338 lines
12 KiB
Python
338 lines
12 KiB
Python
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import os
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import cv2
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import glob
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from pathlib import Path
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import utils
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import argparse
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import numpy as np
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import torch
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from utils import convert_state_dict
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from models import restormer_arch
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from data.preprocess.crop_merge_image import stride_integral
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os.sys.path.append('./data/MBD/')
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from data.MBD.infer import net1_net2_infer_single_im
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# Единая константа для всех задач
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MAX_SIZE = 1800
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def _resize_if_needed(img, max_size=MAX_SIZE):
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"""Ресайз с сохранением пропорций. Возвращает (img, was_resized)."""
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h, w = img.shape[:2]
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scale = min(max_size / max(h, w), 1.0)
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if scale < 1.0:
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new_w, new_h = int(w * scale), int(h * scale)
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img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_AREA)
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print(f'[DocRes] Resized to {new_w}x{new_h}')
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return img, True
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return img, False
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def dewarp_prompt(img):
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mask = net1_net2_infer_single_im(img,'data/MBD/checkpoint/mbd.pkl')
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base_coord = utils.getBasecoord(256,256)/256
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img[mask==0]=0
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mask = cv2.resize(mask,(256,256))/255
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return img,np.concatenate((base_coord,np.expand_dims(mask,-1)),-1)
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def deshadow_prompt(img):
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h,w = img.shape[:2]
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img = cv2.resize(img,(1024,1024))
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rgb_planes = cv2.split(img)
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result_planes = []
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result_norm_planes = []
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bg_imgs = []
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for plane in rgb_planes:
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dilated_img = cv2.dilate(plane, np.ones((7,7), np.uint8))
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bg_img = cv2.medianBlur(dilated_img, 21)
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bg_imgs.append(bg_img)
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diff_img = 255 - cv2.absdiff(plane, bg_img)
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norm_img = cv2.normalize(diff_img,None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)
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result_planes.append(diff_img)
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result_norm_planes.append(norm_img)
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bg_imgs = cv2.merge(bg_imgs)
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bg_imgs = cv2.resize(bg_imgs,(w,h))
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result_norm = cv2.merge(result_norm_planes)
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result_norm[result_norm==0]=1
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shadow_map = np.clip(img.astype(float)/result_norm.astype(float)*255,0,255).astype(np.uint8)
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shadow_map = cv2.resize(shadow_map,(w,h))
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shadow_map = cv2.cvtColor(shadow_map,cv2.COLOR_BGR2GRAY)
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shadow_map = cv2.cvtColor(shadow_map,cv2.COLOR_GRAY2BGR)
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return bg_imgs
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def deblur_prompt(img):
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x = cv2.Sobel(img,cv2.CV_16S,1,0)
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y = cv2.Sobel(img,cv2.CV_16S,0,1)
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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high_frequency = cv2.addWeighted(absX,0.5,absY,0.5,0)
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high_frequency = cv2.cvtColor(high_frequency,cv2.COLOR_BGR2GRAY)
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high_frequency = cv2.cvtColor(high_frequency,cv2.COLOR_GRAY2BGR)
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return high_frequency
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def appearance_prompt(img):
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h,w = img.shape[:2]
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img = cv2.resize(img,(1024,1024))
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rgb_planes = cv2.split(img)
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result_planes = []
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result_norm_planes = []
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for plane in rgb_planes:
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dilated_img = cv2.dilate(plane, np.ones((7,7), np.uint8))
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bg_img = cv2.medianBlur(dilated_img, 21)
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diff_img = 255 - cv2.absdiff(plane, bg_img)
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norm_img = cv2.normalize(diff_img,None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)
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result_planes.append(diff_img)
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result_norm_planes.append(norm_img)
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result_norm = cv2.merge(result_norm_planes)
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result_norm = cv2.resize(result_norm,(w,h))
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return result_norm
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def binarization_promptv2(img):
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result,thresh = utils.SauvolaModBinarization(img)
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thresh = thresh.astype(np.uint8)
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result[result>155]=255
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result[result<=155]=0
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x = cv2.Sobel(img,cv2.CV_16S,1,0)
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y = cv2.Sobel(img,cv2.CV_16S,0,1)
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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high_frequency = cv2.addWeighted(absX,0.5,absY,0.5,0)
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high_frequency = cv2.cvtColor(high_frequency,cv2.COLOR_BGR2GRAY)
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return np.concatenate((np.expand_dims(thresh,-1),np.expand_dims(high_frequency,-1),np.expand_dims(result,-1)),-1)
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def dewarping(model,im_path):
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INPUT_SIZE=256
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im_org = cv2.imread(im_path)
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im_masked, prompt_org = dewarp_prompt(im_org.copy())
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h,w = im_masked.shape[:2]
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im_masked = im_masked.copy()
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im_masked = cv2.resize(im_masked,(INPUT_SIZE,INPUT_SIZE))
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im_masked = im_masked / 255.0
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im_masked = torch.from_numpy(im_masked.transpose(2,0,1)).unsqueeze(0)
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im_masked = im_masked.float().to(DEVICE)
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prompt = torch.from_numpy(prompt_org.transpose(2,0,1)).unsqueeze(0)
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prompt = prompt.float().to(DEVICE)
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in_im = torch.cat((im_masked,prompt),dim=1)
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base_coord = utils.getBasecoord(INPUT_SIZE,INPUT_SIZE)/INPUT_SIZE
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model = model.float()
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with torch.no_grad():
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pred = model(in_im)
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pred = pred[0][:2].permute(1,2,0).cpu().numpy()
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pred = pred+base_coord
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for i in range(15):
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pred = cv2.blur(pred,(3,3),borderType=cv2.BORDER_REPLICATE)
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pred = cv2.resize(pred,(w,h))*(w,h)
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pred = pred.astype(np.float32)
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out_im = cv2.remap(im_org,pred[:,:,0],pred[:,:,1],cv2.INTER_LINEAR)
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prompt_org = (prompt_org*255).astype(np.uint8)
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prompt_org = cv2.resize(prompt_org,im_org.shape[:2][::-1])
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return prompt_org[:,:,0],prompt_org[:,:,1],prompt_org[:,:,2],out_im
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def appearance(model, im_path):
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im_org = cv2.imread(im_path)
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im_org, _ = _resize_if_needed(im_org)
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h, w = im_org.shape[:2]
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prompt = appearance_prompt(im_org)
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in_im = np.concatenate((im_org, prompt), -1)
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in_im, padding_h, padding_w = stride_integral(in_im, 8)
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in_im = in_im / 255.0
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in_im = torch.from_numpy(in_im.transpose(2, 0, 1)).unsqueeze(0)
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in_im = in_im.half().to(DEVICE)
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model = model.half()
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with torch.no_grad():
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pred = model(in_im)
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pred = torch.clamp(pred, 0, 1)
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pred = pred[0].permute(1, 2, 0).cpu().numpy()
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pred = (pred * 255).astype(np.uint8)
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out_im = pred[padding_h:, padding_w:]
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return prompt[:, :, 0], prompt[:, :, 1], prompt[:, :, 2], out_im
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def deshadowing(model, im_path):
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im_org = cv2.imread(im_path)
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im_org, _ = _resize_if_needed(im_org)
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h, w = im_org.shape[:2]
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prompt = deshadow_prompt(im_org)
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in_im = np.concatenate((im_org, prompt), -1)
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in_im, padding_h, padding_w = stride_integral(in_im, 8)
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in_im = in_im / 255.0
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in_im = torch.from_numpy(in_im.transpose(2, 0, 1)).unsqueeze(0)
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in_im = in_im.half().to(DEVICE)
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model = model.half()
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with torch.no_grad():
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pred = model(in_im)
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pred = torch.clamp(pred, 0, 1)
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pred = pred[0].permute(1, 2, 0).cpu().numpy()
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pred = (pred * 255).astype(np.uint8)
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out_im = pred[padding_h:, padding_w:]
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return prompt[:, :, 0], prompt[:, :, 1], prompt[:, :, 2], out_im
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def deblurring(model, im_path):
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im_org = cv2.imread(im_path)
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im_org, _ = _resize_if_needed(im_org)
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in_im, padding_h, padding_w = stride_integral(im_org, 8)
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prompt = deblur_prompt(in_im)
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in_im = np.concatenate((in_im, prompt), -1)
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in_im = in_im / 255.0
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in_im = torch.from_numpy(in_im.transpose(2, 0, 1)).unsqueeze(0)
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in_im = in_im.half().to(DEVICE)
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model = model.half()
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with torch.no_grad():
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pred = model(in_im)
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pred = torch.clamp(pred, 0, 1)
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pred = pred[0].permute(1, 2, 0).cpu().numpy()
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pred = (pred * 255).astype(np.uint8)
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out_im = pred[padding_h:, padding_w:]
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return prompt[:, :, 0], prompt[:, :, 1], prompt[:, :, 2], out_im
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def binarization(model, im_path):
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im_org = cv2.imread(im_path)
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im_org, _ = _resize_if_needed(im_org)
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im, padding_h, padding_w = stride_integral(im_org, 8)
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prompt = binarization_promptv2(im)
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h, w = im.shape[:2]
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in_im = np.concatenate((im, prompt), -1)
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in_im = in_im / 255.0
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in_im = torch.from_numpy(in_im.transpose(2, 0, 1)).unsqueeze(0)
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in_im = in_im.half().to(DEVICE)
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model = model.half()
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with torch.no_grad():
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pred = model(in_im)
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pred = pred[:, :2, :, :]
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pred = torch.max(torch.softmax(pred, 1), 1)[1]
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pred = pred[0].cpu().numpy()
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pred = (pred * 255).astype(np.uint8)
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pred = cv2.resize(pred, (w, h))
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out_im = pred[padding_h:, padding_w:]
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return prompt[:, :, 0], prompt[:, :, 1], prompt[:, :, 2], out_im
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def get_args():
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parser = argparse.ArgumentParser(description='Params')
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parser.add_argument('--model_path', nargs='?', type=str, default='./checkpoints/docres.pkl',help='Path of the saved checkpoint')
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parser.add_argument('--im_path', nargs='?', type=str, default='./distorted/',
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help='Path of input document image')
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parser.add_argument('--out_folder', nargs='?', type=str, default='./restorted/',
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help='Folder of the output images')
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parser.add_argument('--task', nargs='?', type=str, default='dewarping',
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help='task that need to be executed')
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parser.add_argument('--save_dtsprompt', nargs='?', type=int, default=0,
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help='Width of the input image')
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args = parser.parse_args()
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possible_tasks = ['dewarping','deshadowing','appearance','deblurring','binarization','end2end']
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assert args.task in possible_tasks, 'Unsupported task, task must be one of '+', '.join(possible_tasks)
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return args
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def model_init(args):
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model = restormer_arch.Restormer(
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inp_channels=6,
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out_channels=3,
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dim = 48,
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num_blocks = [2,3,3,4],
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num_refinement_blocks = 4,
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heads = [1,2,4,8],
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ffn_expansion_factor = 2.66,
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bias = False,
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LayerNorm_type = 'WithBias',
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dual_pixel_task = True
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)
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if DEVICE.type == 'cpu':
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state = convert_state_dict(torch.load(args.model_path, map_location='cpu')['model_state'])
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else:
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state = convert_state_dict(torch.load(args.model_path, map_location='cuda:0')['model_state'])
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model.load_state_dict(state)
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model.eval()
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model = model.to(DEVICE)
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return model
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def inference_one_im(model,im_path,task):
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if task=='dewarping':
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prompt1,prompt2,prompt3,restorted = dewarping(model,im_path)
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elif task=='deshadowing':
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prompt1,prompt2,prompt3,restorted = deshadowing(model,im_path)
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elif task=='appearance':
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prompt1,prompt2,prompt3,restorted = appearance(model,im_path)
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elif task=='deblurring':
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prompt1,prompt2,prompt3,restorted = deblurring(model,im_path)
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elif task=='binarization':
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prompt1,prompt2,prompt3,restorted = binarization(model,im_path)
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elif task=='end2end':
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prompt1,prompt2,prompt3,restorted = dewarping(model,im_path)
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cv2.imwrite('restorted/step1.jpg',restorted)
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prompt1,prompt2,prompt3,restorted = deshadowing(model,'restorted/step1.jpg')
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cv2.imwrite('restorted/step2.jpg',restorted)
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prompt1,prompt2,prompt3,restorted = appearance(model,'restorted/step2.jpg')
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return prompt1,prompt2,prompt3,restorted
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def save_results(
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img_path: str,
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out_folder: str,
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task: str,
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save_dtsprompt: bool,
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):
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im_name = os.path.split(img_path)[-1]
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im_format = '.'+im_name.split('.')[-1]
|
|||
|
|
save_path = os.path.join(out_folder, im_name.replace(im_format, '_' + task + im_format))
|
|||
|
|
cv2.imwrite(save_path, restorted)
|
|||
|
|
if save_dtsprompt:
|
|||
|
|
cv2.imwrite(save_path.replace(im_format, '_prompt1' + im_format), prompt1)
|
|||
|
|
cv2.imwrite(save_path.replace(im_format, '_prompt2' + im_format), prompt2)
|
|||
|
|
cv2.imwrite(save_path.replace(im_format, '_prompt3' + im_format), prompt3)
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == '__main__':
|
|||
|
|
|
|||
|
|
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
|||
|
|
args = get_args()
|
|||
|
|
model = model_init(args)
|
|||
|
|
|
|||
|
|
img_source = args.im_path
|
|||
|
|
|
|||
|
|
if Path(img_source).is_dir():
|
|||
|
|
img_paths = glob.glob(os.path.join(img_source, '*'))
|
|||
|
|
for img_path in img_paths:
|
|||
|
|
prompt1,prompt2,prompt3,restorted = inference_one_im(model,img_path,args.task)
|
|||
|
|
save_results(
|
|||
|
|
img_path=img_path,
|
|||
|
|
out_folder=args.out_folder,
|
|||
|
|
task=args.task,
|
|||
|
|
save_dtsprompt=args.save_dtsprompt,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
else:
|
|||
|
|
prompt1,prompt2,prompt3,restorted = inference_one_im(model,img_source,args.task)
|
|||
|
|
save_results(
|
|||
|
|
img_path=img_source,
|
|||
|
|
out_folder=args.out_folder,
|
|||
|
|
task=args.task,
|
|||
|
|
save_dtsprompt=args.save_dtsprompt,
|
|||
|
|
)
|