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FPS_test.py

+import os
+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):
+        # 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)  
+
+        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')