temp

app.py

@@ -1,7 +1,8 @@
 import gradio as gr
 
-import torch
+import cv2
 
+import torch
 
 import matplotlib.pyplot as plt
 from matplotlib import cm
@@ -14,8 +15,9 @@ import fire_network
 
 import numpy as np
 
+
+
 from PIL import Image
-from skimage.transform import resize
 
 # Possible Scales for multiscale inference
 scales = [2.0, 1.414, 1.0, 0.707, 0.5, 0.353, 0.25] 
@@ -78,17 +80,17 @@ def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50):
         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)
-    print(all_att_bin2[0].shape)
+
     
     fin_img = []
     img1rsz = np.copy(im1)
-    print(img1rsz.shape)
+    print(img1rsz.size)
     for j, att in enumerate(all_att_bin1):
-        # att = cv2.resize(att, imgz[i].shape[:2][::-1], interpolation=cv2.INTER_NEAREST)
+        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 = resize(att, im1.shape[:2])
-        print(att.shape)
+        # 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]
@@ -99,10 +101,11 @@ def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50):
             
     img2rsz = np.copy(im2)
     for j, att in enumerate(all_att_bin2):
-        # att = cv2.resize(att, imgz[i].shape[:2][::-1], interpolation=cv2.INTER_NEAREST)
+        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 = resize(att, im2.shape[:2])
+        # # 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]
@@ -132,8 +135,8 @@ article = "<p style='text-align: center'><a href='https://github.com/naver/fire'
 iface = gr.Interface(
     fn=generate_matching_superfeatures,
     inputs=[
-        gr.inputs.Image(shape=(1024, 1024), type="numpy"),
-        gr.inputs.Image(shape=(1024, 1024), type="numpy"),
+        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",