Delete FPS_test.py

FPS_test.py

-import time
-
-import numpy as np
-from keras import backend as K
-from PIL import Image
-
-from utils.utils import letterbox_image
-from yolo import YOLO
-
-'''
-该FPS测试不包括前处理（归一化与resize部分）、绘图。
-包括的内容为：网络推理、得分门限筛选、非极大抑制。
-使用'img/street.jpg'图片进行测试，该测试方法参考库https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch
-
-video.py里面测试的FPS会低于该FPS，因为摄像头的读取频率有限，而且处理过程包含了前处理和绘图部分。
-'''
-class FPS_YOLO(YOLO):
-    def get_FPS(self, image, test_interval):
-        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.
-        image_data = np.expand_dims(image_data, 0)
-
-        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})
-
-        t1 = time.time()
-        for _ in range(test_interval):
-            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})
-        t2 = time.time()
-        tact_time = (t2 - t1) / test_interval
-        return tact_time
-        
-yolo = FPS_YOLO()
-test_interval = 100
-img = Image.open('img/street.jpg')
-tact_time = yolo.get_FPS(img, test_interval)
-print(str(tact_time) + ' seconds, ' + str(1/tact_time) + 'FPS, @batch_size 1')