del resize, change output

app.py

@@ -1,6 +1,10 @@
 import gradio as gr
 import torch
 from utils import *
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import io
 
 torch.hub.download_url_to_file(
     'https://github.com/aalto-ui/aim/raw/aim2/backend/data/tests/input_values/wikipedia.org_website.png',
@@ -10,54 +14,32 @@ torch.hub.download_url_to_file(
     'aalto.fi_website.png')
 
 
+
 def inference(img, template, angel):
     color_image = cv2.imread(img.name, cv2.IMREAD_COLOR)
-    height, width, _ = color_image.shape
-
-    # Resize if it is bigeer than 960 * 800
-    if width > height:
-        if width > 960:  # 3/4 * 1280
-            coef_div = width / 960.0
-            color_image = cv2.resize(color_image, dsize=(int(width / coef_div), int(height / coef_div)),
-                                     interpolation=cv2.INTER_CUBIC)
-    else:
-        if height > 800:  # 800
-            coef_div = height / 800.0
-            color_image = cv2.resize(color_image, dsize=(int(width / coef_div), int(height / coef_div)),
-                                     interpolation=cv2.INTER_CUBIC)
 
     HSV_image = cv2.cvtColor(color_image, cv2.COLOR_BGR2HSV)
     selected_harmomic_scheme = HarmonicScheme(str(template), int(angel))
-    new_HSV_image = best_harmomic_scheme.hue_shifted(HSV_image, num_superpixels=-1)
+    new_HSV_image = selected_harmomic_scheme.hue_shifted(HSV_image, num_superpixels=-1)
 
     # Compute shifted histogram
     histo_1 = count_hue_histogram(HSV_image)
     histo_2 = count_hue_histogram(new_HSV_image)
 
     # Create Hue Plots
-    fig1 = plothis(histo_1, best_harmomic_scheme, "Source Hue")
+    fig1 = plothis(histo_1, selected_harmomic_scheme, "Source Hue")
     fig_1_cv = get_img_from_fig(fig1)
-    fig2 = plothis(histo_2, best_harmomic_scheme, "Target Hue")
+    fig2 = plothis(histo_2, selected_harmomic_scheme, "Target Hue")
     fig_2_cv = get_img_from_fig(fig2)
-
-    # Stack Hue Plots
-    vis = np.concatenate((fig_1_cv, fig_2_cv), axis=0)
     # Convert HSV to BGR
     result_image = cv2.cvtColor(new_HSV_image, cv2.COLOR_HSV2BGR)
 
-    # Final output
-    canvas = np.full((800, 960, 3), (255, 255, 255), dtype=np.uint8)
-    # compute center offset
-    x_center = (960 - width) // 2
-    y_center = (800 - height) // 2
-    # copy img image into center of result image
-    canvas[y_center:y_center + height, x_center:x_center + width] = result_image
+    cv2.imwrite('fig_1_cv.jpg', fig_1_cv)
+    cv2.imwrite('fig_2_cv.jpg', fig_1_cv)
+    cv2.imwrite('result_image.jpg', result_image)
 
-    # Combine
-    output = np.concatenate((vis, canvas), axis=1)
-    cv2.imwrite('output.png', output)
 
-    return ['output.png']
+    return ['result_image.jpg', 'fig_1_cv.jpg', 'fig_2_cv.jpg']
 
 
 title = 'Color Harmonization'
@@ -66,6 +48,12 @@ article = "<p style='text-align: center'></p>"
 examples = [['wikipedia.org_website.png'], ['aalto.fi_website.png']]
 css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
 
+title = 'Color Harmonization'
+description = 'Compute Color Harmonization with Different Templates'
+article = "<p style='text-align: center'></p>"
+examples = [['wikipedia.org_website.png'], ['aalto.fi_website.png']]
+# css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
+
 gr.Interface(
     inference,
     [gr.inputs.Image(type='file', label='Input'),
@@ -73,10 +61,12 @@ gr.Interface(
                         default="X",
                         label="Template"),
      gr.inputs.Slider(0, 359, label="Angle")],
-    [gr.outputs.Image(type='file', label='Color Harmonization of Output Image')],
+    [gr.outputs.Image(type='file', label='Color Harmonization of Output Image'),
+     gr.outputs.Image(type='file', label='Source Hue'),
+     gr.outputs.Image(type='file', label='Target Hue'),],
     title=title,
     description=description,
     article=article,
     examples=examples,
-    css=css,
+    # css=css,
 ).launch(debug=True, enable_queue=True)