get_dr_txt.py

#----------------------------------------------------#
#   获取测试集的detection-result和images-optional
#   具体视频教程可查看
#   https://www.bilibili.com/video/BV1zE411u7Vw
#----------------------------------------------------#
from yolo import YOLO
import os
import numpy as np
import copy
import colorsys
import tensorflow as tf
from timeit import default_timer as timer
from tensorflow.keras import backend as K
from tensorflow.keras.models import load_model
from tensorflow.keras.layers import Input, Lambda
from tensorflow.keras.models import Model
from PIL import Image, ImageFont, ImageDraw
from nets.yolo4_tiny import yolo_body,yolo_eval
from utils.utils import letterbox_image
from tqdm import tqdm

import tensorflow as tf

gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)
    
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
        self.yolo_model = yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes)
        self.yolo_model.load_weights(self.model_path,by_name=True)
        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)

        if self.eager:
            self.input_image_shape = Input([2,],batch_size=1)
            inputs = [*self.yolo_model.output, self.input_image_shape]
            outputs = Lambda(yolo_eval, output_shape=(1,), name='yolo_eval',
                arguments={'anchors': self.anchors, 'num_classes': len(self.class_names), 'image_shape': self.model_image_size, 
                'score_threshold': self.score, 'eager': True, 'max_boxes': self.max_boxes})(inputs)
            self.yolo_model = Model([self.yolo_model.input, self.input_image_shape], outputs)
        else:
            self.input_image_shape = K.placeholder(shape=(2, ))
            
            self.boxes, self.scores, self.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)
 

    #---------------------------------------------------#
    #   检测图片
    #---------------------------------------------------#
    def detect_image(self, image_id, image):
        f = open("./input/detection-results/"+image_id+".txt","w") 

        # 调整图片使其符合输入要求
        new_image_size = (self.model_image_size[1],self.model_image_size[0])
        boxed_image = letterbox_image(image, new_image_size)
        image_data = np.array(boxed_image, dtype='float32')
        image_data /= 255.
        image_data = np.expand_dims(image_data, 0)  # Add batch dimension.

        if self.eager:
            # 预测结果
            input_image_shape = np.expand_dims(np.array([image.size[1], image.size[0]], dtype='float32'), 0)
            out_boxes, out_scores, out_classes = self.yolo_model.predict([image_data, input_image_shape]) 
        else:
            # 预测结果
            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):
            predicted_class = self.class_names[int(c)]
            score = str(out_scores[i])

            top, left, bottom, right = out_boxes[i]
            f.write("%s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)),str(int(bottom))))

        f.close()
        return 

yolo = mAP_YOLO()

image_ids = open('VOCdevkit/VOC2007/ImageSets/Main/test.txt').read().strip().split()

if not os.path.exists("./input"):
    os.makedirs("./input")
if not os.path.exists("./input/detection-results"):
    os.makedirs("./input/detection-results")
if not os.path.exists("./input/images-optional"):
    os.makedirs("./input/images-optional")

for image_id in tqdm(image_ids):
    image_path = "./VOCdevkit/VOC2007/JPEGImages/"+image_id+".jpg"
    image = Image.open(image_path)
    # 开启后在之后计算mAP可以可视化
    # image.save("./input/images-optional/"+image_id+".jpg")
    yolo.detect_image(image_id,image)
    
print("Conversion completed!")