YannisK commited on
Commit
7497da3
1 Parent(s): 689e965
.ipynb_checkpoints/app-checkpoint.py DELETED
@@ -1,165 +0,0 @@
1
- import gradio as gr
2
-
3
- import cv2
4
-
5
- import torch
6
-
7
- import matplotlib.pyplot as plt
8
- from matplotlib import cm
9
- from matplotlib import colors
10
- from mpl_toolkits.axes_grid1 import ImageGrid
11
-
12
- from torchvision import transforms
13
-
14
- import fire_network
15
-
16
- import numpy as np
17
-
18
-
19
-
20
- from PIL import Image
21
-
22
- # Possible Scales for multiscale inference
23
- scales = [2.0, 1.414, 1.0, 0.707, 0.5, 0.353, 0.25]
24
-
25
- device = 'cpu'
26
-
27
- # Load net
28
- state = torch.load('fire.pth', map_location='cpu')
29
- state['net_params']['pretrained'] = None # no need for imagenet pretrained model
30
- net = fire_network.init_network(**state['net_params']).to(device)
31
- net.load_state_dict(state['state_dict'])
32
-
33
- transform = transforms.Compose([
34
- transforms.Resize(1024),
35
- transforms.ToTensor(),
36
- transforms.Normalize(**dict(zip(["mean", "std"], net.runtime['mean_std'])))
37
- ])
38
-
39
-
40
- # which sf
41
- sf_idx_ = [55, 14, 5, 4, 52, 57, 40, 9]
42
-
43
- col = plt.get_cmap('tab10')
44
-
45
- def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50):
46
-
47
- im1_tensor = transform(im1).unsqueeze(0)
48
- im2_tensor = transform(im2).unsqueeze(0)
49
-
50
- im1_cv = np.array(im1)[:, :, ::-1].copy()
51
- im2_cv = np.array(im2)[:, :, ::-1].copy()
52
-
53
- # extract features
54
- with torch.no_grad():
55
- output1 = net.get_superfeatures(im1_tensor.to(device), scales=[scale_id])
56
- feats1 = output1[0][0]
57
- attns1 = output1[1][0]
58
- strenghts1 = output1[2][0]
59
-
60
- output2 = net.get_superfeatures(im2_tensor.to(device), scales=[scale_id])
61
- feats2 = output2[0][0]
62
- attns2 = output2[1][0]
63
- strenghts2 = output2[2][0]
64
-
65
- print(feats1.shape, feats2.shape)
66
- print(attns1.shape, attns2.shape)
67
- print(strenghts1.shape, strenghts2.shape)
68
-
69
- # Store all binary SF att maps to show them all at once in the end
70
- all_att_bin1 = []
71
- all_att_bin2 = []
72
- for n, i in enumerate(sf_idx_):
73
- # all_atts[n].append(attn[j][scale_id][0,i,:,:].numpy())
74
- att_heat = np.array(attns1[0,i,:,:].numpy(), dtype=np.float32)
75
- att_heat = np.uint8(att_heat / np.max(att_heat[:]) * 255.0)
76
- att_heat_bin = np.where(att_heat>threshold, 255, 0)
77
- print(att_heat_bin)
78
- all_att_bin1.append(att_heat_bin)
79
-
80
- att_heat = np.array(attns2[0,i,:,:].numpy(), dtype=np.float32)
81
- att_heat = np.uint8(att_heat / np.max(att_heat[:]) * 255.0)
82
- att_heat_bin = np.where(att_heat>threshold, 255, 0)
83
- all_att_bin2.append(att_heat_bin)
84
-
85
-
86
- fin_img = []
87
- img1rsz = np.copy(im1_cv)
88
- print(im1.size)
89
- print(img1rsz.shape)
90
- for j, att in enumerate(all_att_bin1):
91
- att = cv2.resize(att, im1.size, interpolation=cv2.INTER_NEAREST)
92
- # att = cv2.resize(att, imgz[i].shape[:2][::-1], interpolation=cv2.INTER_CUBIC)
93
- # att = cv2.resize(att, imgz[i].shape[:2][::-1])
94
- # att = att.resize(shape)
95
- # att = resize(att, im1.size)
96
- mask2d = zip(*np.where(att==255))
97
- for m,n in mask2d:
98
- col_ = col.colors[j] if j < 7 else col.colors[j+1]
99
- if j == 0: col_ = col.colors[9]
100
- col_ = 255*np.array(colors.to_rgba(col_))[:3]
101
- img1rsz[m,n, :] = col_[::-1]
102
- fin_img.append(img1rsz)
103
-
104
- img2rsz = np.copy(im2_cv)
105
- for j, att in enumerate(all_att_bin2):
106
- att = cv2.resize(att, im2.size, interpolation=cv2.INTER_NEAREST)
107
- # att = cv2.resize(att, imgz[i].shape[:2][::-1], interpolation=cv2.INTER_CUBIC)
108
- # # att = cv2.resize(att, imgz[i].shape[:2][::-1])
109
- # att = att.resize(im2.shape)
110
- # print('att:', att.shape)
111
- mask2d = zip(*np.where(att==255))
112
- for m,n in mask2d:
113
- col_ = col.colors[j] if j < 7 else col.colors[j+1]
114
- if j == 0: col_ = col.colors[9]
115
- col_ = 255*np.array(colors.to_rgba(col_))[:3]
116
- img2rsz[m,n, :] = col_[::-1]
117
- fin_img.append(img2rsz)
118
-
119
-
120
- fig = plt.figure()
121
- grid = ImageGrid(fig, 111, nrows_ncols=(2, 1), axes_pad=0.1)
122
- for ax, img in zip(grid, fin_img):
123
- ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
124
- ax.axis('scaled')
125
- ax.axis('off')
126
- plt.tight_layout()
127
- # fig.suptitle("Matching SFs", fontsize=16)
128
-
129
- # fig.canvas.draw()
130
- # # Now we can save it to a numpy array.
131
- # data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
132
- # data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
133
- return fig
134
-
135
-
136
- # GRADIO APP
137
- title = "Visualizing Super-features"
138
- description = "TBD"
139
- article = "<p style='text-align: center'><a href='https://github.com/naver/fire' target='_blank'>Original Github Repo</a></p>"
140
-
141
-
142
- # css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}"
143
- # css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }"
144
- # css = ".output_image, .input_image {height: 600px !important}"
145
- css = ".input_image {height: 600px !important} .output_image, {height: 1200px !important}"
146
- # css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}"
147
-
148
-
149
- iface = gr.Interface(
150
- fn=generate_matching_superfeatures,
151
- inputs=[
152
- gr.inputs.Image(shape=(1024, 1024), type="pil"),
153
- gr.inputs.Image(shape=(1024, 1024), type="pil"),
154
- gr.inputs.Slider(minimum=1, maximum=7, step=1, default=2, label="Scale"),
155
- gr.inputs.Slider(minimum=1, maximum=255, step=25, default=50, label="Binarizatio Threshold")],
156
- outputs="plot",
157
- # outputs=gr.outputs.Image(shape=(1024,2048), type="plot"),
158
- enable_queue=True,
159
- title=title,
160
- description=description,
161
- article=article,
162
- css=css,
163
- examples=[["chateau_1.png", "chateau_2.png", 6, 50]],
164
- )
165
- iface.launch()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
__pycache__/fire_network.cpython-37.pyc DELETED
Binary file (4.67 kB)
 
__pycache__/lit.cpython-37.pyc DELETED
Binary file (2.59 kB)
 
gradio_queue.db DELETED
File without changes
gradio_queue.db-journal DELETED
Binary file (512 Bytes)
 
how/__pycache__/__init__.cpython-37.pyc DELETED
Binary file (296 Bytes)
 
how/layers/__pycache__/__init__.cpython-37.pyc DELETED
Binary file (312 Bytes)
 
how/layers/__pycache__/attention.cpython-37.pyc DELETED
Binary file (674 Bytes)
 
how/layers/__pycache__/dim_reduction.cpython-37.pyc DELETED
Binary file (1.5 kB)
 
how/layers/__pycache__/functional.cpython-37.pyc DELETED
Binary file (2.29 kB)
 
how/layers/__pycache__/pooling.cpython-37.pyc DELETED
Binary file (959 Bytes)
 
how/networks/__pycache__/__init__.cpython-37.pyc DELETED
Binary file (213 Bytes)
 
how/networks/__pycache__/how_net.cpython-37.pyc DELETED
Binary file (4.01 kB)
 
how/utils/__pycache__/__init__.cpython-37.pyc DELETED
Binary file (202 Bytes)
 
how/utils/__pycache__/whitening.cpython-37.pyc DELETED
Binary file (1.27 kB)