图像超分之——寻找两张图差异的区域
2024-05-08 11:30:55
本代码是超分或者复原任务中，想找出PSNR差距较大的区域的代码
import os
import math
import numpy as np
import cv2
import glob
from skimage import transform
from skimage import measure
from collections import OrderedDictimport matplotlib.pyplot as plt
import matplotlib.patches as patchesdef bgr2ycbcr(img, only_y=True):'''same as matlab rgb2ycbcronly_y: only return Y channelInput:uint8, [0, 255]float, [0, 1]'''in_img_type = img.dtypeimg.astype(np.float32)if in_img_type != np.uint8:img *= 255.# convertif only_y:rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0else:rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],[65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]if in_img_type == np.uint8:rlt = rlt.round()else:rlt /= 255.return rlt.astype(in_img_type)def calculate_psnr(img1, img2):# img1 and img2 have range [0, 255]img1 = img1.astype(np.float64)img2 = img2.astype(np.float64)mse = np.mean((img1 - img2)**2)if mse == 0:return float('inf')return 20 * math.log10(255.0 / math.sqrt(mse))def mse2psnr(mse):if mse == 0:return float('inf')return 20 * math.log10(1.0 / math.sqrt(mse))def plot_heatmap(image, heat_map, alpha=0.5, display=False, save=None, cmap='viridis', axis='on', dpi=80, verbose=False):height = image.shape[0]width = image.shape[1]# resize heat mapheat_map_resized = transform.resize(heat_map, (height, width))# normalize heat mapmax_value = np.max(heat_map_resized)min_value = np.min(heat_map_resized)normalized_heat_map = (heat_map_resized - min_value) / (max_value - min_value)if display:# displayplt.imshow(image)plt.imshow(255 * normalized_heat_map, alpha=alpha, cmap=cmap)plt.axis(axis)plt.show()if save is not None:if verbose:print('save image: ' + save)H, W, C = image.shapefigsize = W / float(dpi), H / float(dpi)fig = plt.figure(figsize=figsize)ax = fig.add_axes([0, 0, 1, 1])ax.axis('off')ax.imshow(image)ax.imshow(255 * normalized_heat_map, alpha=alpha, cmap=cmap)ax.set(xlim=[0, W], ylim=[H, 0], aspect=1)fig.savefig(save, dpi=dpi, transparent=True)def to_bin(img, lower, upper):return (lower < img) & (img < upper)def plot_diffmap(im_BSL, im_OCT, im_GT, heatmap, thres=0.4, alpha=0.5, display=False, save=None, cmap='viridis', axis='on', dpi=80, verbose=False):height, width, _ = im_BSL.shape# resize heat mapheatmap_resized = transform.resize(heatmap, (height, width))# normalize heat mapmax_value = np.max(heatmap_resized)min_value = np.min(heatmap_resized)normalized_heatmap = (heatmap_resized - min_value) / (max_value - min_value)# capture regionsbin_map = to_bin(normalized_heatmap, thres, 1.0)label_map = measure.label(bin_map, connectivity=2)props = measure.regionprops(label_map)plot_im = im_BSL.copy()plot_im[~bin_map] = 0if save is not None:if verbose:print('save image: ' + save)H, W, C = im_BSL.shapefigsize = W / float(dpi), H / float(dpi)fig = plt.figure(figsize=figsize)ax = fig.add_axes([0, 0, 1, 1])ax.axis('off')ax.imshow(im_BSL)ax.imshow(normalized_heatmap, alpha=alpha)
# ax.imshow(plot_im, alpha=alpha)ax.axis(axis)for i in range(len(props)):if props[i].bbox_area >= 100:bbox_coord = props[i].bboxax.add_patch(patches.Rectangle((bbox_coord[1], bbox_coord[0]),bbox_coord[3] - bbox_coord[1],bbox_coord[2] - bbox_coord[0],edgecolor='y',linewidth = 6,fill=False))psnr = calculate_psnr(im_OCT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255., \im_GT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255.) - \calculate_psnr(im_BSL[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255., \im_GT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255.)h_aln = 'right' if W - bbox_coord[1] < 50 else 'left'if bbox_coord[0] < 20:ax.text(bbox_coord[1], bbox_coord[2], "{:+.2f}".format(psnr), color='r', verticalalignment='top', horizontalalignment=h_aln, fontsize=26)else:ax.text(bbox_coord[1], bbox_coord[0], "{:+.2f}".format(psnr), color='r',verticalalignment='bottom', horizontalalignment=h_aln, fontsize=26)ax.set(xlim=[0, W], ylim=[H, 0], aspect=1)fig.savefig(save, dpi=dpi, transparent=True)
# plt.show()def plot_diff_patch(im_BSL, im_OCT, im_GT, heatmap, thres=0.4, alpha=0.5, display=False, save=None, cmap='viridis', axis='on', dpi=80, verbose=False):H, W, C = im_BSL.shape# resize heat mapheatmap_resized = transform.resize(heatmap, (H, W))# normalize heat mapmax_value = np.max(heatmap_resized)min_value = np.min(heatmap_resized)normalized_heatmap = (heatmap_resized - min_value) / (max_value - min_value)# capture regionsbin_map = to_bin(normalized_heatmap, 0.4, 1.0)label_map = measure.label(bin_map, connectivity=2)props = measure.regionprops(label_map)bbox_err = []for i in range(len(props)):if props[i].bbox_area >= 100:bbox_coord = props[i].bboxerr = np.mean(normalized_heatmap[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3]])psnr = calculate_psnr(im_OCT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255., \im_GT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255.) - \calculate_psnr(im_BSL[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255., \im_GT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255.)bbox_err.append((i, err, psnr))bbox_err.sort(key=lambda x:x[1], reverse=True)im_diff = np.clip(im_OCT - im_BSL + 0.5, 0.0, 1.0)save_dir20= '/data1/cropimage/diff6_cvpr'save_path20 = os.path.join(save_dir20, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '')+'.png')im_diff20=im_diff*255cv2.imwrite(save_path20,im_diff20[:, :, [2, 1, 0]])num_bbox = min(len(bbox_err), 5)# Plot patchesfig, axes = plt.subplots(nrows=num_bbox, ncols=4, figsize=(15,15))if axes.ndim == 1:axes = [axes]for i in range(num_bbox):ind, err, psnr = bbox_err[i]bbox_coord = props[ind].bboxaxes[i][0].imshow(im_GT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :])axes[i][1].imshow(im_BSL[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :])axes[i][2].imshow(im_OCT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :])axes[i][3].imshow(im_diff[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :])
# ###################################################################################################
# im_GT1=im_GT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]
# im_BSL1=im_BSL[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]
# im_OCT1=im_OCT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]
# im_diff1=im_diff[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]# axes[i][0].imshow(im_GT1)
# axes[i][1].imshow(im_BSL1)
# axes[i][2].imshow(im_OCT1)
# axes[i][3].imshow(im_diff1)# save_dir1= '/data1/cropimage/diff/im_GT1'
# save_path1 = os.path.join(save_dir1, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '')+str(i+1) + '.png')
# im_GT1=cv2.resize(im_GT1*255,(100, 100))
# cv2.imwrite(save_path1,im_GT1[:, :, [2, 1, 0]])# save_dir2= '/data1/cropimage/diff/im_BSL1'
# save_path2 = os.path.join(save_dir2, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '')+str(i+1) + '.png')
# #im_BSL1=im_BSL1*255
# im_BSL1=cv2.resize(im_BSL1*255,(100, 100))
# cv2.imwrite(save_path2,im_BSL1[:, :, [2, 1, 0]])# save_dir3= '/data1/cropimage/diff/im_OCT1'
# save_path3 = os.path.join(save_dir3, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '')+str(i+1) + '.png')
# #im_OCT1=im_OCT1*255
# im_OCT1=cv2.resize(im_OCT1*255,(100, 100))
# cv2.imwrite(save_path3,im_OCT1[:, :, [2, 1, 0]])# save_dir4= '/data1/cropimage/diff/im_diff1'
# save_path4 = os.path.join(save_dir4, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '')+str(i+1) + '.png')
# #im_diff1=im_diff1*255
# im_diff1=cv2.resize(im_diff1*255,(100, 100))
# cv2.imwrite(save_path4,im_diff1[:, :, [2, 1, 0]])axes[i][3].text(bbox_coord[3]-bbox_coord[1], bbox_coord[2]-bbox_coord[0], \"{:+.2f}".format(psnr), color='r', fontsize=16)axes[i][3].text(bbox_coord[3]-bbox_coord[1], 0, \"{}".format(bbox_coord), color='r', fontsize=16)fig.savefig(save_path, dpi=300, bbox_inches='tight', transparent=False)# plt.show()folder_BSL = "/data1/results10.09/(v1)Layer_HRLR_withoutconnection_SRResNet_16B64C_alpha=0.5/DIV2K_VAL/"
folder_OCT = "/data1/results/(ture)_1X1_directshare_SRResNet_44B64C_alpha=0.5/DIV2K_VAL/"
folder_GT = '/data1/data/DIV2K_VAL/DIV2K_valid_HR/'crop_border = 4
suffix = '' # suffix for Gen images
test_Y = False # True: test Y channel only; False: test RGB channelsPSNR_all = []
SSIM_all = []
img_list = sorted(glob.glob(folder_OCT + '/*'))[:100]if test_Y:print('Testing Y channel.')
else:print('Testing RGB channels.')patch_size = 32
stride = 10for img_path in img_list:base_name = os.path.splitext(os.path.basename(img_path))[0]im_OCT = cv2.imread(img_path)[:, :, [2, 1, 0]] / 255.im_BSL = cv2.imread(os.path.join(folder_BSL, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', 'SRResNet_16B64C') + suffix + '.png'))[:, :, [2, 1, 0]] / 255.im_GT = cv2.imread(os.path.join(folder_GT, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '').replace('_bicLRx4', '') + suffix + '.png'))[:, :, [2, 1, 0]] / 255.H, W, C = im_OCT.shapeH_axis = np.arange(0, H - patch_size, stride)W_axis = np.arange(0, W - patch_size, stride)err_map = np.zeros((len(H_axis), len(W_axis)))inv_map = np.zeros((len(H_axis), len(W_axis)))total_err = np.mean((im_OCT - im_BSL)**2)for i, h in enumerate(H_axis):for j, w in enumerate(W_axis):patch_OCT = im_OCT[h:h+patch_size, w:w+patch_size, :]patch_BSL = im_BSL[h:h+patch_size, w:w+patch_size, :]patch_err = np.sum((patch_OCT - patch_BSL)**2) / (H*W*C)err_map[i, j] = mse2psnr(patch_err)inv_map[i, j] = mse2psnr(total_err- patch_err)save_dir = '/data1/cropimage/diff_cvpr/'save_path = os.path.join(save_dir, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '') + '.png')save_dir6 = '/data1/cropimage/heatdiff_cvpr/'save_path6 = os.path.join(save_dir6, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '') + '.png')#plot_heatmap(im_BSL, inv_map, alpha=0.7, save=save_path, axis='off', display=False)plot_diffmap(im_BSL, im_OCT, im_GT, inv_map, alpha=0.5, save=save_path6, axis='off', display=False)plot_diff_patch(im_BSL, im_OCT, im_GT, inv_map, alpha=0.5, save=save_path, axis='off', display=False)改进版：
import os
import math
import numpy as np
import cv2
import glob
from skimage import transform
from skimage import measure
from collections import OrderedDictimport matplotlib.pyplot as plt
import matplotlib.patches as patchesdef bgr2ycbcr(img, only_y=True):'''same as matlab rgb2ycbcronly_y: only return Y channelInput:uint8, [0, 255]float, [0, 1]'''in_img_type = img.dtypeimg.astype(np.float32)if in_img_type != np.uint8:img *= 255.# convertif only_y:rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0else:rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],[65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]if in_img_type == np.uint8:rlt = rlt.round()else:rlt /= 255.return rlt.astype(in_img_type)def calculate_psnr(img1, img2):# img1 and img2 have range [0, 255]img1 = img1.astype(np.float64)img2 = img2.astype(np.float64)mse = np.mean((img1 - img2)**2)if mse == 0:return float('inf')return 20 * math.log10(255.0 / math.sqrt(mse))def mse2psnr(mse):if mse == 0:return float('inf')return 20 * math.log10(1.0 / math.sqrt(mse))def plot_heatmap(image, heat_map, alpha=0.5, display=False, save=None, cmap='viridis', axis='on', dpi=80, verbose=False):height = image.shape[0]width = image.shape[1]# resize heat mapheat_map_resized = transform.resize(heat_map, (height, width))# normalize heat mapmax_value = np.max(heat_map_resized)min_value = np.min(heat_map_resized)normalized_heat_map = (heat_map_resized - min_value) / (max_value - min_value)if display:# displayplt.imshow(image)plt.imshow(255 * normalized_heat_map, alpha=alpha, cmap=cmap)plt.axis(axis)plt.show()if save is not None:if verbose:print('save image: ' + save)H, W, C = image.shapefigsize = W / float(dpi), H / float(dpi)fig = plt.figure(figsize=figsize)ax = fig.add_axes([0, 0, 1, 1])ax.axis('off')ax.imshow(image)ax.imshow(255 * normalized_heat_map, alpha=alpha, cmap=cmap)ax.set(xlim=[0, W], ylim=[H, 0], aspect=1)fig.savefig(save, dpi=dpi, transparent=True)def to_bin(img, lower, upper):return (lower < img) & (img < upper)def plot_diffmap(im_BSL, im_OCT, im_GT, heatmap, thres=0.4, alpha=0.5, display=False, save=None, cmap='viridis', axis='on', dpi=80, verbose=False):height, width, _ = im_BSL.shape# resize heat mapheatmap_resized = transform.resize(heatmap, (height, width))# normalize heat mapmax_value = np.max(heatmap_resized)min_value = np.min(heatmap_resized)normalized_heatmap = (heatmap_resized - min_value) / (max_value - min_value)# capture regionsbin_map = to_bin(normalized_heatmap, thres, 1.0)label_map = measure.label(bin_map, connectivity=2)props = measure.regionprops(label_map)plot_im = im_BSL.copy()plot_im[~bin_map] = 0if save is not None:if verbose:print('save image: ' + save)H, W, C = im_BSL.shapefigsize = W / float(dpi), H / float(dpi)fig = plt.figure(figsize=figsize)ax = fig.add_axes([0, 0, 1, 1])ax.axis('off')ax.imshow(im_BSL)ax.imshow(normalized_heatmap, alpha=alpha)
# ax.imshow(plot_im, alpha=alpha)ax.axis(axis)for i in range(len(props)):if props[i].bbox_area >= 100:bbox_coord = props[i].bboxax.add_patch(patches.Rectangle((bbox_coord[1], bbox_coord[0]),bbox_coord[3] - bbox_coord[1],bbox_coord[2] - bbox_coord[0],edgecolor='y',linewidth = 6,fill=False))psnr = calculate_psnr(im_OCT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255., \im_GT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255.) - \calculate_psnr(im_BSL[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255., \im_GT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255.)h_aln = 'right' if W - bbox_coord[1] < 50 else 'left'if bbox_coord[0] < 20:ax.text(bbox_coord[1], bbox_coord[2], "{:+.2f}".format(psnr), color='r', verticalalignment='top', horizontalalignment=h_aln, fontsize=26)else:ax.text(bbox_coord[1], bbox_coord[0], "{:+.2f}".format(psnr), color='r',verticalalignment='bottom', horizontalalignment=h_aln, fontsize=26)ax.set(xlim=[0, W], ylim=[H, 0], aspect=1)fig.savefig(save, dpi=dpi, transparent=True)
# plt.show()def plot_diff_patch(im_BSL, im_OCT, im_GT, heatmap, thres=0.4, alpha=0.5, display=False, save=None, cmap='viridis', axis='on', dpi=80, verbose=False):H, W, C = im_BSL.shape# resize heat mapheatmap_resized = transform.resize(heatmap, (H, W))# normalize heat mapmax_value = np.max(heatmap_resized)min_value = np.min(heatmap_resized)normalized_heatmap = (heatmap_resized - min_value) / (max_value - min_value)# capture regionsbin_map = to_bin(normalized_heatmap, 0.4, 1.0)label_map = measure.label(bin_map, connectivity=2)props = measure.regionprops(label_map)bbox_err = []for i in range(len(props)):if props[i].bbox_area >= 100:bbox_coord = props[i].bboxerr = np.mean(normalized_heatmap[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3]])psnr = calculate_psnr(im_OCT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255., \im_GT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255.) - \calculate_psnr(im_BSL[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255., \im_GT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]*255.)bbox_err.append((i, err, psnr))bbox_err.sort(key=lambda x:x[1], reverse=True)im_diff = np.clip(im_OCT - im_BSL + 0.5, 0.0, 1.0)save_dir20= '/data1/cropimage/diff6_cvpr'save_path20 = os.path.join(save_dir20, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '')+'.png')im_diff20=im_diff*255cv2.imwrite(save_path20,im_diff20[:, :, [2, 1, 0]])num_bbox = min(len(bbox_err), 5)# Plot patchesfig, axes = plt.subplots(nrows=num_bbox, ncols=4, figsize=(15,15))if axes.ndim == 1:axes = [axes]for i in range(num_bbox):ind, err, psnr = bbox_err[i]bbox_coord = props[ind].bboxaxes[i][0].imshow(im_GT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :])axes[i][1].imshow(im_BSL[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :])axes[i][2].imshow(im_OCT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :])axes[i][3].imshow(im_diff[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :])
# ###################################################################################################
# im_GT1=im_GT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]
# im_BSL1=im_BSL[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]
# im_OCT1=im_OCT[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]
# im_diff1=im_diff[bbox_coord[0]:bbox_coord[2], bbox_coord[1]:bbox_coord[3], :]# axes[i][0].imshow(im_GT1)
# axes[i][1].imshow(im_BSL1)
# axes[i][2].imshow(im_OCT1)
# axes[i][3].imshow(im_diff1)# save_dir1= '/data1/cropimage/diff/im_GT1'
# save_path1 = os.path.join(save_dir1, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '')+str(i+1) + '.png')
# im_GT1=cv2.resize(im_GT1*255,(100, 100))
# cv2.imwrite(save_path1,im_GT1[:, :, [2, 1, 0]])# save_dir2= '/data1/cropimage/diff/im_BSL1'
# save_path2 = os.path.join(save_dir2, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '')+str(i+1) + '.png')
# #im_BSL1=im_BSL1*255
# im_BSL1=cv2.resize(im_BSL1*255,(100, 100))
# cv2.imwrite(save_path2,im_BSL1[:, :, [2, 1, 0]])# save_dir3= '/data1/cropimage/diff/im_OCT1'
# save_path3 = os.path.join(save_dir3, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '')+str(i+1) + '.png')
# #im_OCT1=im_OCT1*255
# im_OCT1=cv2.resize(im_OCT1*255,(100, 100))
# cv2.imwrite(save_path3,im_OCT1[:, :, [2, 1, 0]])# save_dir4= '/data1/cropimage/diff/im_diff1'
# save_path4 = os.path.join(save_dir4, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '')+str(i+1) + '.png')
# #im_diff1=im_diff1*255
# im_diff1=cv2.resize(im_diff1*255,(100, 100))
# cv2.imwrite(save_path4,im_diff1[:, :, [2, 1, 0]])axes[i][3].text(bbox_coord[3]-bbox_coord[1], bbox_coord[2]-bbox_coord[0], \"{:+.2f}".format(psnr), color='r', fontsize=16)axes[i][3].text(bbox_coord[3]-bbox_coord[1], 0, \"{}".format(bbox_coord), color='r', fontsize=16)fig.savefig(save_path, dpi=300, bbox_inches='tight', transparent=False)# plt.show()folder_BSL = "/data1/results10.09/(multi_scale)_SRResNet_16B64C/DIV2K_VAL0.8/"
folder_OCT ="/data1/results/(multi)1X1_directshare_SRResNet_48B64C_alpha=0.5/DIV2K_VAL0.8/"
folder_GT = "/data1/data/multiscale_dataset/DIV2K_valid_HR_0.8/"crop_border = 4
suffix = '' # suffix for Gen images
test_Y = False # True: test Y channel only; False: test RGB channelsPSNR_all = []
SSIM_all = []
img_list = sorted(glob.glob(folder_OCT + '/*'))[:100]if test_Y:print('Testing Y channel.')
else:print('Testing RGB channels.')patch_size = 32
stride = 10for img_path in img_list:base_name = os.path.splitext(os.path.basename(img_path))[0]im_OCT = cv2.imread(img_path)[:, :, [2, 1, 0]] / 255.im_BSL = cv2.imread(os.path.join(folder_BSL, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', 'SRResNet_16B64C') + suffix + '.png'))[:, :, [2, 1, 0]] / 255.im_GT = cv2.imread(os.path.join(folder_GT, base_name.replace('_bicLRx4', '_bicLRx0.6') + suffix + '.png'))[:, :, [2, 1, 0]] / 255.H, W, C = im_OCT.shapeH_axis = np.arange(0, H - patch_size, stride)W_axis = np.arange(0, W - patch_size, stride)err_map = np.zeros((len(H_axis), len(W_axis)))inv_map = np.zeros((len(H_axis), len(W_axis)))total_err = np.mean((im_OCT - im_BSL)**2)for i, h in enumerate(H_axis):for j, w in enumerate(W_axis):patch_OCT = im_OCT[h:h+patch_size, w:w+patch_size, :]patch_BSL = im_BSL[h:h+patch_size, w:w+patch_size, :]patch_err = np.sum((patch_OCT - patch_BSL)**2) / (H*W*C)err_map[i, j] = mse2psnr(patch_err)inv_map[i, j] = mse2psnr(total_err- patch_err)save_dir = '/data1/cropimage/heatmap/diff_DIV2K_VAL0.8/'if not os.path.exists(save_dir):os.mkdir(save_dir)save_path = os.path.join(save_dir, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '') + '.png')save_dir6 = '/data1/cropimage/heatmap/heatdiff_DIV2K_VAL0.8/'if not os.path.exists(save_dir6):os.mkdir(save_dir6)save_path6 = os.path.join(save_dir6, base_name.replace('Layer_HRLRadd_SRResNet_16B64C_alpha=0.5', '') + '.png')#plot_heatmap(im_BSL, inv_map, alpha=0.7, save=save_path, axis='off', display=False)plot_diffmap(im_BSL, im_OCT, im_GT, inv_map, alpha=0.5, save=save_path6, axis='off', display=False)plot_diff_patch(im_BSL, im_OCT, im_GT, inv_map, alpha=0.5, save=save_path, axis='off', display=False)图像超分之——寻找两张图差异的区域相关推荐
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