class SSD(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/ssd_weights.h5', "classes_path" : 'model_data/voc_classes.txt', #---------------------------------------------------------------------# # 用于预测的图像大小,和train时使用同一个即可 #---------------------------------------------------------------------# "input_shape" : [300, 300], #---------------------------------------------------------------------# # 只有得分大于置信度的预测框会被保留下来 #---------------------------------------------------------------------# "confidence" : 0.5, #---------------------------------------------------------------------# # 非极大抑制所用到的nms_iou大小 #---------------------------------------------------------------------# "nms_iou" : 0.45, #---------------------------------------------------------------------# # 用于指定先验框的大小 #---------------------------------------------------------------------# 'anchors_size' : [30, 60, 111, 162, 213, 264, 315], #---------------------------------------------------------------------# # 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, } @classmethod def get_defaults(cls, n): if n in cls._defaults: return cls._defaults[n] else: return "Unrecognized attribute name '" + n + "'" #---------------------------------------------------# # 初始化ssd #---------------------------------------------------# def __init__(self, **kwargs): self.__dict__.update(self._defaults) for name, value in kwargs.items(): setattr(self, name, value) #---------------------------------------------------# # 计算总的类的数量 #---------------------------------------------------# self.class_names, self.num_classes = get_classes(self.classes_path) self.anchors = get_anchors(self.input_shape, self.anchors_size) self.num_classes = self.num_classes + 1 #---------------------------------------------------# # 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.bbox_util = BBoxUtility(self.num_classes, nms_thresh=self.nms_iou) self.generate() show_config(**self._defaults) #---------------------------------------------------# # 载入模型 #---------------------------------------------------# def generate(self): model_path = os.path.expanduser(self.model_path) assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.' #-------------------------------# # 载入模型与权值 #-------------------------------# self.ssd = SSD300([self.input_shape[0], self.input_shape[1], 3], self.num_classes) self.ssd.load_weights(self.model_path, by_name=True) print('{} model, anchors, and classes loaded.'.format(model_path)) @tf.function def get_pred(self, photo): preds = self.ssd(photo, training=False) return preds #---------------------------------------------------# # 检测图片 #---------------------------------------------------# def detect_image(self, image, crop = False, count = False): 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 = preprocess_input(np.expand_dims(np.array(image_data, dtype='float32'), 0)) preds = self.get_pred(image_data).numpy() #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# results = self.bbox_util.decode_box(preds, self.anchors, image_shape, self.input_shape, self.letterbox_image, confidence=self.confidence) #--------------------------------------# # 如果没有检测到物体,则返回原图 #--------------------------------------# if len(results[0])<=0: return image top_label = np.array(results[0][:, 4], dtype = 'int32') top_conf = results[0][:, 5] 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 * np.shape(image)[1] + 0.5).astype('int32')) thickness = max((np.shape(image)[0] + np.shape(image)[1]) // self.input_shape[0], 1) #---------------------------------------------------------# # 计数 #---------------------------------------------------------# if count: print("top_label:", top_label) classes_nums = np.zeros([self.num_classes]) for i in range(self.num_classes): num = np.sum(top_label == i) if num > 0: print(self.class_names[i], " : ", num) classes_nums[i] = num print("classes_nums:", classes_nums) #---------------------------------------------------------# # 是否进行目标的裁剪 #---------------------------------------------------------# if crop: for i, c in list(enumerate(top_boxes)): 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 = preprocess_input(np.expand_dims(np.array(image_data, dtype='float32'), 0)) preds = self.get_pred(image_data).numpy() #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# results = self.bbox_util.decode_box(preds, self.anchors, image_shape, self.input_shape, self.letterbox_image, confidence=self.confidence) t1 = time.time() for _ in range(test_interval): preds = self.get_pred(image_data).numpy() #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# results = self.bbox_util.decode_box(preds, self.anchors, image_shape, self.input_shape, self.letterbox_image, confidence=self.confidence) t2 = time.time() tact_time = (t2 - t1) / test_interval return tact_time 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 = preprocess_input(np.expand_dims(np.array(image_data, dtype='float32'), 0)) preds = self.get_pred(image_data).numpy() #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# results = self.bbox_util.decode_box(preds, self.anchors, image_shape, self.input_shape, self.letterbox_image, confidence=self.confidence) #--------------------------------------# # 如果没有检测到物体,则返回原图 #--------------------------------------# if len(results[0])<=0: return top_label = results[0][:, 4] top_conf = results[0][:, 5] 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
class SSD(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/ssd_weights.pth', "classes_path": 'model_data/voc_classes.txt', #---------------------------------------------------------------------# # 用于预测的图像大小,和train时使用同一个即可 #---------------------------------------------------------------------# "input_shape": [300, 300], #-------------------------------# # 主干网络的选择 # vgg或者mobilenetv2 #-------------------------------# "backbone": "vgg", #---------------------------------------------------------------------# # 只有得分大于置信度的预测框会被保留下来 #---------------------------------------------------------------------# "confidence": 0.5, #---------------------------------------------------------------------# # 非极大抑制所用到的nms_iou大小 #---------------------------------------------------------------------# "nms_iou": 0.45, #---------------------------------------------------------------------# # 用于指定先验框的大小 #---------------------------------------------------------------------# 'anchors_size': [30, 60, 111, 162, 213, 264, 315], #---------------------------------------------------------------------# # 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ssd #---------------------------------------------------# def __init__(self, **kwargs): self.__dict__.update(self._defaults) for name, value in kwargs.items(): setattr(self, name, value) #---------------------------------------------------# # 计算总的类的数量 #---------------------------------------------------# self.class_names, self.num_classes = get_classes(self.classes_path) self.anchors = torch.from_numpy( get_anchors(self.input_shape, self.anchors_size, self.backbone)).type(torch.FloatTensor) if self.cuda: self.anchors = self.anchors.cuda() self.num_classes = self.num_classes + 1 #---------------------------------------------------# # 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.bbox_util = BBoxUtility(self.num_classes) self.generate() show_config(**self._defaults) #---------------------------------------------------# # 载入模型 #---------------------------------------------------# def generate(self, onnx=False): #-------------------------------# # 载入模型与权值 #-------------------------------# self.net = SSD300(self.num_classes, self.backbone) 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 not onnx: if self.cuda: self.net = torch.nn.DataParallel(self.net) self.net = self.net.cuda() #---------------------------------------------------# # 检测图片 #---------------------------------------------------# def detect_image(self, image, crop=False, count=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(): #---------------------------------------------------# # 转化成torch的形式 #---------------------------------------------------# images = torch.from_numpy(image_data).type(torch.FloatTensor) if self.cuda: images = images.cuda() #---------------------------------------------------------# # 将图像输入网络当中进行预测! #---------------------------------------------------------# outputs = self.net(images) #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# results = self.bbox_util.decode_box(outputs, self.anchors, image_shape, self.input_shape, self.letterbox_image, nms_iou=self.nms_iou, confidence=self.confidence) #--------------------------------------# # 如果没有检测到物体,则返回原图 #--------------------------------------# if len(results[0]) <= 0: return image top_label = np.array(results[0][:, 4], dtype='int32') top_conf = results[0][:, 5] 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 * np.shape(image)[1] + 0.5).astype('int32')) thickness = max( (np.shape(image)[0] + np.shape(image)[1]) // self.input_shape[0], 1) #---------------------------------------------------------# # 计数 #---------------------------------------------------------# if count: print("top_label:", top_label) classes_nums = np.zeros([self.num_classes]) for i in range(self.num_classes): num = np.sum(top_label == i) if num > 0: print(self.class_names[i], " : ", num) classes_nums[i] = num print("classes_nums:", classes_nums) #---------------------------------------------------------# # 是否进行目标的裁剪 #---------------------------------------------------------# if crop: for i, c in list(enumerate(top_boxes)): 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): #---------------------------------------------------# # 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(): #---------------------------------------------------# # 转化成torch的形式 #---------------------------------------------------# images = torch.from_numpy(image_data).type(torch.FloatTensor) if self.cuda: images = images.cuda() #---------------------------------------------------------# # 将图像输入网络当中进行预测! #---------------------------------------------------------# outputs = self.net(images) #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# results = self.bbox_util.decode_box(outputs, self.anchors, image_shape, self.input_shape, self.letterbox_image, nms_iou=self.nms_iou, confidence=self.confidence) t1 = time.time() for _ in range(test_interval): with torch.no_grad(): #---------------------------------------------------------# # 将图像输入网络当中进行预测! #---------------------------------------------------------# outputs = self.net(images) #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# results = self.bbox_util.decode_box(outputs, self.anchors, image_shape, self.input_shape, self.letterbox_image, nms_iou=self.nms_iou, confidence=self.confidence) t2 = time.time() tact_time = (t2 - t1) / test_interval return tact_time def convert_to_onnx(self, simplify, model_path): import onnx self.generate(onnx=True) im = torch.zeros(1, 3, *self.input_shape).to( 'cpu') # image size(1, 3, 512, 512) BCHW input_layer_names = ["images"] output_layer_names = ["output"] # Export the model print(f'Starting export with onnx {onnx.__version__}.') torch.onnx.export(self.net, im, f=model_path, verbose=False, opset_version=12, training=torch.onnx.TrainingMode.EVAL, do_constant_folding=True, input_names=input_layer_names, output_names=output_layer_names, dynamic_axes=None) # Checks model_onnx = onnx.load(model_path) # load onnx model onnx.checker.check_model(model_onnx) # check onnx model # Simplify onnx if simplify: import onnxsim print(f'Simplifying with onnx-simplifier {onnxsim.__version__}.') model_onnx, check = onnxsim.simplify(model_onnx, dynamic_input_shape=False, input_shapes=None) assert check, 'assert check failed' onnx.save(model_onnx, model_path) print('Onnx model save as {}'.format(model_path)) 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") #---------------------------------------------------# # 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(): #---------------------------------------------------# # 转化成torch的形式 #---------------------------------------------------# images = torch.from_numpy(image_data).type(torch.FloatTensor) if self.cuda: images = images.cuda() #---------------------------------------------------------# # 将图像输入网络当中进行预测! #---------------------------------------------------------# outputs = self.net(images) #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# results = self.bbox_util.decode_box(outputs, self.anchors, image_shape, self.input_shape, self.letterbox_image, nms_iou=self.nms_iou, confidence=self.confidence) #--------------------------------------# # 如果没有检测到物体,则返回原图 #--------------------------------------# if len(results[0]) <= 0: return top_label = np.array(results[0][:, 4], dtype='int32') top_conf = results[0][:, 5] 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