import numpy as np from PIL import Image #---------------------------------------------------------# # 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 #---------------------------------------------------------# def cvtColor(image): if len(np.shape(image)) == 3 and np.shape(image)[2] == 3: return image else: image = image.convert('RGB') return image #---------------------------------------------------# # 对输入图像进行resize #---------------------------------------------------# def resize_image(image, size, letterbox_image): iw, ih = image.size w, h = size if letterbox_image: scale = min(w/iw, h/ih) nw = int(iw*scale) nh = int(ih*scale) image = image.resize((nw,nh), Image.BICUBIC) new_image = Image.new('RGB', size, (128,128,128)) new_image.paste(image, ((w-nw)//2, (h-nh)//2)) else: new_image = image.resize((w, h), Image.BICUBIC) return new_image #---------------------------------------------------# # 获得类 #---------------------------------------------------# def get_classes(classes_path): with open(classes_path, encoding='utf-8') as f: class_names = f.readlines() class_names = [c.strip() for c in class_names] return class_names, len(class_names) #---------------------------------------------------# # 获得先验框 #---------------------------------------------------# def get_anchors(anchors_path): '''loads the anchors from a file''' with open(anchors_path, encoding='utf-8') as f: anchors = f.readline() anchors = [float(x) for x in anchors.split(',')] anchors = np.array(anchors).reshape(-1, 2) return anchors, len(anchors) #---------------------------------------------------# # 获得学习率 #---------------------------------------------------# def get_lr(optimizer): for param_group in optimizer.param_groups: return param_group['lr'] def preprocess_input(image): image /= 255.0 return image