Delete eval_coco.py

eval_coco.py

-import colorsys
-import json
-import os
-
-import cv2
-import numpy as np
-import torch
-import torch.backends.cudnn as cudnn
-import torch.nn as nn
-from PIL import Image, ImageDraw, ImageFont
-from torch.autograd import Variable
-from tqdm import tqdm
-
-from nets.yolo4 import YoloBody
-from utils.utils import (DecodeBox, bbox_iou, diou_non_max_suppression,
-                         letterbox_image, non_max_suppression,
-                         yolo_correct_boxes)
-from yolo import YOLO
-
-coco_classes = {'person': 1, 'bicycle': 2, 'car': 3, 'motorbike': 4, 'aeroplane': 5, 
-    'bus': 6, 'train': 7, 'truck': 8, 'boat': 9, 'traffic light': 10, 'fire hydrant': 11, 
-    '': 83, 'stop sign': 13, 'parking meter': 14, 'bench': 15, 'bird': 16, 'cat': 17, 
-    'dog': 18, 'horse': 19, 'sheep': 20, 'cow': 21, 'elephant': 22, 'bear': 23, 'zebra': 24, 
-    'giraffe': 25, 'backpack': 27, 'umbrella': 28, 'handbag': 31, 'tie': 32, 'suitcase': 33, 
-    'frisbee': 34, 'skis': 35, 'snowboard': 36, 'sports ball': 37, 'kite': 38, 'baseball bat': 39, 
-    'baseball glove': 40, 'skateboard': 41, 'surfboard': 42, 'tennis racket': 43, 'bottle': 44, 
-    'wine glass': 46, 'cup': 47, 'fork': 48, 'knife': 49, 'spoon': 50, 'bowl': 51, 'banana': 52, 
-    'apple': 53, 'sandwich': 54, 'orange': 55, 'broccoli': 56, 'carrot': 57, 'hot dog': 58, 
-    'pizza': 59, 'donut': 60, 'cake': 61, 'chair': 62, 'sofa': 63, 'pottedplant': 64, 'bed': 65, 
-    'diningtable': 67, 'toilet': 70, 'tvmonitor': 72, 'laptop': 73, 'mouse': 74, 'remote': 75, 
-    'keyboard': 76, 'cell phone': 77, 'microwave': 78, 'oven': 79, 'toaster': 80, 'sink': 81, 
-    'refrigerator': 82, 'book': 84, 'clock': 85, 'vase': 86, 'scissors': 87, 'teddy bear': 88, 
-    'hair drier': 89, 'toothbrush': 90
-}
-
-class mAP_YOLO(YOLO):
-    #---------------------------------------------------#
-    #   检测图片
-    #---------------------------------------------------#
-    def detect_image(self, image_id, image, results):
-        self.confidence = 0.001
-        image_shape = np.array(np.shape(image)[0:2])
-
-        #---------------------------------------------------------#
-        #   给图像增加灰条，实现不失真的resize
-        #---------------------------------------------------------#
-        crop_img = np.array(letterbox_image(image, (self.model_image_size[1],self.model_image_size[0])))
-        photo = np.array(crop_img,dtype = np.float32) / 255.0
-        photo = np.transpose(photo, (2, 0, 1))
-        #---------------------------------------------------------#
-        #   添加上batch_size维度
-        #---------------------------------------------------------#
-        images = [photo]
-
-        with torch.no_grad():
-            images = torch.from_numpy(np.asarray(images))
-            if self.cuda:
-                images = images.cuda()
-
-            #---------------------------------------------------------#
-            #   将图像输入网络当中进行预测！
-            #---------------------------------------------------------#
-            outputs = self.net(images)
-            output_list = []
-            for i in range(3):
-                output_list.append(self.yolo_decodes[i](outputs[i]))
-
-            #---------------------------------------------------------#
-            #   将预测框进行堆叠，然后进行非极大抑制
-            #---------------------------------------------------------#
-            output = torch.cat(output_list, 1)
-            batch_detections = non_max_suppression(output, len(self.class_names),
-                                                    conf_thres=self.confidence,
-                                                    nms_thres=self.iou)
-
-            #---------------------------------------------------------#
-            #   如果没有检测出物体，返回原图
-            #---------------------------------------------------------#
-            try:
-                batch_detections = batch_detections[0].cpu().numpy()
-            except:
-                return image
-            
-            #---------------------------------------------------------#
-            #   对预测框进行得分筛选
-            #---------------------------------------------------------#
-            top_index = batch_detections[:,4] * batch_detections[:,5] > self.confidence
-            top_conf = batch_detections[top_index,4]*batch_detections[top_index,5]
-            top_label = np.array(batch_detections[top_index,-1],np.int32)
-            top_bboxes = np.array(batch_detections[top_index,:4])
-            top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:,0],-1),np.expand_dims(top_bboxes[:,1],-1),np.expand_dims(top_bboxes[:,2],-1),np.expand_dims(top_bboxes[:,3],-1)
-
-            #-----------------------------------------------------------------#
-            #   在图像传入网络预测前会进行letterbox_image给图像周围添加灰条
-            #   因此生成的top_bboxes是相对于有灰条的图像的
-            #   我们需要对其进行修改，去除灰条的部分。
-            #-----------------------------------------------------------------#
-            boxes = yolo_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.model_image_size[0],self.model_image_size[1]]),image_shape)
-
-        for i, c in enumerate(top_label):
-            result = {}
-            predicted_class = self.class_names[c]
-            top, left, bottom, right = boxes[i]
-
-            top = max(0, np.floor(top + 0.5).astype('int32'))
-            left = max(0, np.floor(left + 0.5).astype('int32'))
-            bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
-            right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
-
-            result["image_id"] = int(image_id)
-            result["category_id"] = coco_classes[predicted_class]
-            result["bbox"] = [float(left),float(top),float(right-left),float(bottom-top)]
-            result["score"] = float(top_conf[i])
-            results.append(result)
-
-        return results
-
-yolo = mAP_YOLO()
-
-jpg_names = os.listdir("./coco_dataset/val2017")
-
-with open("./coco_dataset/eval_results.json","w") as f:
-    results = []
-    for jpg_name in tqdm(jpg_names):
-        if jpg_name.endswith("jpg"):
-            image_path = "./coco_dataset/val2017/" + jpg_name
-            image = Image.open(image_path)
-            # 开启后在之后计算mAP可以可视化
-            results = yolo.detect_image(jpg_name.split(".")[0],image,results)
-    json.dump(results,f)