make_label.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9203f649-91e8-448c-b36c-0c18c845e9ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "643f289a-5f85-4600-9397-6f5b6c57c84b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4039829a-b02b-48ec-a2cf-00a3408e4257",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00022e1a.jpg</td>\n",
       "      <td>w_e15442c</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000466c4.jpg</td>\n",
       "      <td>w_1287fbc</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>w_da2efe0</td>\n",
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       "      <th>3</th>\n",
       "      <td>001296d5.jpg</td>\n",
       "      <td>w_19e5482</td>\n",
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       "      <th>9845</th>\n",
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       "      <th>9846</th>\n",
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       "      <td>w_b067417</td>\n",
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       "      <th>9847</th>\n",
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       "      <th>9848</th>\n",
       "      <td>fff04277.jpg</td>\n",
       "      <td>w_2dcbf82</td>\n",
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       "      <th>9849</th>\n",
       "      <td>fffd4260.jpg</td>\n",
       "      <td>w_b9bfd4e</td>\n",
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       "  </tbody>\n",
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       "<p>9850 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Image         Id\n",
       "0     00022e1a.jpg  w_e15442c\n",
       "1     000466c4.jpg  w_1287fbc\n",
       "2     00087b01.jpg  w_da2efe0\n",
       "3     001296d5.jpg  w_19e5482\n",
       "4     0014cfdf.jpg  w_f22f3e3\n",
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       "9847  ffecec63.jpg  w_8b56cb1\n",
       "9848  fff04277.jpg  w_2dcbf82\n",
       "9849  fffd4260.jpg  w_b9bfd4e\n",
       "\n",
       "[9850 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4b1a66b1-9cd1-4225-a18c-2e7119c23d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1200/1200 [00:04<00:00, 289.68it/s]\n"
     ]
    }
   ],
   "source": [
    "f = open('cropping.txt', 'rt').read()\n",
    "line_list = f.split('\\n')\n",
    "bbox_dic = {}\n",
    "for line in tqdm(line_list, total=len(line_list)):\n",
    "    id_points = line.split(',')\n",
    "    id, points = id_points[0], np.array(id_points[1:])\n",
    "    img = cv2.imread('dataset/images/train/' + id)\n",
    "    h, w = img.shape[0], img.shape[1]\n",
    "    points = (points.reshape((-1, 2))).astype(int)\n",
    "    if points.shape[0] != 0:\n",
    "        x_min, x_max = np.min(points[:, 0]), np.max(points[:, 0])\n",
    "        y_min, y_max = np.min(points[:, 1]), np.max(points[:, 1])\n",
    "        bbox = [[(x_min + x_max) / 2 / w, (y_min + y_max) / 2 / h, (x_max - x_min) / w, (y_max - y_min) / h]]\n",
    "    # train.loc[train['Image'] == id, 'bbox'] = bbox\n",
    "        bbox_dic[id] = bbox\n",
    "# train['bbox'] = train['Image'].map(bbox_dic)\n",
    "bbox_dic = pd.Series(bbox_dic)\n",
    "train['bbox'] = train['Image'].map(bbox_dic)\n",
    "# train['Image'].map(bbox_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d6cc860-6610-4093-a6a0-69a716e26cdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image</th>\n",
       "      <th>Id</th>\n",
       "      <th>bbox</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000466c4.jpg</td>\n",
       "      <td>w_1287fbc</td>\n",
       "      <td>[[0.3861904761904762, 0.525, 0.328571428571428...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00087b01.jpg</td>\n",
       "      <td>w_da2efe0</td>\n",
       "      <td>[[0.4957142857142857, 0.5081521739130435, 0.98...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0014cfdf.jpg</td>\n",
       "      <td>w_f22f3e3</td>\n",
       "      <td>[[0.49357142857142855, 0.5314070351758794, 0.8...</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0025e8c2.jpg</td>\n",
       "      <td>w_8b1ca89</td>\n",
       "      <td>[[0.496, 0.47699386503067487, 0.756, 0.3006134...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0031c258.jpg</td>\n",
       "      <td>new_whale</td>\n",
       "      <td>[[0.49476190476190474, 0.4934210526315789, 0.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9806</th>\n",
       "      <td>feb9ded0.jpg</td>\n",
       "      <td>w_2a939eb</td>\n",
       "      <td>[[0.49047619047619045, 0.46936542669584247, 0....</td>\n",
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       "    <tr>\n",
       "      <th>9813</th>\n",
       "      <td>ff0d2705.jpg</td>\n",
       "      <td>w_4e52a49</td>\n",
       "      <td>[[0.49142857142857144, 0.5216666666666666, 0.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9819</th>\n",
       "      <td>ff2c20cc.jpg</td>\n",
       "      <td>w_f6fb689</td>\n",
       "      <td>[[0.4719047619047619, 0.5681818181818182, 0.91...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9832</th>\n",
       "      <td>ff8b2ad0.jpg</td>\n",
       "      <td>w_0f20cbc</td>\n",
       "      <td>[[0.4942857142857143, 0.49828178694158076, 0.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9839</th>\n",
       "      <td>ffbca206.jpg</td>\n",
       "      <td>w_73b26b7</td>\n",
       "      <td>[[0.49523809523809526, 0.49496221662468515, 0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Image         Id  \\\n",
       "1     000466c4.jpg  w_1287fbc   \n",
       "2     00087b01.jpg  w_da2efe0   \n",
       "4     0014cfdf.jpg  w_f22f3e3   \n",
       "5     0025e8c2.jpg  w_8b1ca89   \n",
       "7     0031c258.jpg  new_whale   \n",
       "...            ...        ...   \n",
       "9806  feb9ded0.jpg  w_2a939eb   \n",
       "9813  ff0d2705.jpg  w_4e52a49   \n",
       "9819  ff2c20cc.jpg  w_f6fb689   \n",
       "9832  ff8b2ad0.jpg  w_0f20cbc   \n",
       "9839  ffbca206.jpg  w_73b26b7   \n",
       "\n",
       "                                                   bbox  \n",
       "1     [[0.3861904761904762, 0.525, 0.328571428571428...  \n",
       "2     [[0.4957142857142857, 0.5081521739130435, 0.98...  \n",
       "4     [[0.49357142857142855, 0.5314070351758794, 0.8...  \n",
       "5     [[0.496, 0.47699386503067487, 0.756, 0.3006134...  \n",
       "7     [[0.49476190476190474, 0.4934210526315789, 0.9...  \n",
       "...                                                 ...  \n",
       "9806  [[0.49047619047619045, 0.46936542669584247, 0....  \n",
       "9813  [[0.49142857142857144, 0.5216666666666666, 0.9...  \n",
       "9819  [[0.4719047619047619, 0.5681818181818182, 0.91...  \n",
       "9832  [[0.4942857142857143, 0.49828178694158076, 0.9...  \n",
       "9839  [[0.49523809523809526, 0.49496221662468515, 0....  \n",
       "\n",
       "[1200 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train.loc[~train['bbox'].isna(), :]\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a77dc14-2727-4a89-992c-6995ca0f9da7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1200 [00:00<?, ?it/s]/tmp/ipykernel_5841/123064445.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train.loc[train['Image'] == img_name, 'hight'] = img.shape[0]\n",
      "/tmp/ipykernel_5841/123064445.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train.loc[train['Image'] == img_name, 'width'] = img.shape[1]\n",
      "100%|██████████| 1200/1200 [00:04<00:00, 260.84it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image</th>\n",
       "      <th>Id</th>\n",
       "      <th>bbox</th>\n",
       "      <th>hight</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000466c4.jpg</td>\n",
       "      <td>w_1287fbc</td>\n",
       "      <td>[[0.3861904761904762, 0.525, 0.328571428571428...</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00087b01.jpg</td>\n",
       "      <td>w_da2efe0</td>\n",
       "      <td>[[0.4957142857142857, 0.5081521739130435, 0.98...</td>\n",
       "      <td>368.0</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0014cfdf.jpg</td>\n",
       "      <td>w_f22f3e3</td>\n",
       "      <td>[[0.49357142857142855, 0.5314070351758794, 0.8...</td>\n",
       "      <td>398.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0025e8c2.jpg</td>\n",
       "      <td>w_8b1ca89</td>\n",
       "      <td>[[0.496, 0.47699386503067487, 0.756, 0.3006134...</td>\n",
       "      <td>652.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0031c258.jpg</td>\n",
       "      <td>new_whale</td>\n",
       "      <td>[[0.49476190476190474, 0.4934210526315789, 0.9...</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9806</th>\n",
       "      <td>feb9ded0.jpg</td>\n",
       "      <td>w_2a939eb</td>\n",
       "      <td>[[0.49047619047619045, 0.46936542669584247, 0....</td>\n",
       "      <td>457.0</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9813</th>\n",
       "      <td>ff0d2705.jpg</td>\n",
       "      <td>w_4e52a49</td>\n",
       "      <td>[[0.49142857142857144, 0.5216666666666666, 0.9...</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9819</th>\n",
       "      <td>ff2c20cc.jpg</td>\n",
       "      <td>w_f6fb689</td>\n",
       "      <td>[[0.4719047619047619, 0.5681818181818182, 0.91...</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9832</th>\n",
       "      <td>ff8b2ad0.jpg</td>\n",
       "      <td>w_0f20cbc</td>\n",
       "      <td>[[0.4942857142857143, 0.49828178694158076, 0.9...</td>\n",
       "      <td>291.0</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9839</th>\n",
       "      <td>ffbca206.jpg</td>\n",
       "      <td>w_73b26b7</td>\n",
       "      <td>[[0.49523809523809526, 0.49496221662468515, 0....</td>\n",
       "      <td>397.0</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Image         Id  \\\n",
       "1     000466c4.jpg  w_1287fbc   \n",
       "2     00087b01.jpg  w_da2efe0   \n",
       "4     0014cfdf.jpg  w_f22f3e3   \n",
       "5     0025e8c2.jpg  w_8b1ca89   \n",
       "7     0031c258.jpg  new_whale   \n",
       "...            ...        ...   \n",
       "9806  feb9ded0.jpg  w_2a939eb   \n",
       "9813  ff0d2705.jpg  w_4e52a49   \n",
       "9819  ff2c20cc.jpg  w_f6fb689   \n",
       "9832  ff8b2ad0.jpg  w_0f20cbc   \n",
       "9839  ffbca206.jpg  w_73b26b7   \n",
       "\n",
       "                                                   bbox  hight   width  \n",
       "1     [[0.3861904761904762, 0.525, 0.328571428571428...  700.0  1050.0  \n",
       "2     [[0.4957142857142857, 0.5081521739130435, 0.98...  368.0  1050.0  \n",
       "4     [[0.49357142857142855, 0.5314070351758794, 0.8...  398.0   700.0  \n",
       "5     [[0.496, 0.47699386503067487, 0.756, 0.3006134...  652.0  1000.0  \n",
       "7     [[0.49476190476190474, 0.4934210526315789, 0.9...  456.0  1050.0  \n",
       "...                                                 ...    ...     ...  \n",
       "9806  [[0.49047619047619045, 0.46936542669584247, 0....  457.0  1050.0  \n",
       "9813  [[0.49142857142857144, 0.5216666666666666, 0.9...  600.0  1050.0  \n",
       "9819  [[0.4719047619047619, 0.5681818181818182, 0.91...  506.0  1050.0  \n",
       "9832  [[0.4942857142857143, 0.49828178694158076, 0.9...  291.0  1050.0  \n",
       "9839  [[0.49523809523809526, 0.49496221662468515, 0....  397.0  1050.0  \n",
       "\n",
       "[1200 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for img_name in tqdm(train['Image']):\n",
    "    img = cv2.imread('dataset/images/train/' + img_name)\n",
    "    train.loc[train['Image'] == img_name, 'hight'] = img.shape[0]\n",
    "    train.loc[train['Image'] == img_name, 'width'] = img.shape[1]\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f3b04419-10f1-40ad-a82d-ab41e7a33918",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for idx in tqdm(train.index, total = 1200):\n",
    "#     label_path = 'dataset/labels/train/' + train.loc[idx, 'Image'][:-3] + 'txt'\n",
    "#     # print(label_path)\n",
    "#     # print(label_path)\n",
    "#     # print(str(train.loc[idx, 'bbox'])[2: -2].replace(',', ''))\n",
    "#     with open(label_path, 'w') as f:\n",
    "#         f.write('0 ' + str(train.loc[idx, 'bbox'])[2: -2].replace(',', ''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da26562b-c626-4dd4-812c-28ea14568552",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.49476190476190474, 0.4934210526315789, 0.9857142857142858, 0.9254385964912281]\n",
      "(456, 1050, 3)\n",
      "[1, 13] [1037, 436]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f60f511e160>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_name = '0031c258'\n",
    "image_path = 'dataset/images/train/' + image_name + '.jpg'\n",
    "img = cv2.imread(image_path)\n",
    "label_path = 'dataset/labels/train/' + image_name + '.txt'\n",
    "with open(label_path, 'rt') as f:\n",
    "    \n",
    "    annot = f.read()\n",
    "    annot = np.array(annot.split(' ')[1:])\n",
    "    annot = [float(a) for a in annot]\n",
    "    print(annot)\n",
    "pt1 = [int((annot[0] - annot[2] / 2) * img.shape[1]), int((annot[1] - annot[3] / 2) * img.shape[0])]\n",
    "pt2 = [int((annot[0] + annot[2] / 2) * img.shape[1]), int((annot[1] + annot[3] / 2) * img.shape[0])]\n",
    "print(img.shape)\n",
    "print(pt1, pt2)\n",
    "image_path = 'dataset/images/train/' + image_name + '.jpg'\n",
    "img = cv2.imread(image_path)\n",
    "img = cv2.rectangle(img, pt1, pt2, [255, 0, 0], 2)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7de7b160-6eff-4e53-aeea-3e9fe47c02ba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5/yolov5x.pt, cfg=, data=yolov5/whale_config/whale_data.yaml, hyp=yolov5/whale_config/whale_hyp.yaml, epochs=300, batch_size=24, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=0, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/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",
      "Command 'git fetch origin' timed out after 5 seconds\n",
      "YOLOv5 🚀 v7.0-215-ga6659d0 Python-3.8.10 torch-1.10.0+cu113 CUDA:0 (NVIDIA GeForce RTX 4090, 24217MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, 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=30.0, translate=0.1, scale=0.8, shear=10.0, perspective=0.0, flipud=0.5, fliplr=0.5, mosaic=0.75, 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 yolov5/runs/train', view at http://localhost:6006/\n",
      "Overriding model.yaml nc=80 with nc=1\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      8800  models.common.Conv                      [3, 80, 6, 2, 2]              \n",
      "  1                -1  1    115520  models.common.Conv                      [80, 160, 3, 2]               \n",
      "  2                -1  4    309120  models.common.C3                        [160, 160, 4]                 \n",
      "  3                -1  1    461440  models.common.Conv                      [160, 320, 3, 2]              \n",
      "  4                -1  8   2259200  models.common.C3                        [320, 320, 8]                 \n",
      "  5                -1  1   1844480  models.common.Conv                      [320, 640, 3, 2]              \n",
      "  6                -1 12  13125120  models.common.C3                        [640, 640, 12]                \n",
      "  7                -1  1   7375360  models.common.Conv                      [640, 1280, 3, 2]             \n",
      "  8                -1  4  19676160  models.common.C3                        [1280, 1280, 4]               \n",
      "  9                -1  1   4099840  models.common.SPPF                      [1280, 1280, 5]               \n",
      " 10                -1  1    820480  models.common.Conv                      [1280, 640, 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  4   5332480  models.common.C3                        [1280, 640, 4, False]         \n",
      " 14                -1  1    205440  models.common.Conv                      [640, 320, 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  4   1335040  models.common.C3                        [640, 320, 4, False]          \n",
      " 18                -1  1    922240  models.common.Conv                      [320, 320, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  4   4922880  models.common.C3                        [640, 640, 4, False]          \n",
      " 21                -1  1   3687680  models.common.Conv                      [640, 640, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  4  19676160  models.common.C3                        [1280, 1280, 4, False]        \n",
      " 24      [17, 20, 23]  1     40374  models.yolo.Detect                      [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [320, 640, 1280]]\n",
      "Model summary: 445 layers, 86217814 parameters, 86217814 gradients, 204.6 GFLOPs\n",
      "\n",
      "Transferred 739/745 items from yolov5/yolov5x.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 123 weight(decay=0.0), 126 weight(decay=0.0005625000000000001), 126 bias\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/whale_tail/dataset/labels/train.cache... 1200 i\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/whale_tail/dataset/labels/train.cache... 1200 ima\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m1.82 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to yolov5/runs/train/exp6/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1myolov5/runs/train/exp6\u001b[0m\n",
      "Starting training for 300 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      0/299      21.2G    0.05146   0.009779          0          9        640:  fatal: unable to access 'https://github.com/ultralytics/yolov5.git/': GnuTLS recv error (-110): The TLS connection was non-properly terminated.\n",
      "      0/299      21.2G    0.04647   0.008234          0          5        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200    0.00302      0.993      0.122     0.0523\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      1/299      19.4G     0.0322   0.004145          0          6        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200     0.0545      0.978      0.133     0.0707\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      2/299      19.4G    0.03642   0.004055          0          4        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200   0.000986      0.927     0.0387     0.0115\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      3/299      19.4G    0.04308   0.004005          0          1        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200   0.000464      0.823    0.00403    0.00073\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      4/299      19.5G    0.04127   0.003948          0          2        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200    0.00188      0.958     0.0661     0.0241\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      5/299      19.5G    0.03751   0.004046          0          4        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200     0.0248      0.811     0.0527     0.0131\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      6/299      19.5G    0.03395   0.003976          0          4        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.119     0.0817      0.103     0.0473\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      7/299      19.5G    0.03336   0.003952          0          3        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200     0.0723      0.127     0.0636     0.0219\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      8/299      19.5G     0.0334    0.00389          0          6        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.134      0.152      0.116     0.0495\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      9/299      19.5G    0.03136   0.003888          0          1        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.103      0.318     0.0916     0.0341\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     10/299      19.5G    0.02967   0.003738          0          6        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200       0.11      0.871      0.115     0.0489\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     11/299      19.5G     0.0291   0.003765          0          1        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.095      0.147     0.0915      0.033\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     12/299      19.5G    0.02742   0.003832          0          3        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.129      0.492      0.133     0.0669\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     13/299      19.5G    0.02849   0.003842          0          2        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200       0.12      0.136       0.11     0.0499\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     14/299      19.5G    0.02582   0.003647          0          6        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.131      0.466      0.139     0.0789\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     15/299      19.5G    0.02657   0.003731          0          4        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.123      0.357      0.121     0.0535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     16/299      19.5G    0.02665   0.003769          0          6        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.135      0.217      0.115      0.052\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     17/299      19.5G    0.02536    0.00371          0          4        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.126      0.512      0.131      0.071\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     18/299      19.5G     0.0243   0.003722          0          4        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.125      0.347      0.123     0.0752\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     19/299      19.5G    0.02425   0.003752          0          4        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.134      0.511      0.134     0.0652\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     20/299      19.5G    0.02431   0.003757          0          6        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.125      0.682      0.135     0.0711\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     21/299      19.5G    0.02431   0.003793          0          2        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.137      0.375      0.133     0.0849\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     22/299      19.5G     0.0256   0.003761          0          2        640: 1\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       9850       1200      0.132      0.426      0.135     0.0671\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "     23/299      19.5G     0.0253   0.003823          0         11        640:  "
     ]
    }
   ],
   "source": [
    "! python3 'yolov5/train.py'  --data 'yolov5/whale_config/whale_data.yaml' --device '0' --weight 'yolov5/yolov5x.pt' --epochs 300 --batch-size 24 --hyp 'yolov5/whale_config/whale_hyp.yaml' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2faf87b-2bf4-4e76-a849-f8a41a1170fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72e74903.jpg\n",
      "72e74903\n",
      "[0.489648033126294, 0.3695652173913043, 0.9585921325051759, 0.5362318840579711]\n",
      "(207, 483, 3)\n",
      "[5, 20] [468, 132]\n",
      "dataset/images/train/72e74903.jpg\n",
      "4416\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from random import choice\n",
    "idx = choice(train['Image'].index)\n",
    "# idx = 1\n",
    "image_name = train['Image'][idx]\n",
    "print(image_name)\n",
    "image_name = image_name.split('.')[0]\n",
    "print(image_name)\n",
    "image_path = 'dataset/images/train/' + image_name + '.jpg'\n",
    "img = cv2.imread(image_path)\n",
    "label_path = 'dataset/labels/train/' + image_name + '.txt'\n",
    "with open(label_path, 'rt') as f:\n",
    "    annot = f.read()\n",
    "    annot = np.array(annot.split(' ')[1:])\n",
    "    annot = [float(a) for a in annot]\n",
    "    print(annot)\n",
    "pt1 = [int((annot[0] - annot[2] / 2) * img.shape[1]), int((annot[1] - annot[3] / 2) * img.shape[0])]\n",
    "pt2 = [int((annot[0] + annot[2] / 2) * img.shape[1]), int((annot[1] + annot[3] / 2) * img.shape[0])]\n",
    "print(img.shape)\n",
    "print(pt1, pt2)\n",
    "image_path = 'dataset/images/train/' + image_name + '.jpg'\n",
    "print(image_path)\n",
    "img = cv2.imread(image_path)\n",
    "img = cv2.rectangle(img, pt1, pt2, [255, 0, 0], 2)\n",
    "plt.imshow(img)\n",
    "print(idx)"
   ]
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}