image_Augmentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import os\n",
    "import json\n",
    "import cv2\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_upside_down_flipping(image, bbox):\n",
    "    \n",
    "    flipped_image = cv2.flip(image, 0)\n",
    "    x_min, y_min, width, height = bbox\n",
    "    \n",
    "    y_min = int((640 - y_min - height) / 2)\n",
    "    x_min = int(x_min / 2)\n",
    "\n",
    "    width = int(width / 2)\n",
    "    height = int(height / 2)\n",
    "\n",
    "    re_bbox = [x_min, y_min, width, height]\n",
    "    return flipped_image, re_bbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_leftright_down_flipping(image, bbox):\n",
    "    \n",
    "    flipped_image = cv2.flip(image, 1)\n",
    "    x_min, y_min, width, height = bbox\n",
    "    \n",
    "    x_min = int((640 - x_min - width) / 2)\n",
    "    y_min = int (y_min / 2)\n",
    "    width = int(width / 2)\n",
    "    height = int(height / 2)\n",
    "    \n",
    "    re_bbox = [x_min, y_min, width, height]\n",
    "    return flipped_image, re_bbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def both_down_flipping(image, bbox):\n",
    "    \n",
    "    image = cv2.flip(image, 0)\n",
    "    flipped_image = cv2.flip(image, 1)\n",
    "    x_min, y_min, width, height = bbox\n",
    "    \n",
    "    y_min = int((640 - y_min - height) / 2)\n",
    "    x_min = int((640 - x_min - width) / 2 )\n",
    "    width = int(width / 2)\n",
    "    height = int(height / 2)\n",
    "    \n",
    "    re_bbox = [x_min, y_min, width, height]\n",
    "    return flipped_image, re_bbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_augmentation(input_image_path, output_image_path, image_annotation_path, output_image_annotation_path):\n",
    "\n",
    "    with open(image_annotation_path, 'r') as file:\n",
    "        content = file.read()\n",
    "\n",
    "    data = json.loads(content)\n",
    "    filenames = os.listdir(input_image_path)\n",
    "    output_folder = output_image_path\n",
    "    filenames.sort()\n",
    "    annotations = []\n",
    "    number = 0\n",
    "    origin_image_num = 0\n",
    "    for filename in filenames:\n",
    "        print(filename)\n",
    "        image = cv2.imread(f\"{input_image_path}/{filename}\")\n",
    "        image = cv2.resize(image, (320, 320))\n",
    "        \n",
    "        x_min, y_min, width, height = data[origin_image_num][\"bbox\"]\n",
    "        y_min = int(y_min / 2)\n",
    "        x_min = int(x_min / 2)\n",
    "        width = int(width / 2)\n",
    "        height = int(height / 2)\n",
    "        re_bbox = [x_min, y_min, width, height]\n",
    "        annotation = {\"id\": number, \"image_id\": number, \"bbox\": re_bbox, \"area\": int(data[origin_image_num][\"area\"] / 4), \"category_id\": data[origin_image_num][\"category_id\"]}\n",
    "        annotations.append(annotation)\n",
    "        output_path = f\"{output_folder}/{number}.jpg\"\n",
    "        cv2.imwrite(output_path, image)\n",
    "        number += 1\n",
    "        print(f\"image {number}.jpg is already process\")\n",
    "\n",
    "\n",
    "        flipped_image, bbox = image_upside_down_flipping(image, data[origin_image_num][\"bbox\"])\n",
    "        annotation = {\"id\": number, \"image_id\": number, \"bbox\": bbox, \"area\": int(data[origin_image_num][\"area\"] / 4), \"category_id\": data[origin_image_num][\"category_id\"]}\n",
    "        annotations.append(annotation)\n",
    "        output_path = f\"{output_folder}/{number}.jpg\"\n",
    "        cv2.imwrite(output_path, flipped_image)\n",
    "        number += 1\n",
    "        print(f\"image {number}.jpg is already process\")\n",
    "\n",
    "        \n",
    "        flipped_image, bbox = image_leftright_down_flipping(image, data[origin_image_num][\"bbox\"])\n",
    "        annotation = {\"id\": number, \"image_id\": number, \"bbox\": bbox, \"area\": int(data[origin_image_num][\"area\"] / 4), \"category_id\": data[origin_image_num][\"category_id\"]}\n",
    "        annotations.append(annotation)\n",
    "        output_path = f\"{output_folder}/{number}.jpg\"\n",
    "        cv2.imwrite(output_path, flipped_image)\n",
    "        number += 1\n",
    "        print(f\"image {number}.jpg is already process\")\n",
    "\n",
    "        \n",
    "        flipped_image, bbox = both_down_flipping(image, data[origin_image_num][\"bbox\"])\n",
    "        annotation = {\"id\": number, \"image_id\": number, \"bbox\": bbox, \"area\": int(data[origin_image_num][\"area\"] / 4), \"category_id\": data[origin_image_num][\"category_id\"]}\n",
    "        annotations.append(annotation)\n",
    "        output_path = f\"{output_folder}/{number}.jpg\"\n",
    "        cv2.imwrite(output_path, flipped_image)\n",
    "        number += 1\n",
    "        print(f\"image {number}.jpg is already process\")\n",
    "        \n",
    "        \n",
    "        origin_image_num += 1\n",
    "        \n",
    "    with open(output_image_annotation_path, \"w\") as file:\n",
    "        json.dump(annotations, file)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "\n",
    "image_augmentation(\"train/train/\", \"train/augmentation/\", \"train_annotations\", \"train_annotations_augmentation\")\n",
    "\n",
    "image_augmentation(\"train/preprocess/\", \"train/pre_augmentation/\", \"train_annotations\", \"train_annotations_augmentation\")\n",
    "\n",
    "image_augmentation(\"valid/valid/\", \"valid/augmentation/\", \"valid_annotations\", \"valid_annotations_augmentation\")\n",
    "\n",
    "image_augmentation(\"valid/preprocess/\", \"valid/pre_augmentation/\", \"valid_annotations\", \"valid_annotations_augmentation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['image1', 'image3', 'image2']\n",
      "['image1', 'image2', 'image3']\n"
     ]
    }
   ],
   "source": [
    "filename = [\"image1\", \"image3\", \"image2\"]\n",
    "print(filename)\n",
    "filename.sort()\n",
    "print(filename)"
   ]
  }
 ],
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