Added a new option + Fixed zoom

- New option: Result Image Scale
- Fixed: In-site zoom not really aligning with input image, as the result resolution can be at a different resolution than input

app.py

@@ -21,20 +21,19 @@ for model_path in ['fbcnn_gray.pth','fbcnn_color.pth']:
         r = requests.get(url, allow_redirects=True)
         open(model_path, 'wb').write(r.content)    
 
-def inference(input_img, is_gray, input_quality, zoom, x_shift, y_shift):
+def inference(input_img, is_gray, res_percentage, input_quality, zoom, x_shift, y_shift):
     
     print("datetime:", datetime.datetime.utcnow())
     input_img_width, input_img_height = Image.fromarray(input_img).size
     print("img size:", (input_img_width, input_img_height))
-    
-    if (input_img_width > 1080) or (input_img_height > 1080):
-        resize_ratio = min(1080/input_img_width, 1080/input_img_height)
-        resized_input = Image.fromarray(input_img).resize(
-            (int(input_img_width*resize_ratio) + (input_img_width*resize_ratio < 1),
-             int(input_img_height*resize_ratio) + (input_img_height*resize_ratio < 1)),
-            resample=Image.BICUBIC)
-        input_img = np.array(resized_input)
-        print("input image resized to:", resized_input.size)
+
+    resized_input = Image.fromarray(input_img).resize(
+        (
+            int(input_img_width * (res_percentage/100)),
+            int(input_img_height * (res_percentage/100))
+        ), resample = Image.BICUBIC)
+    input_img = np.array(resized_input)
+    print("input image resized to:", resized_input.size)
 
     if is_gray:
         n_channels = 1
@@ -148,8 +147,8 @@ def zoom_image(zoom, x_shift, y_shift, input_img, output_img = None):
     y_offset = int((img_h - zoom_h) * y_shift)
     
     crop_box = (x_offset, y_offset, x_offset + zoom_w, y_offset + zoom_h)
-    img = img.crop(crop_box).resize((img_w, img_h), Image.BILINEAR)
-    out_img = out_img.crop(crop_box).resize((img_w, img_h), Image.BILINEAR)
+    img = img.resize((img_w, img_h), Image.BILINEAR).crop(crop_box)
+    out_img = out_img.resize((img_w, img_h), Image.BILINEAR).crop(crop_box)
 
     return (img, out_img)
     
@@ -161,6 +160,7 @@ with gr.Blocks() as demo:
         output_img = gr.Image(label="Result")
     
     is_gray = gr.Checkbox(label="Grayscale (Check this if your image is grayscale)")
+    max_res = gr.Slider(1, 100, step=0.5, label="Output image resolution Percentage (Higher% = longer processing time)")
     input_quality = gr.Slider(1, 100, step=1, label="Intensity (Higher = stronger JPEG artifact removal)")
     zoom = gr.Slider(10, 100, step=1, value=50, label="Zoom Percentage (0 = original size)")
     x_shift = gr.Slider(0, 100, step=1, label="Horizontal shift Percentage (Before/After)")
@@ -173,19 +173,19 @@ with gr.Blocks() as demo:
 
     run.click(
         inference, 
-        inputs=[input_img, is_gray, input_quality, zoom, x_shift, y_shift], 
+        inputs=[input_img, is_gray, max_res, input_quality, zoom, x_shift, y_shift], 
         outputs=[output_img, before_after]
     )
     
     gr.Examples([
-        ["doraemon.jpg", False, 60, 58, 50, 50],
-        ["tomandjerry.jpg", False, 60, 60, 57, 44],
-        ["somepanda.jpg", True, 100, 70, 8, 24],
-        ["cemetry.jpg", False, 70, 80, 76, 62],
-        ["michelangelo_david.jpg", True, 30, 88, 53, 27],
-        ["elon_musk.jpg", False, 45, 75, 33, 30],
-        ["text.jpg", True, 70, 50, 11, 29]
-    ], inputs=[input_img, is_gray, input_quality, zoom, x_shift, y_shift])
+        ["doraemon.jpg", False, 100, 60, 58, 50, 50],
+        ["tomandjerry.jpg", False,  100, 60, 60, 57, 44],
+        ["somepanda.jpg", True,  100, 100, 70, 8, 24],
+        ["cemetry.jpg", False,  100, 70, 80, 76, 62],
+        ["michelangelo_david.jpg", True,  100, 30, 88, 53, 27],
+        ["elon_musk.jpg", False,  100, 45, 75, 33, 30],
+        ["text.jpg", True,  100, 70, 50, 11, 29]
+    ], inputs=[input_img, is_gray, max_res, input_quality, zoom, x_shift, y_shift])
     
     zoom.release(zoom_image, inputs=[zoom, x_shift, y_shift, input_img], outputs=[before_after])
     x_shift.release(zoom_image, inputs=[zoom, x_shift, y_shift, input_img], outputs=[before_after])

requirements.txt

@@ -5,3 +5,4 @@ gradio
 jinja2
 matplotlib
 gradio_imageslider
+pydantic==2.10.6