result_output_v3_2023.08.08.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "983e8af1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['best_noodle_drink.pt'], source=C:\\Users\\sj990\\MachineLearning\\CAPSTONE_DESIGN_AI_Module\\yolo\\first_image_folder - \\1.jpg, data=yolov5\\data\\coco128.yaml, imgsz=[640, 640], conf_thres=0.65, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=True, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=../yolo, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
      "YOLOv5  v7.0-198-g34c2187 Python-3.9.7 torch-2.0.0+cpu CPU\n",
      "\n",
      "Fusing layers... \n",
      "Model summary: 157 layers, 7015519 parameters, 0 gradients, 15.8 GFLOPs\n",
      "image 1/1 C:\\Users\\sj990\\MachineLearning\\CAPSTONE_DESIGN_AI_Module\\yolo\\first_image_folder - \\1.jpg: 640x480 1 noodle, 118.1ms\n",
      "Speed: 1.0ms pre-process, 118.1ms inference, 0.0ms NMS per image at shape (1, 3, 640, 640)\n",
      "Results saved to \u001b[1m..\\yolo\\exp\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'상품': '10111_오뚜기컵누들매콤37.8G', '정확도': 0.99}]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub\n",
    "import numpy as np\n",
    "import glob     \n",
    "import json\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "#########################################################################\n",
    "# 어플에서 올라온 이미지 처음 경로 - 초기 설정 필요합니다.\n",
    "application_image = r'C:\\Users\\sj990\\MachineLearning\\CAPSTONE_DESIGN_AI_Module\\yolo\\first_image_folder - 복사본'\n",
    "#########################################################################\n",
    "\n",
    "image_directory = application_image\n",
    "# jpg, png, jpeg 형식 (리스트형식으로 저장된다, 경로에는 파일한장만 존재하게)\n",
    "image_files = glob.glob(image_directory + '/*.png') + glob.glob(image_directory + '/*.jpg') + glob.glob(image_directory + '/*.jpeg')\n",
    "# 리스트중 첫번째것만 사용\n",
    "first_image = image_files[0]\n",
    "\n",
    "# 객체검출 실행\n",
    "!python yolov5/detect.py --weights best_noodle_drink.pt --img 640 --conf 0.65 --source \"{first_image}\" --project ../yolo --save-crop\n",
    "\n",
    "# 검출경로\n",
    "crops_path = os.path.join('exp', 'crops')\n",
    "\n",
    "\n",
    "###############################################################\n",
    "# 객체 폴더 있는지 탐지 (매번 추가) - 작업중\n",
    "noodle_detect = os.path.exists(os.path.join(crops_path, 'noodle'))\n",
    "drink_detect = os.path.exists(os.path.join(crops_path, 'drink'))\n",
    "\n",
    "if noodle_detect:\n",
    "    yolo_img = os.path.join(crops_path, 'noodle')\n",
    "    classification_label = \"noodle\"\n",
    "    \n",
    "elif drink_detect:\n",
    "    yolo_img = os.path.join(crops_path, 'drink')\n",
    "    classification_label = \"drink\"\n",
    "    \n",
    "    \n",
    "else:\n",
    "    print(\"객체검출실패\")\n",
    "###############################################################\n",
    "    \n",
    "    \n",
    "yolo_image_directory = yolo_img\n",
    "# jpg, png, jpeg 형식 (리스트형식으로 저장된다, 경로에는 파일한장만 존재하게)\n",
    "yolo_image_files = glob.glob(yolo_image_directory + '/*.png') + glob.glob(yolo_image_directory + '/*.jpg') + glob.glob(yolo_image_directory + '/*.jpeg')\n",
    "# 리스트중 첫번째것만 사용\n",
    "yolo_image_path = yolo_image_files[0]\n",
    "\n",
    "    \n",
    "class Model:\n",
    "    # 초기화\n",
    "    def __init__(self, image_path, model_path, class_labels_path):\n",
    "        self.image_path = image_path\n",
    "        self.model_path = model_path\n",
    "        self.class_labels_path = class_labels_path\n",
    "        \n",
    "    #예측 (전처리된이미지와 클래스라벨을 삽입)\n",
    "    def preds_model(self, img, class_labels):\n",
    "        model = tf.keras.models.load_model(self.model_path,  custom_objects={'KerasLayer': hub.KerasLayer})\n",
    "        preds = model.predict(img) \n",
    "        \n",
    "        # 결과값 상위 1개\n",
    "        top_preds_idx = preds[0].argsort()[ : : -1][ : 1]\n",
    "        top_preds_labels = [class_labels[idx] for idx in top_preds_idx]\n",
    "        top_preds_probs = preds[0][top_preds_idx]\n",
    "        return top_preds_labels, top_preds_probs\n",
    "    \n",
    "    #이미지전처리_1 (기본)\n",
    "    def img_pre_default(self):  \n",
    "        img = tf.keras.preprocessing.image.load_img(self.image_path, target_size=(256, 256))\n",
    "        img = tf.keras.preprocessing.image.img_to_array(img)\n",
    "        img = img.astype('float32') / 255.0\n",
    "        img = np.expand_dims(img, axis=0)\n",
    "        return img\n",
    "\n",
    "    #라벨불러오기\n",
    "    def class_label(self):\n",
    "        class_labels = []\n",
    "        with open(self.class_labels_path, 'r') as file:\n",
    "            for line in file:\n",
    "                class_labels.append(line.strip())\n",
    "        return class_labels\n",
    "    \n",
    "    #결과출력\n",
    "    def print_result(self, top_preds_labels, top_preds_probs):\n",
    "        results = []\n",
    "        #json형식 results\n",
    "        for label, prob in zip(top_preds_labels, top_preds_probs):\n",
    "            result = {\n",
    "                '상품': label,\n",
    "                '정확도': round(float(prob), 2)\n",
    "            }\n",
    "            results.append(result)\n",
    "        print(results)\n",
    "        return results\n",
    "  \n",
    "\n",
    "#########################################################################\n",
    "# 저장한 모델, 라벨들 경로 - 초기 설정 필요합니다.\n",
    "\n",
    "# 면류 모델경로, 면류 라벨경로\n",
    "image_path = yolo_image_path\n",
    "noodle_model_path = r'C:\\Users\\sj990\\Desktop\\종설\\모델,라벨\\모델\\model_noodle.h5'\n",
    "noodle_label_path = r'C:\\Users\\sj990\\Desktop\\종설\\모델,라벨\\라벨\\model_noodle_label.txt'\n",
    "\n",
    "# 음료 모델경로, 음료 라벨경로\n",
    "drink_model_path = r'C:\\Users\\sj990\\Desktop\\종설\\모델,라벨\\라벨\\model_drink.h5'\n",
    "drink_label_path = r'C:\\Users\\sj990\\Desktop\\종설\\모델,라벨\\라벨\\model_drink_label.txt'\n",
    "\n",
    "\n",
    "# 미분류 모델경로, 면류 라벨경로\n",
    "unclassified_model_path = r'C:\\Users\\sj990\\Desktop\\종설\\모델,라벨\\모델\\model_unclass.h5'\n",
    "unclassified_label_path = r'C:\\Users\\sj990\\Desktop\\종설\\모델,라벨\\라벨\\model_unclass_label.txt'\n",
    "\n",
    "\n",
    "################################경로설정################################\n",
    "\n",
    "\n",
    "\n",
    "### 특징분류 ###\n",
    "if classification_label == \"noodle\":\n",
    "    #모델정의\n",
    "    model_noodle = Model(image_path, noodle_model_path, noodle_label_path)\n",
    "    #예측\n",
    "    result_noodle_labels, result_noodle_probs = model_noodle.preds_model(model_noodle.img_pre_default(), model_noodle.class_label())\n",
    "    #결과출력\n",
    "    noodle_result = model_noodle.print_result(result_noodle_labels, result_noodle_probs)\n",
    "    \n",
    "    \n",
    "    # json파일 저장필요할시 주석 제거후 사용\n",
    "    with open('results.json', 'w', encoding='utf-8') as file:\n",
    "        json.dump(noodle_result, file, ensure_ascii=False)\n",
    "        \n",
    "elif classification_labe ==\"drink\":\n",
    "    #모델정의\n",
    "    model_drink = Model(image_path, drink_model_path, drink_label_path)\n",
    "    #예측\n",
    "    result_drink_labels, result_drink_probs = model_drink.preds_model(model_drink.img_pre_default(), model_drink.class_label())\n",
    "    #결과출력\n",
    "    drink_result = model_drink.print_result(result_drink_labels, result_drink_probs)\n",
    "    \n",
    "    # json파일 저장필요할시 주석 제거후 사용\n",
    "    with open('results.json', 'w', encoding='utf-8') as file:\n",
    "        json.dump(drink_result, file, ensure_ascii=False)\n",
    "        \n",
    "    \n",
    "elif classification_labe ==\"미분류\":\n",
    "    #모델정의\n",
    "    model_unclassified = Model(image_path, unclassified_model_path, unclassified_label_path)\n",
    "    #예측\n",
    "    result_unclassified_labels, result_unclassified_probs = model_unclassified.preds_model(model_unclassified.img_pre_default(), model_unclassified.class_label())\n",
    "    #결과출력\n",
    "    unclassified_result = model_unclassified.print_result(result_unclassified_labels, result_unclassified_probs)\n",
    "    \n",
    "    \n",
    "    # json파일 저장필요할시 주석 제거후 사용\n",
    "    with open('results.json', 'w', encoding='utf-8') as file:\n",
    "        json.dump(unclassified_result, file, ensure_ascii=False)\n",
    "    \n",
    "    \n",
    "# 작업끝난후 yolo 이미지 폴더 삭제\n",
    "shutil.rmtree(\"exp\")\n",
    "\n",
    "# 작업끝난후 어플리케이션에서 올라온 폴더 삭제\n",
    "shutil.rmtree(application_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "366388ca",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf_gpu",
   "language": "python",
   "name": "tf_gpu"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}