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FPS_test.py

+import colorsys
+import os
+import time
+
+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, letterbox_image,
+                         non_max_suppression, yolo_correct_boxes)
+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
+        image_shape = np.array(np.shape(image)[0:2])
+
+        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)
+        photo /= 255.0
+        photo = np.transpose(photo, (2, 0, 1))
+        photo = photo.astype(np.float32)
+        images = []
+        images.append(photo)
+        images = np.asarray(images)
+
+        with torch.no_grad():
+            images = torch.from_numpy(images)
+            if self.cuda:
+                images = images.cuda()
+            outputs = self.net(images)
+            output_list = []
+            for i in range(3):
+                output_list.append(self.yolo_decodes[i](outputs[i]))
+            output = torch.cat(output_list, 1)
+            batch_detections = non_max_suppression(output, len(self.class_names),
+                                                    conf_thres=self.confidence,
+                                                    nms_thres=self.iou)
+            try:
+                batch_detections = batch_detections[0].cpu().numpy()
+                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)
+                # 去掉灰条
+                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)
+            except:
+                pass
+                
+        t1 = time.time()
+        for _ in range(test_interval):
+            with torch.no_grad():
+                outputs = self.net(images)
+                output_list = []
+                for i in range(3):
+                    output_list.append(self.yolo_decodes[i](outputs[i]))
+                output = torch.cat(output_list, 1)
+                batch_detections = non_max_suppression(output, len(self.class_names),
+                                                        conf_thres=self.confidence,
+                                                        nms_thres=self.iou)
+                try:
+                    batch_detections = batch_detections[0].cpu().numpy()
+                    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)
+                    # 去掉灰条
+                    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)
+                except:
+                    pass
+
+        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')