def __call__(self, in_data): img, label = in_data _, H, W = img.shape img = preprocess(img, self.min_size, self.max_size) _, o_H, o_W = img.shape scale = o_H / H # horizontally flip img, params = util.random_flip(img, x_random=True, return_param=True) return img, label, scale
def __call__(self, in_data): img, bbox, label = in_data _, H, W = img.shape img = preprocess(img, self.min_size, self.max_size)#对图像缩放处理 _, o_H, o_W = img.shape scale = o_H / H bbox = util.resize_bbox(bbox, (H, W), (o_H, o_W))#对bbox处理 #图像随机水平翻转 img, params = util.random_flip(img, x_random=True, return_param=True) #对应的bbox bbox = util.flip_bbox(bbox, (o_H, o_W), x_flip=params['x_flip']) return img, bbox, label, scale
def __call__(self, in_data): img, bbox, label = in_data _, H, W = img.shape img = preprocess(img, self.min_size, self.max_size) _, o_H, o_W = img.shape scale = o_H / H bbox = util.resize_bbox(bbox, (H, W), (o_H, o_W)) # horizontally flip img, params = util.random_flip(img, x_random=True, return_param=True) bbox = util.flip_bbox(bbox, (o_H, o_W), x_flip=params['x_flip']) return img, bbox, label, scale
def __call__(self, in_data): img, bbox, label = in_data _, H, W = img.shape img = preprocess(img, self.min_size, self.max_size) _, o_H, o_W = img.shape # 缩放后的图像大小 scale = o_H / H # 缩放比例 bbox = util.resize_bbox(bbox, (H, W), (o_H, o_W)) # 按比例缩放包围盒 # horizontally flip, 将图像随机水平翻转 img, params = util.random_flip(img, x_random=True, return_param=True) # 将包围盒进行与图像相同的水平翻转 bbox = util.flip_bbox(bbox, (o_H, o_W), x_flip=params['x_flip']) return img, bbox, label, scale
def __call__(self, data): img, bbox, label = data _, H, W = img.shape img = preprocess(img) _, o_H, o_W = img.shape bbox = resize_bbox(bbox, (H, W), (o_H, o_W)) #randomly horizontally flip img, x_flip = random_flip(img) if x_flip: bbox = flip_bbox(bbox, (o_H, o_W)) scale = o_H / H return img, bbox, label, scale
def __call__(self, in_data): img, bbox, label = in_data _, H, W = img.shape img = preprocess(img, self.min_size, self.max_size) #预处理图片 _, o_H, o_W = img.shape scale = o_H / H #预处理图片中的bbox,对相应的bounding boxes 也进行同等尺度的缩放。 bbox = util.resize_bbox(bbox, (H, W), (o_H, o_W)) # horizontally flip水平翻转 img, params = util.random_flip(img, x_random=True, return_param=True) bbox = util.flip_bbox(bbox, (o_H, o_W), x_flip=params['x_flip']) return img, bbox, label, scale
def __call__(self, in_data): img, bbox, label = in_data _, H, W = img.shape img = preprocess(img, self.min_size, self.max_size) # 将图片进行最小最大化放缩然后进行归一化 _, o_H, o_W = img.shape scale = o_H / H # 放缩前后相除,得出放缩比因子 bbox = util.resize_bbox(bbox, (H, W), (o_H, o_W)) # 重新调整bboxes框的大小 # 水平翻转 # 进行图片的随机反转,图片旋转不变性,增强网络的鲁棒性! img, params = util.random_flip(img, x_random=True, return_param=True) # 同样的对bboxes进行随机反转 bbox = util.flip_bbox(bbox, (o_H, o_W), x_flip=params['x_flip']) return img, bbox, label, scale
def __call__(self, in_data): img, bbox, label = in_data _, H, W = img.shape #(3,h,w) img = preprocess(img, self.min_size, self.max_size) #(3,h*scale,w*scale) _, o_H, o_W = img.shape scale = o_H / H #(得出缩放比因子) #scale bbox = util.resize_bbox(bbox, (H, W), (o_H, o_W)) # # horizontally flip img, params = util.random_flip( img, x_random=True, return_param=True) #jiang pictures shuiping fanzhuang bbox = util.flip_bbox(bbox, (o_H, o_W), x_flip=params['x_flip']) return img, bbox, label, scale
def __call__(self, in_data): img, bbox, label = in_data _, H, W = img.shape # 图像等比例缩放 img = preprocess(img, self.min_size, self.max_size) _, o_H, o_W = img.shape # 得出缩放比因子 scale = o_H / H # bbox按照与原图等比例缩放 bbox = util.resize_bbox(bbox, (H, W), (o_H, o_W)) # 将图片进行随机水平翻转,没有进行垂直翻转 img, params = util.random_flip( img, x_random=True, return_param=True) # 同样地将bbox进行与对应图片同样的水平翻转 bbox = util.flip_bbox( bbox, (o_H, o_W), x_flip=params['x_flip']) return img, bbox, label, scale
def __call__(self, in_data): img, bbox, label = in_data _, H, W = img.shape # print("before preprocessing:", img.shape, img[:, 100: 200, 100: 200]) img = preprocess(img, self.min_size, self.max_size) # print("after preprocessing:", img.shape, img[:, 100: 200, 100: 200]) _, o_H, o_W = img.shape # TODO change the defenition of scale. # warning: might have issue. scale = o_H / H # print("bbox pre: ", bbox) bbox = util.resize_bbox(bbox, (H, W), (o_H, o_W)) # print("bbox after: ", bbox) # horizontally flip img, params = util.random_flip(img, x_random=True, return_param=True) bbox = util.flip_bbox(bbox, (o_H, o_W), x_flip=params['x_flip']) # print("bbox after flip: ", bbox, bbox.shape) return img, bbox, label, scale
def __call__(self, in_data): img, bbox, label, depth, y_rot = in_data _, H, W = img.shape img = preprocess(img, self.min_size, self.max_size) _, o_H, o_W = img.shape scale = o_H / H bbox = util.resize_bbox(bbox, (H, W), (o_H, o_W)) # horizontally flip img, params = util.random_flip(img, x_random=True, return_param=True) bbox = util.flip_bbox(bbox, (o_H, o_W), x_flip=params['x_flip']) if params['x_flip']: for i in range(len(y_rot)): theta = float(y_rot[i]) if theta > 0: y_rot[i] = pi - theta if theta < 0: y_rot[i] = -pi - theta y_rot[i] if theta == 0: y_rot[i] = 3.14 return img, bbox, label, depth, y_rot, scale