Machine learning 大形状图像去噪自动编码器
我想为任何形状的图像创建一个去噪自动编码器。大多数解决方案的图像形状不大于(500500),而我的图像是形状的文档扫描(300020000)。我试图重塑图像并建立模型,但预测是错误的。有人能帮我吗Machine learning 大形状图像去噪自动编码器,machine-learning,image-processing,deep-learning,conv-neural-network,autoencoder,Machine Learning,Image Processing,Deep Learning,Conv Neural Network,Autoencoder,我想为任何形状的图像创建一个去噪自动编码器。大多数解决方案的图像形状不大于(500500),而我的图像是形状的文档扫描(300020000)。我试图重塑图像并建立模型,但预测是错误的。有人能帮我吗 我尝试用这里的代码构建模型,围绕图像形状进行模拟,但预测失败。我已经有了一个文档去噪器。 对于一个大的形状,不需要有一个模型,您可以简单地拆分它们,将它们馈送到模型,然后将预测的块再次合并在一起。 我的模型接受512x512形状的图像,因此我必须将图像分割为512x512块。 图像必须大于或等于512
我尝试用这里的代码构建模型,围绕图像形状进行模拟,但预测失败。我已经有了一个文档去噪器。 对于一个大的形状,不需要有一个模型,您可以简单地拆分它们,将它们馈送到模型,然后将预测的块再次合并在一起。 我的模型接受512x512形状的图像,因此我必须将图像分割为512x512块。 图像必须大于或等于512x512。 如果图像较小,则只需调整其大小或将其调整为512x512形状即可
def split_page(page):
chunk_size = (512, 512)
main_size = page.shape[:2]
chunks=[]
chunk_grid = tuple(np.array(main_size)//np.array(chunk_size))
extra_chunk = tuple(np.array(main_size)%np.array(chunk_size))
for yc in range(chunk_grid[0]):
row = []
for xc in range(chunk_grid[1]):
chunk = page[yc*chunk_size[0]:yc*chunk_size[0]+chunk_size[0], xc*chunk_size[1]: xc*chunk_size[1]+chunk_size[1]]
row.append(chunk)
if extra_chunk[1]:
chunk = page[yc*chunk_size[0]:yc*chunk_size[0]+chunk_size[0], page.shape[1]-chunk_size[1]:page.shape[1]]
row.append(chunk)
chunks.append(row)
if extra_chunk[0]:
row = []
for xc in range(chunk_grid[1]):
chunk = page[page.shape[0]-chunk_size[0]:page.shape[0], xc*chunk_size[1]: xc*chunk_size[1]+chunk_size[1]]
row.append(chunk)
if extra_chunk[1]:
chunk = page[page.shape[0]-chunk_size[0]:page.shape[0], page.shape[1]-chunk_size[1]:page.shape[1]]
row.append(chunk)
chunks.append(row)
return chunks, page.shape[:2]
def merge_chunks(chunks, osize):
extra = np.array(osize)%512
page = np.ones(osize)
for i, row in enumerate(chunks[:-1]):
for j, chunk in enumerate(row[:-1]):
page[i*512:i*512+512,j*512:j*512+512]=chunk
page[i*512:i*512+512,osize[1]-512:osize[1]]=chunks[i,-1]
if extra[0]:
for j, chunk in enumerate(chunks[-1][:-1]):
page[osize[0]-512:osize[0],j*512:j*512+512]=chunk
page[osize[0]-512:osize[0],osize[1]-512:osize[1]]=chunks[-1,-1]
else:
for j, chunk in enumerate(chunks[-1][:-1]):
page[osize[0]-512:osize[0],j*512:j*512+512]=chunk
page[osize[0]-512:osize[0],osize[1]-512:osize[1]]=chunks[-1,-1]
return page
def denoise(chunk):
chunk = chunk.reshape(1,512,512,1)/255.
denoised = model.predict(chunk).reshape(512,512)*255.
return denoised
def denoise_page(page):
chunks, osize= split_page(page)
chunks = np.array(chunks)
denoised_chunks = np.ones(chunks.shape)
for i, row in enumerate(chunks):
for j, chunk in enumerate(row):
denoised = denoise(chunk)
denoised_chunks[i][j]=denoised
denoised_page = merge_chunks(denoised_chunks, osize)
return denoised_page
我已经有了一个文档去噪器。 对于一个大的形状,不需要有一个模型,您可以简单地拆分它们,将它们馈送到模型,然后将预测的块再次合并在一起。 我的模型接受512x512形状的图像,因此我必须将图像分割为512x512块。 图像必须大于或等于512x512。 如果图像较小,则只需调整其大小或将其调整为512x512形状即可
def split_page(page):
chunk_size = (512, 512)
main_size = page.shape[:2]
chunks=[]
chunk_grid = tuple(np.array(main_size)//np.array(chunk_size))
extra_chunk = tuple(np.array(main_size)%np.array(chunk_size))
for yc in range(chunk_grid[0]):
row = []
for xc in range(chunk_grid[1]):
chunk = page[yc*chunk_size[0]:yc*chunk_size[0]+chunk_size[0], xc*chunk_size[1]: xc*chunk_size[1]+chunk_size[1]]
row.append(chunk)
if extra_chunk[1]:
chunk = page[yc*chunk_size[0]:yc*chunk_size[0]+chunk_size[0], page.shape[1]-chunk_size[1]:page.shape[1]]
row.append(chunk)
chunks.append(row)
if extra_chunk[0]:
row = []
for xc in range(chunk_grid[1]):
chunk = page[page.shape[0]-chunk_size[0]:page.shape[0], xc*chunk_size[1]: xc*chunk_size[1]+chunk_size[1]]
row.append(chunk)
if extra_chunk[1]:
chunk = page[page.shape[0]-chunk_size[0]:page.shape[0], page.shape[1]-chunk_size[1]:page.shape[1]]
row.append(chunk)
chunks.append(row)
return chunks, page.shape[:2]
def merge_chunks(chunks, osize):
extra = np.array(osize)%512
page = np.ones(osize)
for i, row in enumerate(chunks[:-1]):
for j, chunk in enumerate(row[:-1]):
page[i*512:i*512+512,j*512:j*512+512]=chunk
page[i*512:i*512+512,osize[1]-512:osize[1]]=chunks[i,-1]
if extra[0]:
for j, chunk in enumerate(chunks[-1][:-1]):
page[osize[0]-512:osize[0],j*512:j*512+512]=chunk
page[osize[0]-512:osize[0],osize[1]-512:osize[1]]=chunks[-1,-1]
else:
for j, chunk in enumerate(chunks[-1][:-1]):
page[osize[0]-512:osize[0],j*512:j*512+512]=chunk
page[osize[0]-512:osize[0],osize[1]-512:osize[1]]=chunks[-1,-1]
return page
def denoise(chunk):
chunk = chunk.reshape(1,512,512,1)/255.
denoised = model.predict(chunk).reshape(512,512)*255.
return denoised
def denoise_page(page):
chunks, osize= split_page(page)
chunks = np.array(chunks)
denoised_chunks = np.ones(chunks.shape)
for i, row in enumerate(chunks):
for j, chunk in enumerate(row):
denoised = denoise(chunk)
denoised_chunks[i][j]=denoised
denoised_page = merge_chunks(denoised_chunks, osize)
return denoised_page
试着阅读并编辑你的问题:如何提出完美的问题:试着阅读并编辑你的问题:如何提出完美的问题: