class EfficientDet(object): _defaults = { #"model_path" : 'model_data/efficientdet-d0.pth', "model_path": './logs/last.pth', "classes_path": 'model_data/classes.txt', "phi": 0, "confidence": 0.01, "iou": 0.01, "cuda": True } @classmethod def get_defaults(cls, n): if n in cls._defaults: return cls._defaults[n] else: return "Unrecognized attribute name '" + n + "'" #---------------------------------------------------# # 初始化Efficientdet #---------------------------------------------------# def __init__(self, **kwargs): self.__dict__.update(self._defaults) self.class_names = self._get_class() self.generate() #---------------------------------------------------# # 获得所有的分类 #---------------------------------------------------# def _get_class(self): classes_path = os.path.expanduser(self.classes_path) with open(classes_path) as f: class_names = f.readlines() class_names = [c.strip() for c in class_names] return class_names #---------------------------------------------------# # 载入模型 #---------------------------------------------------# def generate(self): #----------------------------------------# # 创建Efficientdet模型 #----------------------------------------# self.net = EfficientDetBackbone(len(self.class_names), self.phi).eval() #----------------------------------------# # 载入权值 #----------------------------------------# print('Loading weights into state dict...') state_dict = torch.load(self.model_path) self.net.load_state_dict(state_dict) if self.cuda: os.environ["CUDA_VISIBLE_DEVICES"] = '0' self.net = nn.DataParallel(self.net) self.net = self.net.cuda() print('{} model, anchors, and classes loaded.'.format(self.model_path)) #----------------------------------------# # 画框设置不同的颜色 #----------------------------------------# hsv_tuples = [(x / len(self.class_names), 1., 1.) for x in range(len(self.class_names))] self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) self.colors = list( map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors)) #---------------------------------------------------# # 检测图片 #---------------------------------------------------# def detect_image(self, image): image_shape = np.array(np.shape(image)[0:2]) #---------------------------------------------------------# # 给图像增加灰条,实现不失真的resize #---------------------------------------------------------# crop_img = np.array( letterbox_image(image, (image_sizes[self.phi], image_sizes[self.phi]))) photo = np.array(crop_img, dtype=np.float32) photo = np.transpose(preprocess_input(photo), (2, 0, 1)) with torch.no_grad(): images = torch.from_numpy(np.asarray([photo])) if self.cuda: images = images.cuda() #---------------------------------------------------------# # 传入网络当中进行预测 #---------------------------------------------------------# _, regression, classification, anchors = self.net(images) #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# regression = decodebox(regression, anchors, images) detection = torch.cat([regression, classification], axis=-1) batch_detections = non_max_suppression(detection, len(self.class_names), conf_thres=self.confidence, nms_thres=self.iou) #--------------------------------------# # 如果没有检测到物体,则返回原图 #--------------------------------------# try: batch_detections = batch_detections[0].cpu().numpy() except: return image #-----------------------------------------------------------# # 筛选出其中得分高于confidence的框 #-----------------------------------------------------------# top_index = batch_detections[:, 4] > self.confidence top_conf = batch_detections[top_index, 4] 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) #-----------------------------------------------------------# # 去掉灰条部分 #-----------------------------------------------------------# boxes = efficientdet_correct_boxes( top_ymin, top_xmin, top_ymax, top_xmax, np.array([image_sizes[self.phi], image_sizes[self.phi]]), image_shape) font = ImageFont.truetype(font='model_data/simhei.ttf', size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32')) thickness = max( (np.shape(image)[0] + np.shape(image)[1]) // image_sizes[self.phi], 1) for i, c in enumerate(top_label): predicted_class = self.class_names[c] score = top_conf[i] top, left, bottom, right = boxes[i] top = top - 5 left = left - 5 bottom = bottom + 5 right = right + 5 top = max(0, np.floor(top + 0.5).astype('int32')) left = max(0, np.floor(left + 0.5).astype('int32')) bottom = min( np.shape(image)[0], np.floor(bottom + 0.5).astype('int32')) right = min( np.shape(image)[1], np.floor(right + 0.5).astype('int32')) # 画框框 label = '{} {:.2f}'.format(predicted_class, score) draw = ImageDraw.Draw(image) label_size = draw.textsize(label, font) label = label.encode('utf-8') print(label, top, left, bottom, right) if top - label_size[1] >= 0: text_origin = np.array([left, top - label_size[1]]) else: text_origin = np.array([left, top + 1]) for i in range(thickness): draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[self.class_names.index( predicted_class)]) draw.rectangle( [tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[self.class_names.index(predicted_class)]) draw.text(text_origin, str(label, 'UTF-8'), fill=(0, 0, 0), font=font) del draw return image
class EfficientDet(object): _defaults = { #"model_path": 'model_data/efficientdet-d0.pth', # 'target_path': '2007_train_bike.txt', # "classes_path": 'model_data/coco_classes.txt', "confidence": 0.2, "cuda": True, } @classmethod def get_defaults(cls, n): if n in cls._defaults: return cls._defaults[n] else: return "Unrecognized attribute name '" + n + "'" #---------------------------------------------------# # 初始化Efficientdet #---------------------------------------------------# def __init__(self, model_path, det, target, classes, **kwargs): self.__dict__.update(self._defaults) self.nms_thres = 0.3 self.target_path = target self.classes_path = classes self.model_path = model_path self.phi = det self.class_names = self._get_class() self.generate() #---------------------------------------------------# # 获得所有的分类 #---------------------------------------------------# def _get_class(self): classes_path = os.path.expanduser(self.classes_path) with open(classes_path) as f: class_names = f.readlines() class_names = [c.strip() for c in class_names] return class_names #---------------------------------------------------# # 获得所有的分类 #---------------------------------------------------# def generate(self): os.environ["CUDA_VISIBLE_DEVICES"] = '0' self.net = EfficientDetBackbone(len(self.class_names), self.phi).eval() # 加快模型训练的效率 print('Loading weights into state dict...') state_dict = torch.load(self.model_path) for name, weights in state_dict.items(): # print(name, weights.size()) 可以查看模型中的模型名字和权重维度 if len(weights.size()) == 2: state_dict[name] = weights.squeeze(0) self.net.load_state_dict(state_dict) self.net = nn.DataParallel(self.net) if self.cuda: self.net = self.net.cuda() print('Finished!') print('{} model, anchors, and classes loaded.'.format(self.model_path)) # 画框设置不同的颜色 hsv_tuples = [(x / len(self.class_names), 1., 1.) for x in range(len(self.class_names))] self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) self.colors = list( map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors)) #---------------------------------------------------# # 检测图片 #---------------------------------------------------# def detect_image(self, image, target): image_shape = np.array(np.shape(image)[0:2]) crop_img = np.array(letterbox_image(image, (image_sizes[self.phi],image_sizes[self.phi]))) photo = np.array(crop_img,dtype = np.float32) photo = np.transpose(preprocess_input(photo), (2, 0, 1)) images = [] images.append(photo) images = np.asarray(images) with torch.no_grad(): images = torch.from_numpy(images) if self.cuda: images = images.cuda() _, regression, classification, anchors = self.net(images) regression = decodebox(regression, anchors, images) detection = torch.cat([regression, classification], axis=-1) batch_detections = non_max_suppression(detection, len(self.class_names), conf_thres=self.confidence, nms_thres=self.nms_thres) #default 0.3 try: batch_detections = batch_detections[0].cpu().numpy() except: return image top_index = batch_detections[:,4] > self.confidence top_conf = batch_detections[top_index,4] 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 = efficientdet_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([image_sizes[self.phi],image_sizes[self.phi]]),image_shape) font = ImageFont.truetype(font='model_data/simhei.ttf',size=np.floor(1.5e-2 * np.shape(image)[1] + 0.5).astype('int32')) thickness = (np.shape(image)[0] + np.shape(image)[1]) // image_sizes[self.phi] total_predict = 0 for i, c in enumerate(top_label): predicted_class = self.class_names[c] score = top_conf[i] #confidence if score > self.confidence: total_predict += 1 top, left, bottom, right = boxes[i] top = top - 5 left = left - 5 bottom = bottom + 5 right = right + 5 top = max(0, np.floor(top + 0.5).astype('int32')) left = max(0, np.floor(left + 0.5).astype('int32')) bottom = min(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32')) right = min(np.shape(image)[1], np.floor(right + 0.5).astype('int32')) # 画框框 label = '{} {:.2f}'.format(predicted_class, score) draw = ImageDraw.Draw(image) label_size = draw.textsize(label, font) label = label.encode('utf-8') print(label) if top - label_size[1] >= 0: text_origin = np.array([left, top - label_size[1]]) else: text_origin = np.array([left, top + 1]) for i in range(thickness): draw.rectangle( [left + i, top + i, right - i, bottom - i], outline=self.colors[self.class_names.index(predicted_class)]) draw.rectangle( [tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[self.class_names.index(predicted_class)]) draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font) del draw precision(target, total_predict) return image
class EfficientDet(object): #---------------------------------------------------# # 初始化Efficientdet #---------------------------------------------------# def __init__(self, model_path, phi=0, conf=0.3, cuda=True, **kwargs): # self.__dict__.update(self._defaults) self.class_names = classes self.model_path = model_path self.phi = phi self.confidence = conf self.cuda = cuda self.generate() #---------------------------------------------------# # 获得所有的分类 #---------------------------------------------------# def generate(self): os.environ["CUDA_VISIBLE_DEVICES"] = '0' self.net = EfficientDetBackbone(len(self.class_names), self.phi).eval() # 加快模型训练的效率 print('Loading weights into state dict...') state_dict = torch.load(self.model_path) self.net.load_state_dict(state_dict) self.net = nn.DataParallel(self.net) if self.cuda: self.net = self.net.cuda() print('Finished!') print('{} model, anchors, and classes loaded.'.format(self.model_path)) # 画框设置不同的颜色 hsv_tuples = [(x / len(self.class_names), 1., 1.) for x in range(len(self.class_names))] self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) self.colors = list( map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors)) #---------------------------------------------------# # 检测图片 #---------------------------------------------------# def detect_image(self, image): image_shape = np.array(np.shape(image)[0:2]) crop_img = np.array( letterbox_image(image, (image_sizes[self.phi], image_sizes[self.phi]))) photo = np.array(crop_img, dtype=np.float32) photo = np.transpose(preprocess_input(photo), (2, 0, 1)) images = [] images.append(photo) images = np.asarray(images) with torch.no_grad(): images = torch.from_numpy(images) if self.cuda: images = images.cuda() _, regression, classification, anchors = self.net(images) regression = decodebox(regression, anchors, images) detection = torch.cat([regression, classification], axis=-1) batch_detections = non_max_suppression(detection, len(self.class_names), conf_thres=self.confidence, nms_thres=0.2) try: batch_detections = batch_detections[0].cpu().numpy() except: print('置信度过高,没有找到符合条件的目标') return image, 0, 0 top_index = batch_detections[:, 4] > self.confidence top_conf = batch_detections[top_index, 4] 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 = efficientdet_correct_boxes( top_ymin, top_xmin, top_ymax, top_xmax, np.array([image_sizes[self.phi], image_sizes[self.phi]]), image_shape) font = ImageFont.truetype(font='utils/simhei.ttf', size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32')) thickness = (np.shape(image)[0] + np.shape(image)[1]) // image_sizes[self.phi] for i, c in enumerate(top_label): predicted_class = self.class_names[c] score = top_conf[i] top, left, bottom, right = boxes[i] top = top - 5 left = left - 5 bottom = bottom + 5 right = right + 5 top = max(0, np.floor(top + 0.5).astype('int32')) left = max(0, np.floor(left + 0.5).astype('int32')) bottom = min( np.shape(image)[0], np.floor(bottom + 0.5).astype('int32')) right = min( np.shape(image)[1], np.floor(right + 0.5).astype('int32')) # 画框框 label = '{} {:.2f}'.format(predicted_class, score) draw = ImageDraw.Draw(image) label_size = draw.textsize(label, font) label = label.encode('utf-8') # print(label) if top - label_size[1] >= 0: text_origin = np.array([left, top - label_size[1]]) else: text_origin = np.array([left, top + 1]) for i in range(thickness): draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[self.class_names.index( predicted_class)]) draw.rectangle( [tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[self.class_names.index(predicted_class)]) draw.text(text_origin, str(label, 'UTF-8'), fill=(0, 0, 0), font=font) del draw return image, predicted_class, score
class Efficientdet(object): _defaults = { #--------------------------------------------------------------------------# # 使用自己训练好的模型进行预测一定要修改model_path和classes_path! # model_path指向logs文件夹下的权值文件,classes_path指向model_data下的txt # # 训练好后logs文件夹下存在多个权值文件,选择验证集损失较低的即可。 # 验证集损失较低不代表mAP较高,仅代表该权值在验证集上泛化性能较好。 # 如果出现shape不匹配,同时要注意训练时的model_path和classes_path参数的修改 #--------------------------------------------------------------------------# "model_path" : 'model_data/efficientdet-d0.pth', "classes_path" : 'model_data/coco_classes.txt', #---------------------------------------------------------------------# # 用于选择所使用的模型的版本,0-7 #---------------------------------------------------------------------# "phi" : 0, #---------------------------------------------------------------------# # 只有得分大于置信度的预测框会被保留下来 #---------------------------------------------------------------------# "confidence" : 0.3, #---------------------------------------------------------------------# # 非极大抑制所用到的nms_iou大小 #---------------------------------------------------------------------# "nms_iou" : 0.3, #---------------------------------------------------------------------# # 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize, # 在多次测试后,发现关闭letterbox_image直接resize的效果更好 #---------------------------------------------------------------------# "letterbox_image" : False, #---------------------------------------------------------------------# # 是否使用Cuda # 没有GPU可以设置成False #---------------------------------------------------------------------# "cuda" : True } @classmethod def get_defaults(cls, n): if n in cls._defaults: return cls._defaults[n] else: return "Unrecognized attribute name '" + n + "'" #---------------------------------------------------# # 初始化Efficientdet #---------------------------------------------------# def __init__(self, **kwargs): self.__dict__.update(self._defaults) for name, value in kwargs.items(): setattr(self, name, value) self.input_shape = [image_sizes[self.phi], image_sizes[self.phi]] #---------------------------------------------------# # 计算总的类的数量 #---------------------------------------------------# self.class_names, self.num_classes = get_classes(self.classes_path) #---------------------------------------------------# # 画框设置不同的颜色 #---------------------------------------------------# hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)] self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors)) self.generate() #---------------------------------------------------# # 载入模型 #---------------------------------------------------# def generate(self): #----------------------------------------# # 创建Efficientdet模型 #----------------------------------------# self.net = EfficientDetBackbone(self.num_classes, self.phi) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.net.load_state_dict(torch.load(self.model_path, map_location=device)) self.net = self.net.eval() print('{} model, anchors, and classes loaded.'.format(self.model_path)) if self.cuda: self.net = nn.DataParallel(self.net) self.net = self.net.cuda() #---------------------------------------------------# # 检测图片 #---------------------------------------------------# def detect_image(self, image, crop = False): #---------------------------------------------------# # 计算输入图片的高和宽 #---------------------------------------------------# image_shape = np.array(np.shape(image)[0:2]) #---------------------------------------------------------# # 在这里将图像转换成RGB图像,防止灰度图在预测时报错。 # 代码仅仅支持RGB图像的预测,所有其它类型的图像都会转化成RGB #---------------------------------------------------------# image = cvtColor(image) #---------------------------------------------------------# # 给图像增加灰条,实现不失真的resize # 也可以直接resize进行识别 #---------------------------------------------------------# image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image) #---------------------------------------------------------# # 添加上batch_size维度,图片预处理,归一化。 #---------------------------------------------------------# image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0) with torch.no_grad(): images = torch.from_numpy(image_data) if self.cuda: images = images.cuda() #---------------------------------------------------------# # 传入网络当中进行预测 #---------------------------------------------------------# _, regression, classification, anchors = self.net(images) #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# outputs = decodebox(regression, anchors, self.input_shape) results = non_max_suppression(torch.cat([outputs, classification], axis=-1), self.input_shape, image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou) if results[0] is None: return image top_label = np.array(results[0][:, 5], dtype = 'int32') top_conf = results[0][:, 4] top_boxes = results[0][:, :4] #---------------------------------------------------------# # 设置字体与边框厚度 #---------------------------------------------------------# font = ImageFont.truetype(font='model_data/simhei.ttf', size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32')) thickness = int(max((image.size[0] + image.size[1]) // np.mean(self.input_shape), 1)) #---------------------------------------------------------# # 是否进行目标的裁剪 #---------------------------------------------------------# if crop: for i, c in list(enumerate(top_label)): top, left, bottom, right = top_boxes[i] top = max(0, np.floor(top).astype('int32')) left = max(0, np.floor(left).astype('int32')) bottom = min(image.size[1], np.floor(bottom).astype('int32')) right = min(image.size[0], np.floor(right).astype('int32')) dir_save_path = "img_crop" if not os.path.exists(dir_save_path): os.makedirs(dir_save_path) crop_image = image.crop([left, top, right, bottom]) crop_image.save(os.path.join(dir_save_path, "crop_" + str(i) + ".png"), quality=95, subsampling=0) print("save crop_" + str(i) + ".png to " + dir_save_path) #---------------------------------------------------------# # 图像绘制 #---------------------------------------------------------# for i, c in list(enumerate(top_label)): predicted_class = self.class_names[int(c)] box = top_boxes[i] score = top_conf[i] top, left, bottom, right = box top = max(0, np.floor(top).astype('int32')) left = max(0, np.floor(left).astype('int32')) bottom = min(image.size[1], np.floor(bottom).astype('int32')) right = min(image.size[0], np.floor(right).astype('int32')) label = '{} {:.2f}'.format(predicted_class, score) draw = ImageDraw.Draw(image) label_size = draw.textsize(label, font) label = label.encode('utf-8') print(label, top, left, bottom, right) if top - label_size[1] >= 0: text_origin = np.array([left, top - label_size[1]]) else: text_origin = np.array([left, top + 1]) for i in range(thickness): draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[c]) draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c]) draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font) del draw return image def get_FPS(self, image, test_interval): image_shape = np.array(np.shape(image)[0:2]) #---------------------------------------------------------# # 在这里将图像转换成RGB图像,防止灰度图在预测时报错。 # 代码仅仅支持RGB图像的预测,所有其它类型的图像都会转化成RGB #---------------------------------------------------------# image = cvtColor(image) #---------------------------------------------------------# # 给图像增加灰条,实现不失真的resize # 也可以直接resize进行识别 #---------------------------------------------------------# image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image) #---------------------------------------------------------# # 添加上batch_size维度,图片预处理,归一化。 #---------------------------------------------------------# image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0) with torch.no_grad(): images = torch.from_numpy(image_data) if self.cuda: images = images.cuda() #---------------------------------------------------------# # 传入网络当中进行预测 #---------------------------------------------------------# _, regression, classification, anchors = self.net(images) #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# outputs = decodebox(regression, anchors, self.input_shape) results = non_max_suppression(torch.cat([outputs, classification], axis=-1), self.input_shape, image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou) t1 = time.time() for _ in range(test_interval): with torch.no_grad(): #---------------------------------------------------------# # 传入网络当中进行预测 #---------------------------------------------------------# _, regression, classification, anchors = self.net(images) #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# outputs = decodebox(regression, anchors, self.input_shape) results = non_max_suppression(torch.cat([outputs, classification], axis=-1), self.input_shape, image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou) t2 = time.time() tact_time = (t2 - t1) / test_interval return tact_time #---------------------------------------------------# # 检测图片 #---------------------------------------------------# def get_map_txt(self, image_id, image, class_names, map_out_path): f = open(os.path.join(map_out_path, "detection-results/"+image_id+".txt"),"w") image_shape = np.array(np.shape(image)[0:2]) #---------------------------------------------------------# # 在这里将图像转换成RGB图像,防止灰度图在预测时报错。 # 代码仅仅支持RGB图像的预测,所有其它类型的图像都会转化成RGB #---------------------------------------------------------# image = cvtColor(image) #---------------------------------------------------------# # 给图像增加灰条,实现不失真的resize # 也可以直接resize进行识别 #---------------------------------------------------------# image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image) #---------------------------------------------------------# # 添加上batch_size维度,图片预处理,归一化。 #---------------------------------------------------------# image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0) with torch.no_grad(): images = torch.from_numpy(image_data) if self.cuda: images = images.cuda() #---------------------------------------------------------# # 传入网络当中进行预测 #---------------------------------------------------------# _, regression, classification, anchors = self.net(images) #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# outputs = decodebox(regression, anchors, self.input_shape) results = non_max_suppression(torch.cat([outputs, classification], axis=-1), self.input_shape, image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou) if results[0] is None: return top_label = np.array(results[0][:, 5], dtype = 'int32') top_conf = results[0][:, 4] top_boxes = results[0][:, :4] for i, c in list(enumerate(top_label)): predicted_class = self.class_names[int(c)] box = top_boxes[i] score = str(top_conf[i]) top, left, bottom, right = box if predicted_class not in class_names: continue f.write("%s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)),str(int(bottom)))) f.close() return