temp

app.py

@@ -41,29 +41,12 @@ 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'])))
         ])
-# ---------------------------------------
-
-# class ImgDataset(data.Dataset):
-#     def __init__(self, images, imsize):
-#         self.images = images
-#         self.imsize = imsize
-#         self.transform = transforms.Compose([transforms.ToTensor(), \
-#             transforms.Normalize(**dict(zip(["mean", "std"], net.runtime['mean_std'])))])
-#     def __getitem__(self, index):
-#         img = self.images[index]
-#         img.thumbnail((self.imsize, self.imsize), Image.Resampling.LANCZOS)
-#         print('after imresize:', img.size)
-#         return  self.transform(img)
-#     def __len__(self):
-#         return len(self.images)
-
-# ---------------------------------------    
+
 
 def match(query_feat, pos_feat, LoweRatioTh=0.9):
     # first perform reciprocal nn
@@ -87,14 +70,14 @@ def match(query_feat, pos_feat, LoweRatioTh=0.9):
     return pindices[valid]
     
 
-# sf_idx_ = [55, 14, 5, 4, 52, 57, 40, 9]
 def clear_figures():
     plt.figure().clear()
     plt.close()
     plt.cla()
     plt.clf()
 
-col = plt.get_cmap('tab10')
+
+
 
 def generate_matching_superfeatures(
         im1, im2, 
@@ -105,15 +88,12 @@ def generate_matching_superfeatures(
     print('im2:', im2.size)
 
     clear_figures()
+    col = plt.get_cmap('tab10')
 
     net = net_sfm
     if Imagenet_model:
         net = net_imagenet
 
-    # dataset_ = ImgDataset(images=[im1, im2], imsize=1024)
-    # loader = torch.utils.data.DataLoader(dataset_, shuffle=False, pin_memory=True)
-
-
     im1_tensor = transform(im1).unsqueeze(0)
     im2_tensor = transform(im2).unsqueeze(0)
 
@@ -134,32 +114,17 @@ def generate_matching_superfeatures(
 
         feats1n = F.normalize(torch.t(torch.squeeze(feats1)), dim=1)
         feats2n = F.normalize(torch.t(torch.squeeze(feats2)), dim=1)
-        print('feats1n.shape', feats1n.shape)
         ind_match = match(feats1n, feats2n)
-        print('ind', ind_match)
-        print('ind.shape', ind_match.shape)
-        # outputs = []
-        # for im_tensor in loader:
-        #     outputs.append(net.get_superfeatures(im_tensor.to(device), scales=[scales[scale_id]]))
-        # feats1 = outputs[0][0][0]
-        # attns1 = outputs[0][1][0]
-        # strenghts1 = outputs[0][2][0]
-        # feats2 = outputs[1][0][0]
-        # attns2 = outputs[1][1][0]
-        # strenghts2 = outputs[1][2][0]
-    print(feats1.shape, feats2.shape)
-    print(attns1.shape, attns2.shape)
-    print(strenghts1.shape, strenghts2.shape)
     
     # which sf 
-    sf_idx_ = [55, 14, 5, 4, 52, 57, 40, 9]
+    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(','))
         
-    # if only_matching:
+    # 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_) )
@@ -172,11 +137,9 @@ def generate_matching_superfeatures(
     all_att_bin1 = []
     all_att_bin2 = []
     for n, i in enumerate(sf_idx_):
-        # all_atts[n].append(attn[j][scale_id][0,i,:,:].numpy())
         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)
-        # print(att_heat_bin)
         all_att_bin1.append(att_heat_bin)
 
         att_heat = np.array(attns2[0,i,:,:].numpy(), dtype=np.float32)
@@ -187,19 +150,11 @@ def generate_matching_superfeatures(
     
     fin_img = []
     img1rsz = np.copy(im1_cv)
-    print('im1:', im1.size)
-    print('img1rsz:', img1rsz.shape)
     for j, att in enumerate(all_att_bin1):
         att = cv2.resize(att, im1.size, interpolation=cv2.INTER_NEAREST)
-        # att = cv2.resize(att, imgz[i].shape[:2][::-1], interpolation=cv2.INTER_CUBIC)
-        # att = cv2.resize(att, imgz[i].shape[:2][::-1])
-        # att = att.resize(shape)
-        # att = resize(att, im1.size)
         mask2d = zip(*np.where(att==255))
         for m,n in mask2d:
             col_ = col.colors[j]
-            # col_ = col.colors[j] if j < 7 else col.colors[j+1]
-            # if j == 0: col_ = col.colors[9]
             col_ = 255*np.array(colors.to_rgba(col_))[:3]
             img1rsz[m,n, :] = col_[::-1]   
             
@@ -208,51 +163,35 @@ def generate_matching_superfeatures(
     print('img2rsz:', img2rsz.shape)
     for j, att in enumerate(all_att_bin2):
         att = cv2.resize(att, im2.size, interpolation=cv2.INTER_NEAREST)
-        # att = cv2.resize(att, imgz[i].shape[:2][::-1], interpolation=cv2.INTER_CUBIC)
-        # # att = cv2.resize(att, imgz[i].shape[:2][::-1])
-        # att = att.resize(im2.shape)
-        # print('att:', att.shape)
         mask2d = zip(*np.where(att==255))
         for m,n in mask2d:
             col_ = col.colors[j]
-            # col_ = col.colors[j] if j < 7 else col.colors[j+1]
-            # if j == 0: col_ = col.colors[9]
             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('scaled')
     ax1.axis('off')
     plt.tight_layout()    
-    # fig1.canvas.draw()
     
     fig2 = plt.figure(2)
     plt.imshow(cv2.cvtColor(img2rsz, cv2.COLOR_BGR2RGB))
     ax2 = plt.gca()
-    # ax2.axis('scaled')
     ax2.axis('off')
     plt.tight_layout()    
-    # fig2.canvas.draw()
     
     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")
-    # fig_leg  = legend.figure
-    # fig_leg.canvas.draw()
     ax3 = plt.gca()
-    # ax2.axis('scaled')
     ax3.axis('off')
     plt.tight_layout()    
-    # bbox  = legend.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
     
-
     im1 = None
     im2 = None
     return fig1, fig2, fig_leg
-    # ','.join(map(str, sf_idx_))
 
 
 # GRADIO APP
@@ -265,21 +204,16 @@ iface = gr.Interface(
     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.Image(type="pil", label="First Image"),
-        # gr.inputs.Image(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),
-        # gr.inputs.Checkbox(default=True, label="Show only matching SFs"),
         ],
     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")],
-        # gr.outputs.Textbox(label="SFs")],
-    # outputs=gr.outputs.Image(shape=(1024,2048), type="plot"),
     title=title,
     theme='peach',
     layout="horizontal",
@@ -287,7 +221,6 @@ iface = gr.Interface(
     article=article,
     examples=[
         ["chateau_1.png", "chateau_2.png", False, 3, 150, False, '170,15,25,63,193,125,92,214,107'],
-        # ["anafi1.jpeg", "anafi2.jpeg", False, 4, 150, False, '178,190,144,47,241, 172'],
         ["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'],