Delete eval_coco.py

eval_coco.py

-import colorsys
-import json
-import os
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pylab
-from keras import backend as K
-from keras.applications.imagenet_utils import preprocess_input
-from keras.layers import Input
-from PIL import Image
-from tqdm import tqdm
-
-from nets.yolo4_tiny import yolo_body, yolo_eval
-from utils.utils import letterbox_image
-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):
-    #---------------------------------------------------#
-    #   获得所有的分类
-    #---------------------------------------------------#
-    def generate(self):
-        self.score = 0.01
-        self.iou = 0.5
-        model_path = os.path.expanduser(self.model_path)
-        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
-        
-        # 计算anchor数量
-        num_anchors = len(self.anchors)
-        num_classes = len(self.class_names)
-
-        # 载入模型，如果原来的模型里已经包括了模型结构则直接载入。
-        # 否则先构建模型再载入
-        try:
-            self.yolo_model = load_model(model_path, compile=False)
-        except:
-            self.yolo_model = yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes)
-            self.yolo_model.load_weights(self.model_path)
-        else:
-            assert self.yolo_model.layers[-1].output_shape[-1] == \
-                num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
-                'Mismatch between model and given anchor and class sizes'
-
-        print('{} model, anchors, and classes loaded.'.format(model_path))
-
-        # 画框设置不同的颜色
-        hsv_tuples = [(x / len(self.class_names), 1., 1.)
-                      for x in range(len(self.class_names))]
-        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
-        self.colors = list(
-            map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
-                self.colors))
-
-        # 打乱颜色
-        np.random.seed(10101)
-        np.random.shuffle(self.colors)
-        np.random.seed(None)
-
-        self.input_image_shape = K.placeholder(shape=(2, ))
-
-        #---------------------------------------------------------#
-        #   在yolo_eval函数中，我们会对预测结果进行后处理
-        #   后处理的内容包括，解码、非极大抑制、门限筛选等
-        #---------------------------------------------------------#
-        boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
-                num_classes, self.input_image_shape, max_boxes = self.max_boxes,
-                score_threshold = self.score, iou_threshold = self.iou, letterbox_image=self.letterbox_image)
-        return boxes, scores, classes
-        
-    #---------------------------------------------------#
-    #   检测图片
-    #---------------------------------------------------#
-    def detect_image(self, image_id, image, results):
-        #---------------------------------------------------------#
-        #   给图像增加灰条，实现不失真的resize
-        #---------------------------------------------------------#
-        if self.letterbox_image:
-            boxed_image = letterbox_image(image, (self.model_image_size[1],self.model_image_size[0]))
-        else:
-            boxed_image = image.convert('RGB')
-            boxed_image = boxed_image.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC)
-        image_data = np.array(boxed_image, dtype='float32')
-        image_data /= 255.
-        #---------------------------------------------------------#
-        #   添加上batch_size维度
-        #---------------------------------------------------------#
-        image_data = np.expand_dims(image_data, 0)
-
-        #---------------------------------------------------------#
-        #   将图像输入网络当中进行预测！
-        #---------------------------------------------------------#
-        out_boxes, out_scores, out_classes = self.sess.run(
-            [self.boxes, self.scores, self.classes],
-            feed_dict={
-                self.yolo_model.input: image_data,
-                self.input_image_shape: [image.size[1], image.size[0]],
-                K.learning_phase(): 0})
-
-        for i, c in enumerate(out_classes):
-            result = {}
-            predicted_class = self.class_names[c]
-            top, left, bottom, right = out_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(out_scores[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)