.459     0.0587      0.024\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      9.12G    0.03416    0.05782  0.0001467        400        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.475      0.495      0.491      0.253\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      9.12G     0.0309    0.05563  0.0001084        506        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.481      0.495      0.493       0.36\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      9.12G    0.03021    0.05333  0.0001193        473        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.472      0.498      0.478      0.335\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      9.12G    0.02717    0.04971  0.0001078        393        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.479      0.493      0.486      0.348\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      9.12G    0.02723    0.04996  0.0001148        331        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476      0.498      0.478      0.348\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      9.12G    0.02654    0.04905   0.000112        466        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.318      0.439      0.413      0.249\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      9.12G    0.02798    0.05093  0.0001191        462        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.462      0.442      0.471      0.329\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      9.12G     0.0265    0.04915  0.0001118        325        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.477      0.459      0.485      0.389\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      9.12G    0.02598    0.04964   0.000111        433        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476      0.498      0.493      0.366\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      9.12G      0.024    0.04667  0.0001088        319        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476      0.498      0.489      0.397\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      9.12G    0.02407    0.04688  9.465e-05        357        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476      0.498      0.486      0.297\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      9.12G    0.02397    0.04702  8.851e-05        460        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476      0.493      0.481      0.372\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      9.12G    0.02366    0.04585  9.506e-05        508        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.383      0.498      0.463      0.379\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      9.12G    0.02252    0.04506  9.953e-05        460        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476      0.495      0.491      0.403\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      9.12G    0.02191     0.0454  8.651e-05        451        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476      0.493       0.49      0.403\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      9.12G    0.02273     0.0438  8.973e-05        451        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476      0.498      0.494      0.404\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      9.12G    0.02078    0.04212  8.605e-05        373        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476      0.498      0.484      0.407\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      9.12G     0.0211    0.04347  9.049e-05        430        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.473      0.493      0.476      0.387\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      9.12G    0.01991    0.04155   8.44e-05        424        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.475      0.494      0.492      0.338\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      9.12G    0.02083    0.04251  8.875e-05        440        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.473      0.447      0.474      0.397\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      9.12G    0.01938    0.04055  8.532e-05        360        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.477      0.498      0.491      0.405\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      9.12G    0.01872    0.04012  8.464e-05        439        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476      0.498      0.493      0.383\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      9.12G    0.01842    0.03958  7.933e-05        430        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476        0.5      0.481      0.402\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      9.12G    0.01824    0.03943  7.763e-05        467        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476      0.498      0.477       0.41\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      9.12G    0.01825    0.03909  8.027e-05        326        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.469      0.498      0.487      0.414\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      9.12G     0.0176    0.03846  7.581e-05        386        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476      0.498      0.484      0.415\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      9.12G    0.01632    0.03702   7.84e-05        366        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476        0.5      0.481      0.418\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      9.12G    0.01575    0.03592  7.327e-05        493        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476        0.5       0.48      0.422\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      9.12G    0.01536    0.03566  6.958e-05        453        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.437      0.474      0.466      0.374\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      9.12G    0.01521    0.03584  6.812e-05        416        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.454      0.487      0.485      0.412\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      9.12G    0.01528    0.03459   6.58e-05        485        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476        0.5      0.483      0.425\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      9.12G    0.01465    0.03467  6.195e-05        411        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476        0.5      0.477      0.421\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      9.12G    0.01431    0.03461  6.186e-05        435        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476        0.5      0.491      0.425\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      9.12G    0.01349    0.03287  6.074e-05        402        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.473        0.5      0.485       0.42\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      9.12G    0.01326    0.03252   5.96e-05        373        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476        0.5      0.486      0.421\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      9.12G    0.01281    0.03186  5.858e-05        453        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476        0.5      0.489      0.418\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      9.12G    0.01278    0.03249  5.706e-05        410        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476        0.5      0.489      0.424\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      9.12G    0.01156    0.02975  5.389e-05        371        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476        0.5      0.488      0.427\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      9.12G     0.0117    0.03004  5.269e-05        413        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476        0.5      0.489      0.421\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      9.12G    0.01102    0.02921   5.18e-05        458        736: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476        0.5      0.487      0.427\n",
      "\n",
      "50 epochs completed in 0.689 hours.\n",
      "Optimizer stripped from runs/train/exp49/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/exp49/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/exp49/weights/best.pt...\n",
      "Fusing layers... \n",
      "Model summary: 157 layers, 7053277 parameters, 0 gradients, 15.9 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         21        318      0.476        0.5      0.488      0.426\n",
      "                   dog         21         15          0          0          0          0\n",
      "           square slot         21        303      0.953          1      0.977      0.853\n",
      "Results saved to \u001b[1mruns/train/exp49\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!python train.py --img 720 --batch 32 --epochs 50 --data dataset.yaml --weights yolov5s.pt --workers 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/cps/Documents/slot-detection/Slot-Detection\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://github.com/ultralytics/yolov5/zipball/master\" to /home/cps/.cache/torch/hub/master.zip\n",
      "YOLOv5 🚀 2023-8-8 Python-3.8.10 torch-2.0.1+cu117 CUDA:0 (NVIDIA TITAN RTX, 24212MiB)\n",
      "\n",
      "Fusing layers... \n",
      "Model summary: 157 layers, 7053277 parameters, 0 gradients, 15.9 GFLOPs\n",
      "Adding AutoShape... \n"
     ]
    }
   ],
   "source": [
    "#latest model = 49\n",
    "\n",
    "model = torch.hub.load(\"ultralytics/yolov5\", \"custom\", path=\"yolov5/runs/train/exp49/weights/last.pt\",force_reload=True)\n",
    "# model.conf = 0.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = os.path.join(\"images\",\"train\",\"images\",\"100.jpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = os.path.join(\"7.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = model(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "plt.imshow(np.squeeze(results.render()))\n",
    "plt.show()\n",
    "cv2.imwrite(\"result_img.jpeg\",np.squeeze(results.render()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_output(img_name):\n",
    "    img = os.path.join(\"images\",\"phone_images\", \"eval\",img_name)\n",
    "    results = model(img)\n",
    "    print(results)\n",
    "    # %matplotlib inline \n",
    "    # plt.imshow(np.squeeze(results.render()))\n",
    "    output_path = os.path.join(\"images\", \"phone_images\", \"output\", img_name )\n",
    "    cv2.imwrite(output_path, np.squeeze(results.render()))\n",
    "    # plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fnmatch\n",
    "\n",
    "def list_jpg_files(directory):\n",
    "    jpg_files = []\n",
    "    for root, dirnames, filenames in os.walk(directory):\n",
    "        for filename in fnmatch.filter(filenames, '*.jpeg'):\n",
    "            jpg_files.append(os.path.join(root, filename))\n",
    "    return jpg_files\n",
    "\n",
    "# Example usage\n",
    "directory = 'images/phone_images/eval'\n",
    "jpg_files = list_jpg_files(directory)\n",
    "for file in jpg_files:\n",
    "    filename = file.split('/')[-1]\n",
    "    print(filename)\n",
    "    get_output(filename)\n",
    "# get_output(\"1.jpeg\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yolo Realtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(1)\n",
    "while cap.isOpened():\n",
    "    ret, frame = cap.read()\n",
    "    results = model(frame)\n",
    "    cv2.imshow(\"YOLO\",np.squeeze(results.render()))\n",
    "\n",
    "    if cv2.waitKey(10) & 0XFF == ord(\"q\"):\n",
    "        break\n",
    "cap.release()\n",
    "cv2.destroyAllWindows() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "%matplotlib inline \n",
    "plt.imshow(rotated_image)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb Cell 40\u001b[0m in \u001b[0;36m<cell line: 26>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#X54sZmlsZQ%3D%3D?line=37'>38</a>\u001b[0m     results \u001b[39m=\u001b[39m model(frame)\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#X54sZmlsZQ%3D%3D?line=38'>39</a>\u001b[0m     cv2\u001b[39m.\u001b[39mimshow(\u001b[39m\"\u001b[39m\u001b[39mSquare Slot Detection\u001b[39m\u001b[39m\"\u001b[39m, np\u001b[39m.\u001b[39msqueeze(results\u001b[39m.\u001b[39mrender()))\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#X54sZmlsZQ%3D%3D?line=40'>41</a>\u001b[0m \u001b[39mif\u001b[39;00m cv2\u001b[39m.\u001b[39;49mwaitKey(\u001b[39m10\u001b[39;49m) \u001b[39m&\u001b[39m \u001b[39m0XFF\u001b[39m \u001b[39m==\u001b[39m \u001b[39mord\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mq\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/cps/Documents/slot-detection/Slot-Detection/slot-detection.ipynb#X54sZmlsZQ%3D%3D?line=41'>42</a>\u001b[0m     \u001b[39mbreak\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import depthai as dai\n",
    "\n",
    "pipeline = dai.Pipeline()\n",
    "\n",
    "camRgb = pipeline.create(dai.node.ColorCamera)\n",
    "# camRgb.setBoardSocket(dai.CameraBoSGDardSocket.CAM_A)\n",
    "camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)\n",
    "\n",
    "xoutRgb = pipeline.create(dai.node.XLinkOut)\n",
    "xoutRgb.setStreamName(\"rgb\")\n",
    "camRgb.video.link(xoutRgb.input)\n",
    "\n",
    "xin = pipeline.create(dai.node.XLinkIn)\n",
    "xin.setStreamName(\"control\")\n",
    "xin.out.link(camRgb.inputControl)\n",
    "\n",
    "videoEnc = pipeline.create(dai.node.VideoEncoder)\n",
    "videoEnc.setDefaultProfilePreset(1, dai.VideoEncoderProperties.Profile.MJPEG)\n",
    "camRgb.still.link(videoEnc.input)\n",
    "\n",
    "xoutStill = pipeline.create(dai.node.XLinkOut)\n",
    "xoutStill.setStreamName(\"still\")\n",
    "videoEnc.bitstream.link(xoutStill.input)\n",
    "\n",
    "with dai.Device(pipeline) as device:\n",
    "\n",
    "    qRgb = device.getOutputQueue(name=\"rgb\", maxSize=30, blocking=False)\n",
    "    qStill = device.getOutputQueue(name=\"still\", maxSize=30, blocking=True)\n",
    "    qControl = device.getInputQueue(name=\"control\")\n",
    "\n",
    "    while True:\n",
    "        inRgb = qRgb.tryGet() \n",
    "        if inRgb is not None:\n",
    "            frame = inRgb.getCvFrame()\n",
    "            frame = cv2.pyrDown(frame)\n",
    "            frame = cv2.pyrDown(frame)\n",
    "            results = model(frame)\n",
    "            cv2.imshow(\"Square Slot Detection\", np.squeeze(results.render()))\n",
    "\n",
    "        if cv2.waitKey(10) & 0XFF == ord('q'):\n",
    "            break\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "steve",
   "language": "python",
   "name": "steve"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}