diff --git "a/Disease_Detection/for_all_classe.ipynb" "b/Disease_Detection/for_all_classe.ipynb" new file mode 100644--- /dev/null +++ "b/Disease_Detection/for_all_classe.ipynb" @@ -0,0 +1,1292 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "76fe7874-7381-4b1b-ac6d-89747001b561", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: inference-sdk in c:\\users\\sriram\\anaconda3\\lib\\site-packages (0.9.17)\n", + "Requirement already satisfied: requests>=2.0.0 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from inference-sdk) (2.31.0)\n", + "Requirement already satisfied: dataclasses-json>=0.6.0 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from inference-sdk) (0.6.7)\n", + "Requirement already satisfied: opencv-python>=4.8.0.0 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from inference-sdk) (4.10.0.84)\n", + "Requirement already 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Cause: Error connecting to Docker daemon. Is docker installed and running? See https://www.docker.com/get-started/ for installation instructions.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SRIRAM\\anaconda3\\Lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated and will be removed in a future release\n", + " \"class\": algorithms.Blowfish,\n" + ] + } + ], + "source": [ + "!pip install inference-cli && inference server start" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e059518c-7cd2-4ec3-a18e-4f8610333cf0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: opencv-python in c:\\users\\sriram\\anaconda3\\lib\\site-packages (4.10.0.84)\n", + "Requirement already satisfied: numpy>=1.21.2 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from opencv-python) (1.26.4)\n" + ] + } + ], + "source": [ + "!pip install opencv-python" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e34a866f-695a-4161-8010-44fc1a62759b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: opencv-python-headless in c:\\users\\sriram\\anaconda3\\lib\\site-packages (4.10.0.84)\n", + "Requirement already satisfied: numpy>=1.21.2 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from opencv-python-headless) (1.26.4)\n" + ] + } + ], + "source": [ + "!pip install opencv-python-headless" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "349632ea-a626-4152-9485-6653ad96aaa1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: opencv-python-headless in c:\\users\\sriram\\anaconda3\\lib\\site-packages (4.10.0.84)\n", + "Requirement already satisfied: numpy>=1.21.2 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from opencv-python-headless) (1.26.4)\n" + ] + } + ], + "source": [ + "!pip install opencv-python-headless" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ba247bf8-e415-4f46-8cb6-6c7581d404d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: opencv-contrib-python in c:\\users\\sriram\\anaconda3\\lib\\site-packages (4.10.0.84)\n", + "Requirement already satisfied: numpy>=1.21.2 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from opencv-contrib-python) (1.26.4)\n" + ] + } + ], + "source": [ + "!pip install opencv-contrib-python" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d691f42e-c0e6-4a67-afaa-c2172a943ad1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pillow in c:\\users\\sriram\\anaconda3\\lib\\site-packages (10.3.0)\n" + ] + } + ], + "source": [ + "!pip install pillow" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "e380f580-00cd-4a25-befe-62a8e7d60a43", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 3 Test image categories.\n", + "Loaded 3 Train image categories.\n" + ] + } + ], + "source": [ + "import os\n", + "import cv2\n", + "\n", + "def load_images_from_folder(folder):\n", + " images = []\n", + " for filename in os.listdir(folder):\n", + " if filename.endswith(('.png', '.jpg', '.jpeg')):\n", + " img_path = os.path.join(folder, filename)\n", + " #img = cv2.imread(img_path)\n", + " images.append(img_path)\n", + "\n", + " return images\n", + "\n", + "def load_images_test_dataset(test_folder):\n", + " test_images = [] # Initialize the list\n", + " covid_folder = os.path.join(test_folder, 'Covid')\n", + " normal_chestxray_folder = os.path.join(test_folder, 'Normal_chestxray')\n", + " pneumonia_folder = os.path.join(test_folder, 'Pneumonia')\n", + " \n", + " \n", + " test_images.append({\"category\": \"covid\", \"images\": load_images_from_folder(covid_folder)}) # Append images\n", + " test_images.append({\"category\": \"normal_chestray\", \"images\": load_images_from_folder(normal_chestxray_folder)}) # Append images\n", + " test_images.append({\"category\": \"pneumonia\", \"images\": load_images_from_folder(pneumonia_folder)}) # Append images\n", + " \n", + " return test_images\n", + "\n", + "def load_images_train_dataset(train_folder):\n", + " train_images = [] # Initialize the list\n", + " covid_folder = os.path.join(train_folder, 'Covid')\n", + " normal_chestxray_folder = os.path.join(train_folder, 'Normal_chestxray')\n", + " pneumonia_folder = os.path.join(train_folder, 'Pneumonia')\n", + " \n", + " train_images.append({\"category\": \"covid\", \"images\": load_images_from_folder(covid_folder)}) # Append images\n", + " train_images.append({\"category\": \"normal_chestray\", \"images\": load_images_from_folder(normal_chestxray_folder)}) # Append images\n", + " train_images.append({\"category\": \"pneumonia\", \"images\": load_images_from_folder(pneumonia_folder)}) # Append images\n", + " \n", + " return train_images\n", + "\n", + "def load_image_main_folder(main_folder):\n", + " test_folder = os.path.join(main_folder, 'test')\n", + " train_folder = os.path.join(main_folder, 'train')\n", + " \n", + " test_images = load_images_test_dataset(test_folder)\n", + " train_images = load_images_train_dataset(train_folder)\n", + " \n", + " return test_images, train_images\n", + "\n", + "# Example usage\n", + "main_folder = 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification'\n", + "test_images, train_images = load_image_main_folder(main_folder)\n", + "\n", + "print(f\"Loaded {len(test_images)} Test image categories.\")\n", + "print(f\"Loaded {len(train_images)} Train image categories.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "2d72287e-faf5-4057-b6b2-837aef3c52f0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded [{'category': 'covid', 'images': ['C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\0100.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\0102.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\0105.png', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\0106.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\0108.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\0111.jpg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\0112.jpg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\0113.jpg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\0115.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\0118.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\0119.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\0120.jpg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\094.png', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\096.png', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\098.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\auntminnie-2020_01_31_20_24_2322_2020_01_31_x-ray_coronavirus_US.jpg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\auntminnie-a-2020_01_28_23_51_6665_2020_01_28_Vietnam_coronavirus.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\auntminnie-b-2020_01_28_23_51_6665_2020_01_28_Vietnam_coronavirus.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\auntminnie-c-2020_01_28_23_51_6665_2020_01_28_Vietnam_coronavirus.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\auntminnie-d-2020_01_28_23_51_6665_2020_01_28_Vietnam_coronavirus.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\COVID-00003b.jpg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\COVID-00012.jpg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\COVID-00022.jpg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\COVID-00033.jpg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\COVID-00037.jpg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Covid\\\\radiopaedia-2019-novel-coronavirus-infected-pneumonia.jpg']}, {'category': 'normal_chestray', 'images': ['C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\0101.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\0102.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\0103.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\0105.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\0106.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\0107.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\0108.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\0109.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\0110.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image 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'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0266-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0268-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0269-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0270-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0272-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0273-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0274-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0275-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0276-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0277-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0278-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0279-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0280-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0282-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0283-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Normal_chestxray\\\\IM-0285-0001.jpeg']}, {'category': 'pneumonia', 'images': ['C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\test\\\\Pneumonia\\\\IM-0115-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image 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Classification\\\\train\\\\Normal_chestxray\\\\IM-0234-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Normal_chestxray\\\\IM-0235-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Normal_chestxray\\\\IM-0236-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Normal_chestxray\\\\IM-0237-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Normal_chestxray\\\\IM-0238-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Normal_chestxray\\\\IM-0239-0001.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Normal_chestxray\\\\IM-0240-0001.jpeg']}, {'category': 'pneumonia', 'images': ['C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person10_bacteria_43.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person11_bacteria_45.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person12_bacteria_46.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person12_bacteria_47.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person12_bacteria_48.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person13_bacteria_49.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person13_bacteria_50.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person14_bacteria_51.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person15_bacteria_52.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person16_bacteria_53.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person16_bacteria_54.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person16_bacteria_55.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person17_bacteria_56.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person18_bacteria_57.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person19_bacteria_58.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person19_bacteria_59.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person19_bacteria_60.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person19_bacteria_61.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person19_bacteria_62.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person19_bacteria_63.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person1_bacteria_1.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person1_bacteria_2.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person20_bacteria_64.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person20_bacteria_66.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person20_bacteria_67.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person20_bacteria_69.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person20_bacteria_70.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person21_bacteria_72.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person21_bacteria_73.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person22_bacteria_74.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person22_bacteria_76.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person22_bacteria_77.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_100.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_101.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_102.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_103.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_104.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_105.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_106.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_107.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_78.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_79.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_80.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_81.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_82.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_83.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_84.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_85.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_86.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_87.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_88.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_89.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_90.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_91.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_92.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_93.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_94.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_95.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_96.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_97.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_98.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person23_bacteria_99.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person24_bacteria_108.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person24_bacteria_109.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person24_bacteria_110.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person24_bacteria_111.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person24_bacteria_112.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person25_bacteria_113.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person25_bacteria_114.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person25_bacteria_115.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person25_bacteria_116.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person25_bacteria_117.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person25_bacteria_118.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person25_bacteria_119.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person25_bacteria_120.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person25_bacteria_121.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person26_bacteria_122.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person26_bacteria_123.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person26_bacteria_124.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person26_bacteria_126.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person26_bacteria_127.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person26_bacteria_128.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person26_bacteria_129.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person26_bacteria_130.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person26_bacteria_131.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person26_bacteria_132.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person26_bacteria_133.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person27_bacteria_135.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person27_bacteria_136.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person27_bacteria_137.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person27_bacteria_138.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person28_bacteria_139.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person28_bacteria_141.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person28_bacteria_142.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person28_bacteria_143.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person29_bacteria_144.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person2_bacteria_3.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person2_bacteria_4.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person30_bacteria_145.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person30_bacteria_146.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person30_bacteria_147.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person3_bacteria_10.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person3_bacteria_11.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person3_bacteria_12.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person3_bacteria_13.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person4_bacteria_14.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person5_bacteria_15.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person5_bacteria_16.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person5_bacteria_17.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person5_bacteria_19.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person6_bacteria_22.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person7_bacteria_24.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person7_bacteria_25.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person7_bacteria_28.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person7_bacteria_29.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person8_bacteria_37.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person9_bacteria_38.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person9_bacteria_39.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person9_bacteria_40.jpeg', 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\train\\\\Pneumonia\\\\person9_bacteria_41.jpeg']}] Train image categories.\n" + ] + } + ], + "source": [ + "print(f\"Loaded {test_images} Test image categories.\")\n", + "print(f\"Loaded {train_images} Train image categories.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "dd253634-c77e-4e6a-90f8-857c75b5a596", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: tensorflow in c:\\users\\sriram\\anaconda3\\lib\\site-packages (2.17.0)\n", + "Requirement already satisfied: tensorflow-intel==2.17.0 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from tensorflow) (2.17.0)\n", + "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (2.1.0)\n", + "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (1.6.3)\n", + "Requirement already satisfied: flatbuffers>=24.3.25 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (24.3.25)\n", + "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (0.6.0)\n", + "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (0.2.0)\n", + "Requirement already satisfied: h5py>=3.10.0 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (3.11.0)\n", + "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (18.1.1)\n", + "Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (0.3.1)\n", + "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from 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satisfied: keras-applications<=1.0.8,>=1.0.7 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from efficientnet) (1.0.8)\n", + "Requirement already satisfied: scikit-image in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from efficientnet) (0.23.2)\n", + "Requirement already satisfied: numpy>=1.9.1 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.26.4)\n", + "Requirement already satisfied: h5py in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (3.11.0)\n", + "Requirement already satisfied: scipy>=1.9 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from scikit-image->efficientnet) (1.13.1)\n", + "Requirement already satisfied: networkx>=2.8 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from scikit-image->efficientnet) (3.2.1)\n", + "Requirement already satisfied: pillow>=9.1 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from scikit-image->efficientnet) (10.3.0)\n", + "Requirement already satisfied: imageio>=2.33 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from scikit-image->efficientnet) (2.33.1)\n", + "Requirement already satisfied: tifffile>=2022.8.12 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from scikit-image->efficientnet) (2023.4.12)\n", + "Requirement already satisfied: packaging>=21 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from scikit-image->efficientnet) (23.2)\n", + "Requirement already satisfied: lazy-loader>=0.4 in c:\\users\\sriram\\anaconda3\\lib\\site-packages (from scikit-image->efficientnet) (0.4)\n" + ] + } + ], + "source": [ + "!pip install tensorflow\n", + "!pip install -U efficientnet" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "f056b2ef-bdb0-4f82-b0fe-9a3daa0163c3", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " category image\n", + "0 covid C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "1 covid C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "2 covid C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "3 covid C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "4 covid C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + ".. ... ...\n", + "184 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "185 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "186 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "187 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "188 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "\n", + "[189 rows x 2 columns]\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "flattened_data = []\n", + "for entry in test_images:\n", + " category = entry['category']\n", + " for img in entry['images']:\n", + " flattened_data.append({'category': category, 'image': img})\n", + "\n", + "# Convert to DataFrame\n", + "test_dataset = pd.DataFrame(flattened_data)\n", + "\n", + "# Display the DataFrame\n", + "print(test_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "beaa8548-b4c9-4de3-ac14-37a22f2b8062", + "metadata": {}, + "outputs": [], + "source": [ + "test_dataset_f = test_dataset.sample(frac=1).reset_index(drop=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "5b4c6248-259a-4b82-82cf-b67019bb7ab4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " category image\n", + "0 covid C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "1 covid C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "2 covid C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "3 covid C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "4 covid C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + ".. ... ...\n", + "373 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "374 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "375 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "376 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "377 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification...\n", + "\n", + "[378 rows x 2 columns]\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "flattened_data = []\n", + "for entry in train_images:\n", + " category = entry['category']\n", + " for img in entry['images']:\n", + " flattened_data.append({'category': category, 'image': img})\n", + "\n", + "# Convert to DataFrame\n", + "train_dataset = pd.DataFrame(flattened_data)\n", + "\n", + "# Display the DataFrame\n", + "print(train_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "9543cc21-a078-4926-ab32-a37164c6df37", + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset_f = train_dataset.sample(frac=1).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "c6ecb731-37e2-4d6f-86ac-d166fd1b2288", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label to Integer Mapping:\n", + "covid: 0\n", + "normal_chestray: 1\n", + "pneumonia: 2\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "# Initialize the LabelEncoder\n", + "label_encoder = LabelEncoder()\n", + "\n", + "# Fit and transform the 'category' column\n", + "train_dataset_f['category_encoded'] = label_encoder.fit_transform(train_dataset_f['category'])\n", + "\n", + "# Print the DataFrame with encoded labels\n", + "#print(train_dataset_f)\n", + "\n", + "# Retrieve and print the mapping of labels to integers\n", + "classes_tra = label_encoder.classes_\n", + "print(\"Label to Integer Mapping:\")\n", + "for idx, label in enumerate(classes_tra):\n", + " print(f\"{label}: {idx}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "ae819fc2-0bfa-4bd8-94d3-385ac88d4f14", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " category image \\\n", + "0 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "1 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "2 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "3 normal_chestray C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "4 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + ".. ... ... \n", + "373 covid C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "374 normal_chestray C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "375 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "376 normal_chestray C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "377 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "\n", + " category_encoded \n", + "0 2 \n", + "1 2 \n", + "2 2 \n", + "3 1 \n", + "4 2 \n", + ".. ... \n", + "373 0 \n", + "374 1 \n", + "375 2 \n", + "376 1 \n", + "377 2 \n", + "\n", + "[378 rows x 3 columns]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_dataset_f" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "847a0963-9f30-4d38-9572-69f7ca5ce822", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label to Integer Mapping:\n", + "covid: 0\n", + "normal_chestray: 1\n", + "pneumonia: 2\n" + ] + } + ], + "source": [ + "# Initialize the LabelEncoder\n", + "label_encoder = LabelEncoder()\n", + "\n", + "# Fit and transform the 'category' column\n", + "test_dataset_f['category_encoded'] = label_encoder.fit_transform(test_dataset_f['category'])\n", + "\n", + "# Print the DataFrame with encoded labels\n", + "#print(df)\n", + "\n", + "# Retrieve and print the mapping of labels to integers\n", + "classes_tes = label_encoder.classes_\n", + "print(\"Label to Integer Mapping:\")\n", + "for idx, label in enumerate(classes_tes):\n", + " print(f\"{label}: {idx}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "57a86407-d8d9-4d5a-8ce1-9003b790c0f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " category image \\\n", + "0 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "1 covid C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "2 normal_chestray C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "3 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "4 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + ".. ... ... \n", + "184 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "185 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "186 covid C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "187 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "188 pneumonia C:\\Users\\SRIRAM\\Documents\\Image Classification... \n", + "\n", + " category_encoded \n", + "0 2 \n", + "1 0 \n", + "2 1 \n", + "3 2 \n", + "4 2 \n", + ".. ... \n", + "184 2 \n", + "185 2 \n", + "186 0 \n", + "187 2 \n", + "188 2 \n", + "\n", + "[189 rows x 3 columns]" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_dataset_f" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "262e4f09-f7ab-4539-b8da-9270dfacba16", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 378 validated image filenames belonging to 3 classes.\n", + "Found 189 validated image filenames belonging to 3 classes.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SRIRAM\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\SRIRAM\\anaconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n", + " self._warn_if_super_not_called()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 3s/step - accuracy: 0.3158 - loss: 1.2204 - val_accuracy: 0.2857 - val_loss: 0.6017\n", + "Epoch 2/10\n", + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 2s/step - accuracy: 0.5890 - loss: 0.5441 - val_accuracy: 0.4180 - val_loss: 0.7031\n", + "Epoch 3/10\n", + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 2s/step - accuracy: 0.7783 - loss: 0.3766 - val_accuracy: 0.3968 - val_loss: 1.2036\n", + "Epoch 4/10\n", + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 2s/step - accuracy: 0.7288 - loss: 0.4041 - val_accuracy: 0.4074 - val_loss: 0.9209\n", + "Epoch 5/10\n", + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 2s/step - accuracy: 0.8326 - loss: 0.3062 - val_accuracy: 0.3704 - val_loss: 1.3143\n", + "Epoch 6/10\n", + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 2s/step - accuracy: 0.8650 - loss: 0.2402 - val_accuracy: 0.5503 - val_loss: 0.6968\n", + "Epoch 7/10\n", + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 2s/step - accuracy: 0.8697 - loss: 0.2311 - val_accuracy: 0.4550 - val_loss: 0.9919\n", + "Epoch 8/10\n", + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 2s/step - accuracy: 0.8797 - loss: 0.2156 - val_accuracy: 0.3968 - val_loss: 1.7659\n", + "Epoch 9/10\n", + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 2s/step - accuracy: 0.8894 - loss: 0.1851 - val_accuracy: 0.4021 - val_loss: 1.7183\n", + "Epoch 10/10\n", + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 2s/step - accuracy: 0.9005 - loss: 0.1703 - val_accuracy: 0.3862 - val_loss: 1.7062\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 1s/step - accuracy: 0.4315 - loss: 1.5615\n", + "Validation accuracy: 0.38624337315559387\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n", + "\n", + "\n", + "# Label Encoding\n", + "\n", + "# Create ImageDataGenerators\n", + "train_datagen = ImageDataGenerator(\n", + " rescale=1./255, \n", + " rotation_range=20, \n", + " zoom_range=0.2, \n", + " horizontal_flip=True\n", + ")\n", + "\n", + "val_datagen = ImageDataGenerator(rescale=1./255)\n", + "\n", + "# Create data generators using flow_from_dataframe\n", + "train_generator = train_datagen.flow_from_dataframe(\n", + " dataframe=train_dataset_f,\n", + " x_col='image',\n", + " y_col='category',\n", + " target_size=(150, 150),\n", + " batch_size=32,\n", + " class_mode='categorical' # Change to 'categorical' if you have more than 2 classes\n", + ")\n", + "\n", + "val_generator = val_datagen.flow_from_dataframe(\n", + " dataframe=test_dataset_f,\n", + " x_col='image',\n", + " y_col='category',\n", + " target_size=(150, 150),\n", + " batch_size=32,\n", + " class_mode='categorical'\n", + ")\n", + "\n", + "# Model selection and architecture\n", + "model = Sequential([\n", + " Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n", + " MaxPooling2D((2, 2)),\n", + " Conv2D(64, (3, 3), activation='relu'),\n", + " MaxPooling2D((2, 2)),\n", + " Conv2D(128, (3, 3), activation='relu'),\n", + " MaxPooling2D((2, 2)),\n", + " Flatten(),\n", + " Dense(512, activation='relu'),\n", + " Dropout(0.5),\n", + " Dense(3, activation='sigmoid') # Change to match the number of classes\n", + "])\n", + "\n", + "# Compile and train the model\n", + "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(train_generator, epochs=10, validation_data=val_generator)\n", + "\n", + "# Evaluate the model\n", + "test_loss, test_acc = model.evaluate(val_generator)\n", + "print('Validation accuracy:', test_acc)\n", + "\n", + "# Save the model\n", + "#model.save('my_model.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "8fe5fece-675a-44a9-a3bb-8e9c266531ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of the preprocessed image: (1, 150, 150, 3)\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n", + "Prediction_Classes for different types\n", + "covid: 0\n", + "normal_chestray: 1\n", + "pneumonia: 2\n", + "Predicted array for : 0\n", + "The predicted class is: Covid\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "# Example usage\n", + "image_path_axby = 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\covid.jpg'\n", + "\n", + "# Load the image using OpenCV\n", + "image_pred = cv2.imread(image_path_axby)\n", + "\n", + "if image_pred is None:\n", + " print(f\"Error: Unable to load image at {image_path}\")\n", + "else:\n", + " # Resize the image to match the input shape of the model (150x150)\n", + " image_pred = cv2.resize(image_pred, (150, 150))\n", + "\n", + " # Convert the image to a numpy array\n", + " image_pred = np.array(image_pred)\n", + "\n", + " # Rescale the image (if the model was trained with rescaling)\n", + " image_pred = image_pred / 255.0\n", + "\n", + " # Add an extra dimension to match the input shape (1, 150, 150, 3)\n", + " image_pred = np.expand_dims(image_pred, axis=0)\n", + "\n", + " # Print the shape of the preprocessed image\n", + " print(\"Shape of the preprocessed image:\", image_pred.shape)\n", + "\n", + " # Predict using the model\n", + " prediction = model.predict(image_pred)\n", + " \n", + " # Example prediction output\n", + " prediction = np.array(prediction)\n", + "\n", + " print(f\"Prediction_Classes for different types\\ncovid: 0\\nnormal_chestray: 1\\npneumonia: 2\")\n", + " \n", + " # Get the predicted class\n", + " predicted_ = np.argmax(prediction)\n", + "\n", + "\n", + " print(f\"Predicted array for : {predicted_}\")\n", + "\n", + " # Decode the prediction\n", + " if predicted_ == 0:\n", + " predicted_class= \"Covid\"\n", + " elif predicted_ == 1:\n", + " predicted_class= \"Normal_chestray\"\n", + " else:\n", + " predicted_class= \"Pneumonia\"\n", + "\n", + " # Print the predicted class\n", + " print(f'The predicted class is: {predicted_class}')\n", + " #print(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "26c0bc32-1ba8-4949-ad72-70a335bcc0a8", + "metadata": {}, + "outputs": [], + "source": [ + "model.save('FINAL_MODEL.keras')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "93e77d2e-2d4d-469d-9dcb-dc92323e2024", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of the preprocessed image: (1, 150, 150, 3)\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n", + "Prediction_Classes for different types\n", + "covid: 0\n", + "normal_chestray: 1\n", + "pneumonia: 2\n", + "Predicted array for : 1\n", + "The predicted class is: Normal_chestray\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "# Example usage\n", + "image_path_axby_o = 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\0103.jpeg'\n", + "\n", + "\n", + "# Load the image using OpenCV\n", + "image_pred = cv2.imread(image_path_axby_o)\n", + "\n", + "if image_pred is None:\n", + " print(f\"Error: Unable to load image at {image_path}\")\n", + "else:\n", + " # Resize the image to match the input shape of the model (150x150)\n", + " image_pred = cv2.resize(image_pred, (150, 150))\n", + "\n", + " # Convert the image to a numpy array\n", + " image_pred = np.array(image_pred)\n", + "\n", + " # Rescale the image (if the model was trained with rescaling)\n", + " image_pred = image_pred / 255.0\n", + "\n", + " # Add an extra dimension to match the input shape (1, 150, 150, 3)\n", + " image_pred = np.expand_dims(image_pred, axis=0)\n", + "\n", + " # Print the shape of the preprocessed image\n", + " print(\"Shape of the preprocessed image:\", image_pred.shape)\n", + "\n", + " # Predict using the model\n", + " prediction = model.predict(image_pred)\n", + " \n", + " # Example prediction output\n", + " prediction = np.array(prediction)\n", + "\n", + " print(f\"Prediction_Classes for different types\\ncovid: 0\\nnormal_chestray: 1\\npneumonia: 2\")\n", + " \n", + " # Get the predicted class\n", + " predicted_ = np.argmax(prediction)\n", + "\n", + "\n", + " print(f\"Predicted array for : {predicted_}\")\n", + "\n", + " # Decode the prediction\n", + " if predicted_ == 0:\n", + " predicted_class= \"Covid\"\n", + " elif predicted_ == 1:\n", + " predicted_class= \"Normal_chestray\"\n", + " else:\n", + " predicted_class= \"Pneumonia\"\n", + "\n", + " # Print the predicted class\n", + " print(f'The predicted class is: {predicted_class}')\n", + " #print(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "7da46192-e124-459c-800e-e84d4cca07b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of the preprocessed image: (1, 150, 150, 3)\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n", + "Prediction_Classes for different types\n", + "covid: 0\n", + "normal_chestray: 1\n", + "pneumonia: 2\n", + "Predicted array for : 0\n", + "The predicted class is: Covid\n" + ] + } + ], + "source": [ + "# Example usage\n", + "image_path_axby_o = 'C:\\\\Users\\\\SRIRAM\\\\Documents\\\\Image Classification\\\\covid_from_website.png'\n", + "\n", + "\n", + "# Load the image using OpenCV\n", + "image_pred = cv2.imread(image_path_axby_o)\n", + "\n", + "if image_pred is None:\n", + " print(f\"Error: Unable to load image at {image_path}\")\n", + "else:\n", + " # Resize the image to match the input shape of the model (150x150)\n", + " image_pred = cv2.resize(image_pred, (150, 150))\n", + "\n", + " # Convert the image to a numpy array\n", + " image_pred = np.array(image_pred)\n", + "\n", + " # Rescale the image (if the model was trained with rescaling)\n", + " image_pred = image_pred / 255.0\n", + "\n", + " # Add an extra dimension to match the input shape (1, 150, 150, 3)\n", + " image_pred = np.expand_dims(image_pred, axis=0)\n", + "\n", + " # Print the shape of the preprocessed image\n", + " print(\"Shape of the preprocessed image:\", image_pred.shape)\n", + "\n", + " # Predict using the model\n", + " prediction = model.predict(image_pred)\n", + " \n", + " # Example prediction output\n", + " prediction = np.array(prediction)\n", + "\n", + " print(f\"Prediction_Classes for different types\\ncovid: 0\\nnormal_chestray: 1\\npneumonia: 2\")\n", + " \n", + " # Get the predicted class\n", + " predicted_ = np.argmax(prediction)\n", + "\n", + "\n", + " print(f\"Predicted array for : {predicted_}\")\n", + "\n", + " # Decode the prediction\n", + " if predicted_ == 0:\n", + " predicted_class= \"Covid\"\n", + " elif predicted_ == 1:\n", + " predicted_class= \"Normal_chestray\"\n", + " else:\n", + " predicted_class= \"Pneumonia\"\n", + "\n", + " # Print the predicted class\n", + " print(f'The predicted class is: {predicted_class}')\n", + " #print(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3f8c986-d186-48de-8f99-9b2b3b73d035", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62f10b4c-eec4-4cde-a973-ae95582987ab", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01d80481-0b8b-4827-983e-1692e3483416", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}