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| 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 | |