图像&视频编辑工具箱MMEditing使用示例：图像超分辨率(super-resolution)

      MMEditing的介绍及安装参考：https://blog.csdn.net/fengbingchun/article/details/126331541，这里给出图像超分的测试代码，论文：《Learning Continuous Image Representation with Local Implicit Image Function》：

      (1).下载模型(checkpoint)：

def download_checkpoint(path, name, url):
	if os.path.isfile(path+name) == False:
		print("checkpoint(model) file does not exist, now download ...")
		subprocess.run(["wget", "-P", path, url])
 
path = "../../data/model/"
checkpoint = "liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth"
url = "https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth"
download_checkpoint(path, checkpoint, url)

      (2).根据配置文件和checkpoint文件构建模型：

config = "../../src/mmediting/configs/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k.py"
model = init_model(config, path+checkpoint, device)

      (3).准备测试图像：源图来自于MMEditing

image_path = "../../src/mmediting/tests/data/gt/"
image_name = "baboon.png"

     

      (4).进行推理产生超分图：

result = restoration_inference(model, image)
print(f"result shape: {result.shape}; max value: {torch.max(result)}") # result shape: torch.Size([1, 3, 1920, 2000]); max value: 1.0

      (5).显示执行结果及保存图像：

def crop_save_image(srcimage, dstimage, name):
	crop_height, crop_width = int(srcimage.shape[1]/2), int(srcimage.shape[0]/2)
	print(f"crop height: {crop_height}; crop width: {crop_width}, data type: {type(crop_height)}")
 
	mat = cv2.resize(srcimage, (dstimage.shape[1], dstimage.shape[0]))
	srccrop = mat[0:crop_height, 0:crop_width]
	dstcrop = dstimage[0:crop_height, 0:crop_width]
 
	path = "../../data/"
	cv2.imwrite(path+"src_"+name, srccrop)
	cv2.imwrite(path+"result_"+name, dstcrop)
 
	cv2.imshow("show_src", srccrop)
	cv2.waitKey(0)
	cv2.imshow("show_result", dstcrop)
	cv2.waitKey(0)
 
result = tensor2img(result)
srcimage = cv2.imread(image)
crop_save_image(srcimage, result, "restoration_liif.png")

      结果如下图所示：左图为源图，右图为结果图，仅显示图像的部分结果

                       

       GitHub：https://github.com/fengbingchun/PyTorch_Test