Delete FPS_test.py

FPS_test.py

-import time
-
-import numpy as np
-import torch
-from PIL import Image
-
-from utils.utils import 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])
-
-        #---------------------------------------------------------#
-        #   给图像增加灰条，实现不失真的resize
-        #   也可以直接resize进行识别
-        #---------------------------------------------------------#
-        if self.letterbox_image:
-            crop_img = np.array(letterbox_image(image, (self.model_image_size[1],self.model_image_size[0])))
-        else:
-            crop_img = image.convert('RGB')
-            crop_img = crop_img.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC)
-        photo = np.array(crop_img,dtype = np.float32) / 255.0
-        photo = np.transpose(photo, (2, 0, 1))
-        #---------------------------------------------------------#
-        #   添加上batch_size维度
-        #---------------------------------------------------------#
-        images = [photo]
-
-        with torch.no_grad():
-            images = torch.from_numpy(np.asarray(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)
-                
-                if self.letterbox_image:
-                    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)
-                else:
-                    top_xmin = top_xmin / self.model_image_size[1] * image_shape[1]
-                    top_ymin = top_ymin / self.model_image_size[0] * image_shape[0]
-                    top_xmax = top_xmax / self.model_image_size[1] * image_shape[1]
-                    top_ymax = top_ymax / self.model_image_size[0] * image_shape[0]
-                    boxes = np.concatenate([top_ymin,top_xmin,top_ymax,top_xmax], axis=-1)
-            
-            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)
-                   
-                    if self.letterbox_image:
-                        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)
-                    else:
-                        top_xmin = top_xmin / self.model_image_size[1] * image_shape[1]
-                        top_ymin = top_ymin / self.model_image_size[0] * image_shape[0]
-                        top_xmax = top_xmax / self.model_image_size[1] * image_shape[1]
-                        top_ymax = top_ymax / self.model_image_size[0] * image_shape[0]
-                        boxes = np.concatenate([top_ymin,top_xmin,top_ymax,top_xmax], axis=-1)
-                
-                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')