123 lines
4.2 KiB
Python
123 lines
4.2 KiB
Python
import os
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import torch
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import cv2
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import argparse
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import numpy as np
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from pprint import pprint
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from tqdm import tqdm
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from mmseg.apis import init_model, inference_model
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"""
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"""
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DEVICE = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
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# 测试图像所在文件夹
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# IMAGE_FILE_PATH = r"D:\project\UAV_model/need_prediction"
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IMAGE_FILE_PATH = r"C:\Users\14867\Desktop\seg_tiff"
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# 模型训练结果的config配置文件路径
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CONFIG = r'D:\project\UAV_model/tools/work_dirs/unetformer_UAV_6000X4000/unetformer_UAV_6000X4000.py'
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# 模型训练结果的权重文件路径
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CHECKPOINT = r'D:\project\UAV_model/tools/work_dirs/unetformer_UAV_6000X4000/best_mIoU_iter_20000.pth'
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# 模型推理测试结果的保存路径，每个模型的推理结果都保存在`{save_dir}/{模型config同名文件夹}`下，如文末图片所示。
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SAVE_DIR = r"D:\project\UAV_model/prediction_results"
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def parse_args():
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parser = argparse.ArgumentParser(description='Visualize CAM')
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parser.add_argument('--img', default=IMAGE_FILE_PATH, help='Image file')
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parser.add_argument('--config', default=CONFIG, help='Config file')
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parser.add_argument('--checkpoint', default=CHECKPOINT, help='Checkpoint file')
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parser.add_argument('--device', default=DEVICE, help='device')
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parser.add_argument('--save_dir', default=SAVE_DIR, help='save_dir')
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args = parser.parse_args()
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return args
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def make_full_path(root_list, root_path):
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file_full_path_list = []
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for filename in root_list:
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file_full_path = os.path.join(root_path, filename)
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file_full_path_list.append(file_full_path)
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return file_full_path_list
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def read_filepath(root):
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from natsort import natsorted
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test_image_list = natsorted(os.listdir(root))
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test_image_full_path_list = make_full_path(test_image_list, root)
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return test_image_full_path_list
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from PIL import Image
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def save_colored_prediction(predictions, save_path):
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# color_map = [
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# [0, 0, 0],
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# [165, 42, 42],
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# [0, 255, 0],2
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# [255, 255, 0],
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# [0, 0, 255],
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# [128, 128, 128],
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# [0, 255, 255]
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# ]
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color_map = [
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[255, 0, 0], # 类别0: 红色
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[0, 255, 0], # 类别1: 绿色
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[0, 0, 255], # 类别2: 蓝色
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[255, 255, 0], # 类别3: 黄色
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[255, 0, 255], # 类别4: 品红
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[0, 255, 255], # 类别5: 青色
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[128, 0, 128] # 类别6: 紫色
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]
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# 类别 0：黑色 ；背景
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# 类别 1：棕色 ；荒地
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# 类别 2：绿色 ；林地
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# 类别 3：黄色 ；农田
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# 类别 4：蓝色 ；水域
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# 类别 5：灰色 ；道路
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# 类别 6：青色 ；建筑用地
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# 创建一个空的 RGB 图像
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colored_image = np.zeros((predictions.shape[0], predictions.shape[1], 3), dtype=np.uint8)
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# 将每个类别的颜色赋值到图像
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for class_id in range(len(color_map)):
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colored_image[predictions == class_id] = color_map[class_id]
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# 转换为 PIL 图像并保存
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image = Image.fromarray(colored_image)
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image.save(save_path)
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def main():
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args = parse_args()
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model_mmseg = init_model(args.config, args.checkpoint, device=args.device)
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for imgs in tqdm(read_filepath(args.img)):
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result = inference_model(model_mmseg, imgs)
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pred_mask = result.pred_sem_seg.data.squeeze(0).detach().cpu().numpy().astype(np.uint8)
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save_path = os.path.join(args.save_dir, f"{os.path.basename(args.config).split('.')[0]}")
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if not os.path.exists(save_path):
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os.makedirs(save_path)
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saves_path=os.path.join(save_path, f"{os.path.basename(result.img_path).split('.')[0]}.png")
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save_colored_prediction(pred_mask,saves_path)
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#
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# pred_mask[pred_mask == 1] = 255
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# save_path = os.path.join(args.save_dir, f"{os.path.basename(args.config).split('.')[0]}")
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#
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# if not os.path.exists(save_path):
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# os.makedirs(save_path)
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#
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# cv2.imwrite(os.path.join(save_path, f"{os.path.basename(result.img_path).split('.')[0]}.png"), pred_mask,
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# [cv2.IMWRITE_PNG_COMPRESSION, 0])
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if __name__ == '__main__':
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main()
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