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| import gradio as gr | |
| import cv2 | |
| import torch | |
| import torch.utils.data as data | |
| from torchvision import transforms | |
| from torch import nn | |
| import torch.nn.functional as F | |
| import matplotlib.pyplot as plt | |
| from matplotlib import cm | |
| from matplotlib import colors | |
| from mpl_toolkits.axes_grid1 import ImageGrid | |
| import fire_network | |
| import numpy as np | |
| from PIL import Image | |
| # Possible Scales for multiscale inference | |
| scales = [2.0, 1.414, 1.0, 0.707, 0.5, 0.353, 0.25] | |
| device = 'cpu' | |
| # Load nets | |
| state = torch.load('fire.pth', map_location='cpu') | |
| state['net_params']['pretrained'] = None # no need for imagenet pretrained model | |
| net_sfm = fire_network.init_network(**state['net_params']).to(device) | |
| net_sfm.load_state_dict(state['state_dict']) | |
| dim_red_params_dict = {} | |
| for name, param in net_sfm.named_parameters(): | |
| if 'dim_reduction' in name: | |
| dim_red_params_dict[name] = param | |
| state2 = torch.load('fire_imagenet.pth', map_location='cpu') | |
| state2['net_params'] = state['net_params'] | |
| state2['state_dict'] = dict(state2['state_dict'], **dim_red_params_dict); | |
| net_imagenet = fire_network.init_network(**state['net_params']).to(device) | |
| net_imagenet.load_state_dict(state2['state_dict'], strict=False) | |
| transform = transforms.Compose([ | |
| transforms.Resize(1024), | |
| transforms.ToTensor(), | |
| transforms.Normalize(**dict(zip(["mean", "std"], net_sfm.runtime['mean_std']))) | |
| ]) | |
| def match(query_feat, pos_feat, LoweRatioTh=0.9): | |
| # first perform reciprocal nn | |
| dist = torch.cdist(query_feat, pos_feat) | |
| # print('dist.size',dist.size()) | |
| best1 = torch.argmin(dist, dim=1) | |
| best2 = torch.argmin(dist, dim=0) | |
| # print('best2.size',best2.size()) | |
| arange = torch.arange(best2.size(0)) | |
| reciprocal = best1[best2]==arange | |
| # check Lowe ratio test | |
| dist2 = dist.clone() | |
| dist2[best2,arange] = float('Inf') | |
| dist2_second2 = torch.argmin(dist2, dim=0) | |
| ratio1to2 = dist[best2,arange] / dist2_second2 | |
| valid = torch.logical_and(reciprocal, ratio1to2<=LoweRatioTh) | |
| pindices = torch.where(valid)[0] | |
| qindices = best2[pindices] | |
| # keep only the ones with same indices | |
| valid = pindices==qindices | |
| return pindices[valid] | |
| def clear_figures(): | |
| plt.figure().clear() | |
| plt.close() | |
| plt.cla() | |
| plt.clf() | |
| def generate_matching_superfeatures( | |
| im1, im2, | |
| Imagenet_model=False, | |
| scale_id=6, threshold=50, | |
| random_mode=False, sf_ids=''): #, only_matching=True): | |
| # print('im1:', im1.size) | |
| # print('im2:', im2.size) | |
| clear_figures() | |
| col = plt.get_cmap('tab10') | |
| net = net_sfm | |
| if Imagenet_model: | |
| net = net_imagenet | |
| im1_tensor = transform(im1).unsqueeze(0) | |
| im2_tensor = transform(im2).unsqueeze(0) | |
| im1_cv = np.array(im1)[:, :, ::-1].copy() | |
| im2_cv = np.array(im2)[:, :, ::-1].copy() | |
| # extract features | |
| with torch.no_grad(): | |
| output1 = net.get_superfeatures(im1_tensor.to(device), scales=[scales[scale_id]]) | |
| feats1 = output1[0][0] | |
| attns1 = output1[1][0] | |
| strenghts1 = output1[2][0] | |
| output2 = net.get_superfeatures(im2_tensor.to(device), scales=[scales[scale_id]]) | |
| feats2 = output2[0][0] | |
| attns2 = output2[1][0] | |
| strenghts2 = output2[2][0] | |
| feats1n = F.normalize(torch.t(torch.squeeze(feats1)), dim=1) | |
| feats2n = F.normalize(torch.t(torch.squeeze(feats2)), dim=1) | |
| ind_match = match(feats1n, feats2n) | |
| # which sf | |
| sf_idx_ = [] | |
| n_sf_ids = 10 | |
| if random_mode or sf_ids == '': | |
| sf_idx_ = np.random.randint(256, size=n_sf_ids) | |
| else: | |
| sf_idx_ = map(int, sf_ids.strip().split(',')) | |
| # only_matching: | |
| if random_mode: | |
| sf_idx_ = [int(jj) for jj in ind_match[np.random.randint(len(list(ind_match)), size=n_sf_ids)].numpy()] | |
| sf_idx_ = list( dict.fromkeys(sf_idx_) ) | |
| else: | |
| sf_idx_ = [i for i in sf_idx_ if i in list(ind_match)] | |
| n_sf_ids = len(sf_idx_) | |
| # Store all binary SF att maps to show them all at once in the end | |
| all_att_bin1 = [] | |
| all_att_bin2 = [] | |
| for n, i in enumerate(sf_idx_): | |
| att_heat = np.array(attns1[0,i,:,:].numpy(), dtype=np.float32) | |
| att_heat = np.uint8(att_heat / np.max(att_heat[:]) * 255.0) | |
| att_heat_bin = np.where(att_heat>threshold, 255, 0) | |
| all_att_bin1.append(att_heat_bin) | |
| att_heat = np.array(attns2[0,i,:,:].numpy(), dtype=np.float32) | |
| att_heat = np.uint8(att_heat / np.max(att_heat[:]) * 255.0) | |
| att_heat_bin = np.where(att_heat>threshold, 255, 0) | |
| all_att_bin2.append(att_heat_bin) | |
| fin_img = [] | |
| img1rsz = np.copy(im1_cv) | |
| for j, att in enumerate(all_att_bin1): | |
| att = cv2.resize(att, im1.size, interpolation=cv2.INTER_NEAREST) | |
| mask2d = zip(*np.where(att==255)) | |
| for m,n in mask2d: | |
| col_ = col.colors[j] | |
| col_ = 255*np.array(colors.to_rgba(col_))[:3] | |
| img1rsz[m,n, :] = col_[::-1] | |
| img2rsz = np.copy(im2_cv) | |
| for j, att in enumerate(all_att_bin2): | |
| att = cv2.resize(att, im2.size, interpolation=cv2.INTER_NEAREST) | |
| mask2d = zip(*np.where(att==255)) | |
| for m,n in mask2d: | |
| col_ = col.colors[j] | |
| col_ = 255*np.array(colors.to_rgba(col_))[:3] | |
| img2rsz[m,n, :] = col_[::-1] | |
| fig1 = plt.figure(1) | |
| plt.imshow(cv2.cvtColor(img1rsz, cv2.COLOR_BGR2RGB)) | |
| ax1 = plt.gca() | |
| ax1.axis('off') | |
| plt.tight_layout() | |
| fig2 = plt.figure(2) | |
| plt.imshow(cv2.cvtColor(img2rsz, cv2.COLOR_BGR2RGB)) | |
| ax2 = plt.gca() | |
| ax2.axis('off') | |
| plt.tight_layout() | |
| f = lambda m,c: plt.plot([],[],marker=m, color=c, ls="none")[0] | |
| handles = [f("s", col.colors[i]) for i in range(n_sf_ids)] | |
| fig_leg = plt.figure(3) | |
| legend = plt.legend(handles, sf_idx_, framealpha=1, frameon=False, facecolor='w',fontsize=25, loc="center") | |
| ax3 = plt.gca() | |
| ax3.axis('off') | |
| plt.tight_layout() | |
| im1 = None | |
| im2 = None | |
| return fig1, fig2, fig_leg | |
| # GRADIO APP | |
| title = "Visualizing Super-features" | |
| description = "This is a visualization demo for the ICLR 2022 paper <b><a href='https://github.com/naver/fire' target='_blank'>Learning Super-Features for Image Retrieval</a></p></b>" | |
| article = "<p style='text-align: center'><a href='https://github.com/naver/fire' target='_blank'>Original Github Repo</a></p>" | |
| iface = gr.Interface( | |
| fn=generate_matching_superfeatures, | |
| inputs=[ | |
| gr.inputs.Image(shape=(1024, 1024), type="pil", label="First Image"), | |
| gr.inputs.Image(shape=(1024, 1024), type="pil", label="Second Image"), | |
| gr.inputs.Checkbox(default=False, label="ImageNet Model (Default: SfM-120k)"), | |
| gr.inputs.Slider(minimum=0, maximum=6, step=1, default=4, label="Scale"), | |
| gr.inputs.Slider(minimum=0, maximum=255, step=25, default=150, label="Binarization Threshold"), | |
| gr.inputs.Checkbox(default=True, label="Show random (matching) SFs"), | |
| gr.inputs.Textbox(lines=1, default="", label="...or show specific SF IDs:", optional=True), | |
| ], | |
| outputs=[ | |
| gr.outputs.Image(type="plot", label="First Image SFs"), | |
| gr.outputs.Image(type="plot", label="Second Image SFs"), | |
| gr.outputs.Image(type="plot", label="SF legend")], | |
| title=title, | |
| theme='peach', | |
| layout="horizontal", | |
| description=description, | |
| article=article, | |
| examples=[ | |
| ["chateau_1.png", "chateau_2.png", False, 3, 150, False, '170,15,25,63,193,125,92,214,107'], | |
| ["areopoli1.jpeg", "areopoli2.jpeg", False, 4, 150, False, '205,2,163,130'], | |
| ["jaipur1.jpeg", "jaipur2.jpeg", False, 4, 50, False, '51,206,216,49,27'], | |
| ["basil1.jpeg", "basil2.jpeg", True, 4, 100, False, '75,152,19,36,156'], | |
| ["mill1.jpeg", "mill2.jpeg", False, 4, 100, False, '177,88,170,190,151,155'], | |
| ] | |
| ) | |
| iface.launch(enable_queue=True) |