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FPS_test.py

+import os
+
+import numpy as np
+import torch
+from PIL import Image
+from torch.autograd import Variable
+from tqdm import tqdm
+
+from ssd import SSD
+from utils.box_utils import letterbox_image, ssd_correct_boxes
+
+'''
+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_SSD(SSD):
+    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[0],self.model_image_size[1])))
+        photo = np.array(crop_img,dtype = np.float64)
+        # 图片预处理，归一化
+        with torch.no_grad():
+            photo = Variable(torch.from_numpy(np.expand_dims(np.transpose(crop_img-MEANS,(2,0,1)),0)).type(torch.FloatTensor))
+            if self.cuda:
+                photo = photo.cuda()
+            preds = self.net(photo)
+            top_conf = []
+            top_label = []
+            top_bboxes = []
+            for i in range(preds.size(1)):
+                j = 0
+                while preds[0, i, j, 0] >= self.confidence:
+                    score = preds[0, i, j, 0]
+                    label_name = self.class_names[i-1]
+                    pt = (preds[0, i, j, 1:]).detach().numpy()
+                    coords = [pt[0], pt[1], pt[2], pt[3]]
+                    top_conf.append(score)
+                    top_label.append(label_name)
+                    top_bboxes.append(coords)
+                    j = j + 1
+            # 将预测结果进行解码
+            if len(top_conf)>0:
+                top_conf = np.array(top_conf)
+                top_label = np.array(top_label)
+                top_bboxes = np.array(top_bboxes)
+                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 = ssd_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.model_image_size[0],self.model_image_size[1]]),image_shape)
+
+        t1 = time.time()
+        for _ in range(test_interval):
+            # 图片预处理，归一化
+            with torch.no_grad():
+                preds = self.net(photo)
+                top_conf = []
+                top_label = []
+                top_bboxes = []
+                for i in range(preds.size(1)):
+                    j = 0
+                    while preds[0, i, j, 0] >= self.confidence:
+                        score = preds[0, i, j, 0]
+                        label_name = self.class_names[i-1]
+                        pt = (preds[0, i, j, 1:]).detach().numpy()
+                        coords = [pt[0], pt[1], pt[2], pt[3]]
+                        top_conf.append(score)
+                        top_label.append(label_name)
+                        top_bboxes.append(coords)
+                        j = j + 1
+                # 将预测结果进行解码
+                if len(top_conf)>0:
+                    top_conf = np.array(top_conf)
+                    top_label = np.array(top_label)
+                    top_bboxes = np.array(top_bboxes)
+                    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 = ssd_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.model_image_size[0],self.model_image_size[1]]),image_shape)
+
+        t2 = time.time()
+        tact_time = (t2 - t1) / test_interval
+        return tact_time
+        
+ssd = FPS_SSD()
+test_interval = 100
+img = Image.open('img/street.jpg')
+tact_time = ssd.get_FPS(img, test_interval)
+print(str(tact_time) + ' seconds, ' + str(1/tact_time) + 'FPS, @batch_size 1')