temp

.ipynb_checkpoints/app-checkpoint.py

@@ -1,165 +0,0 @@
-import gradio as gr
-
-import cv2
-
-import torch
-
-import matplotlib.pyplot as plt
-from matplotlib import cm
-from matplotlib import colors
-from mpl_toolkits.axes_grid1 import ImageGrid
-
-from torchvision import transforms
-
-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 net
-state = torch.load('fire.pth', map_location='cpu')
-state['net_params']['pretrained'] = None # no need for imagenet pretrained model
-net = fire_network.init_network(**state['net_params']).to(device)
-net.load_state_dict(state['state_dict'])
-
-transform = transforms.Compose([
-        transforms.Resize(1024),
-        transforms.ToTensor(), 
-        transforms.Normalize(**dict(zip(["mean", "std"], net.runtime['mean_std'])))
-        ])
-
-
-# which sf
-sf_idx_ = [55, 14, 5, 4, 52, 57, 40, 9]
-
-col = plt.get_cmap('tab10')
-
-def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50):
-
-    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=[scale_id])
-        feats1 = output1[0][0]
-        attns1 = output1[1][0]
-        strenghts1 = output1[2][0]
-
-        output2 = net.get_superfeatures(im2_tensor.to(device), scales=[scale_id])
-        feats2 = output2[0][0]
-        attns2 = output2[1][0]
-        strenghts2 = output2[2][0]
-
-    print(feats1.shape, feats2.shape)
-    print(attns1.shape, attns2.shape)
-    print(strenghts1.shape, strenghts2.shape)
-    
-    # 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_):
-        # 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)
-        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)
-    print(im1.size)
-    print(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] 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]   
-    fin_img.append(img1rsz)
-            
-    img2rsz = np.copy(im2_cv)
-    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] 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]   
-    fin_img.append(img2rsz)
-    
-
-    fig = plt.figure()
-    grid = ImageGrid(fig, 111, nrows_ncols=(2, 1),  axes_pad=0.1)
-    for ax, img in zip(grid, fin_img):
-        ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
-        ax.axis('scaled')
-        ax.axis('off')
-    plt.tight_layout()
-    # fig.suptitle("Matching SFs", fontsize=16)
-
-    # fig.canvas.draw()
-    # # Now we can save it to a numpy array.
-    # data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
-    # data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    return fig
-
-
-# GRADIO APP
-title = "Visualizing Super-features"
-description = "TBD"
-article = "<p style='text-align: center'><a href='https://github.com/naver/fire' target='_blank'>Original Github Repo</a></p>"
-
-
-# css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}"
-# css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }"
-# css = ".output_image, .input_image {height: 600px !important}"
-css = ".input_image {height: 600px !important} .output_image, {height: 1200px !important}"
-# css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}"
-
-
-iface = gr.Interface(
-    fn=generate_matching_superfeatures,
-    inputs=[
-        gr.inputs.Image(shape=(1024, 1024), type="pil"),
-        gr.inputs.Image(shape=(1024, 1024), type="pil"),
-        gr.inputs.Slider(minimum=1, maximum=7, step=1, default=2, label="Scale"),
-        gr.inputs.Slider(minimum=1, maximum=255, step=25, default=50, label="Binarizatio Threshold")],
-    outputs="plot",
-    # outputs=gr.outputs.Image(shape=(1024,2048), type="plot"),
-    enable_queue=True,
-    title=title,
-    description=description,
-    article=article,
-    css=css,
-    examples=[["chateau_1.png", "chateau_2.png", 6, 50]],
-)
-iface.launch()