Update app.py

app.py

@@ -1,199 +1,199 @@
-import tensorflow as tf
-from keras import models
-import numpy as np
-import gradio as gr
-import cv2
-
-# Load the model
-try:
-    generator = models.load_model("generator.keras")
-    print("Model loaded successfully!")
-except Exception as e:
-    print("Error loading model:", e)
-
-
-# Function to preprocess the image (resize, normalize)
-def preprocess_image(img):
-    img = cv2.resize(img, (256, 256))
-
-    # Convert L to range [-1, 1]
-    img = img.astype("float32")
-    img = (img / 127.5) - 1
-
-    # Convert to tensor
-    img = tf.convert_to_tensor(img, dtype=tf.float32)
-
-    img = tf.expand_dims(img, axis=-1)  # Add image dimension
-    img = tf.expand_dims(img, axis=0)  # Add batch dimension
-
-    return img
-
-
-# Function to postprocess the image (denormalize)
-def postprocess_image(img):
-    img = cv2.cvtColor(((img + 1) * 127.5).numpy().astype(np.uint8), cv2.COLOR_LAB2RGB)
-    return np.uint8(np.clip(img, 0, 255))
-
-
-# Function to adjust brightness
-def adjust_brightness(img, brightness=0.0):
-    # Apply brightness adjustment
-    img = cv2.convertScaleAbs(img, beta=int(brightness * 127.0 / 4.0))
-    return np.uint8(np.clip(img, 0, 255))
-
-
-# Function to adjust contrast
-def adjust_contrast(img, contrast=0.0):
-    # Apply contrast adjustment
-    img = cv2.convertScaleAbs(img, alpha=(contrast * 0.75 + 1.0))
-    return np.uint8(np.clip(img, 0, 255))
-
-
-# Function to adjust hue
-def adjust_hue(img, hue_shift=0.0):
-    # Convert the image to HSV
-    hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
-
-    # Adjust the hue channel (value is between 0 and 179 in OpenCV's HSV)
-    hsv_img[:, :, 0] = (
-        hsv_img[:, :, 0] + hue_shift * 90
-    ) % 180  # Hue is wrapped in OpenCV HSV format
-
-    # Convert back to BGR
-    img = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2BGR)
-
-    return np.uint8(np.clip(img, 0, 255))
-
-
-def adjust_saturation(img, saturation_factor=0.0):
-    # Convert the image to HSV
-    hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
-
-    # Adjust the saturation channel (index 1 in HSV)
-    hsv_img[:, :, 1] = np.clip(hsv_img[:, :, 1] * (saturation_factor + 1.0), 0, 255)
-
-    # Convert back to BGR
-    img = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2BGR)
-
-    return np.uint8(np.clip(img, 0, 255))
-
-
-# Define the inference function
-def colorize_image(input_image):
-    # Preprocess the image for the model
-    preprocessed_image = preprocess_image(input_image)
-
-    # Predict using the model
-    output_ab = generator.predict(preprocessed_image)
-    output = tf.concat([preprocessed_image[0], output_ab[0]], axis=-1)
-
-    # Postprocess the output
-    output_image = postprocess_image(output)
-
-    return output_image
-
-
-# Function to colorize and store the result for further manipulation
-def colorize_and_store(img):
-    # Colorize the image
-    colorized_image = colorize_image(img)
-
-    # Return the colorized image for further manipulation (no model call)
-    return colorized_image, colorized_image
-
-
-def make_grayscale_256(img):
-    img = cv2.resize(img, (256, 256))
-    # img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
-    return img
-
-
-css = """
-h1 {
-    text-align: center;
-    display:block;
-    font-size:5rem;
-}
-p {
-    text-align: center;
-    display:block;
-    font-size:2rem;
-}
-#input-image img {
-    filter: grayscale(1);
-}
-"""
-
-# Gradio Interface
-with gr.Blocks(css=css) as demo:
-    demo.title = "Portrait Colorizer"
-    # Add a title
-    gr.Markdown("# Portrait Colorizer")
-    gr.Markdown(
-        "Upload a grayscale image to colorize it and fine-tune the output using the sliders below."
-    )
-
-    with gr.Row():
-        input_image = gr.Image(
-            type="numpy",
-            label="Grayscale Image",
-            image_mode="L",
-            height=256,
-            width=256,
-            elem_id="input-image",
-        )
-        output_image = gr.Image(
-            type="numpy",
-            label="Colorized Image",
-            image_mode="RGB",
-            height=256,
-            width=256,
-        )
-    process_button = gr.Button("Colorize")
-    bright_slider = gr.Slider(-1.0, 1.0, value=0.0, label="Brightness")
-    cont_slider = gr.Slider(-1.0, 1.0, value=0.0, label="Contrast")
-    sat_slider = gr.Slider(-1.0, 1.0, value=0.0, label="Saturation")
-    hue_slider = gr.Slider(-1.0, 1.0, value=0.0, label="Hue")
-
-    # Initially colorize and display the image when it is uploaded
-    colorized_image = gr.State()
-
-    # Button click triggers processing
-    process_button.click(
-        fn=colorize_and_store,
-        inputs=input_image,
-        outputs=[colorized_image, output_image],
-    )
-
-    # Apply hue adjustment to the stored colorized image (no re-generation)
-    bright_slider.change(
-        fn=adjust_brightness,
-        inputs=[colorized_image, bright_slider],
-        outputs=output_image,  # Update output image
-    )
-
-    # Apply hue adjustment to the stored colorized image (no re-generation)
-    cont_slider.change(
-        fn=adjust_contrast,
-        inputs=[colorized_image, cont_slider],
-        outputs=output_image,  # Update output image
-    )
-
-    # Apply hue adjustment to the stored colorized image (no re-generation)
-    hue_slider.change(
-        fn=adjust_hue,
-        inputs=[colorized_image, hue_slider],
-        outputs=output_image,  # Update output image
-    )
-
-    # Apply saturation adjustment to the stored colorized image (no re-generation)
-    sat_slider.change(
-        fn=adjust_saturation,
-        inputs=[colorized_image, sat_slider],
-        outputs=output_image,  # Update output image
-    )
-
-# Launch the app
-if __name__ == "__main__":
-    demo.launch()
+import tensorflow as tf
+from keras import models
+import numpy as np
+import gradio as gr
+import cv2
+
+# Load the model
+try:
+    generator = models.load_model("generator.keras")
+    print("Model loaded successfully!")
+except Exception as e:
+    print("Error loading model:", e)
+
+
+# Function to preprocess the image (resize, normalize)
+def preprocess_image(img):
+    img = cv2.resize(img, (256, 256))
+
+    # Convert L to range [-1, 1]
+    img = img.astype("float32")
+    img = (img / 127.5) - 1
+
+    # Convert to tensor
+    img = tf.convert_to_tensor(img, dtype=tf.float32)
+
+    img = tf.expand_dims(img, axis=-1)  # Add image dimension
+    img = tf.expand_dims(img, axis=0)  # Add batch dimension
+
+    return img
+
+
+# Function to postprocess the image (denormalize)
+def postprocess_image(img):
+    img = cv2.cvtColor(((img + 1) * 127.5).numpy().astype(np.uint8), cv2.COLOR_LAB2RGB)
+    return np.uint8(np.clip(img, 0, 255))
+
+
+# Function to adjust brightness
+def adjust_brightness(img, brightness=0.0):
+    # Apply brightness adjustment
+    img = cv2.convertScaleAbs(img, beta=int(brightness * 127.0 / 4.0))
+    return np.uint8(np.clip(img, 0, 255))
+
+
+# Function to adjust contrast
+def adjust_contrast(img, contrast=0.0):
+    # Apply contrast adjustment
+    img = cv2.convertScaleAbs(img, alpha=(contrast * 0.75 + 1.0))
+    return np.uint8(np.clip(img, 0, 255))
+
+
+# Function to adjust hue
+def adjust_hue(img, hue_shift=0.0):
+    # Convert the image to HSV
+    hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+
+    # Adjust the hue channel (value is between 0 and 179 in OpenCV's HSV)
+    hsv_img[:, :, 0] = (
+        hsv_img[:, :, 0] + hue_shift * 90
+    ) % 180  # Hue is wrapped in OpenCV HSV format
+
+    # Convert back to BGR
+    img = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2BGR)
+
+    return np.uint8(np.clip(img, 0, 255))
+
+
+def adjust_saturation(img, saturation_factor=0.0):
+    # Convert the image to HSV
+    hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+
+    # Adjust the saturation channel (index 1 in HSV)
+    hsv_img[:, :, 1] = np.clip(hsv_img[:, :, 1] * (saturation_factor + 1.0), 0, 255)
+
+    # Convert back to BGR
+    img = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2BGR)
+
+    return np.uint8(np.clip(img, 0, 255))
+
+
+# Define the inference function
+def colorize_image(input_image):
+    # Preprocess the image for the model
+    preprocessed_image = preprocess_image(input_image)
+
+    # Predict using the model
+    output_ab = generator.predict(preprocessed_image)
+    output = tf.concat([preprocessed_image[0], output_ab[0]], axis=-1)
+
+    # Postprocess the output
+    output_image = postprocess_image(output)
+
+    return output_image
+
+
+# Function to colorize and store the result for further manipulation
+def colorize_and_store(img):
+    # Colorize the image
+    colorized_image = colorize_image(img)
+
+    # Return the colorized image for further manipulation (no model call)
+    return colorized_image, colorized_image
+
+
+def make_grayscale_256(img):
+    img = cv2.resize(img, (256, 256))
+    # img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    return img
+
+
+css = """
+h1 {
+    text-align: center;
+    display:block;
+    font-size:4rem;
+}
+p {
+    text-align: center;
+    display:block;
+    font-size:2rem;
+}
+#input-image img {
+    filter: grayscale(1);
+}
+"""
+
+# Gradio Interface
+with gr.Blocks(css=css) as demo:
+    demo.title = "Portrait Colorizer"
+    # Add a title
+    gr.Markdown("# Portrait Colorizer")
+    gr.Markdown(
+        "Upload a grayscale image to colorize it and fine-tune the output using the sliders below."
+    )
+
+    with gr.Row():
+        input_image = gr.Image(
+            type="numpy",
+            label="Grayscale Image",
+            image_mode="L",
+            height=256,
+            width=256,
+            elem_id="input-image",
+        )
+        output_image = gr.Image(
+            type="numpy",
+            label="Colorized Image",
+            image_mode="RGB",
+            height=256,
+            width=256,
+        )
+    process_button = gr.Button("Colorize")
+    bright_slider = gr.Slider(-1.0, 1.0, value=0.0, label="Brightness")
+    cont_slider = gr.Slider(-1.0, 1.0, value=0.0, label="Contrast")
+    sat_slider = gr.Slider(-1.0, 1.0, value=0.0, label="Saturation")
+    hue_slider = gr.Slider(-1.0, 1.0, value=0.0, label="Hue")
+
+    # Initially colorize and display the image when it is uploaded
+    colorized_image = gr.State()
+
+    # Button click triggers processing
+    process_button.click(
+        fn=colorize_and_store,
+        inputs=input_image,
+        outputs=[colorized_image, output_image],
+    )
+
+    # Apply hue adjustment to the stored colorized image (no re-generation)
+    bright_slider.change(
+        fn=adjust_brightness,
+        inputs=[colorized_image, bright_slider],
+        outputs=output_image,  # Update output image
+    )
+
+    # Apply hue adjustment to the stored colorized image (no re-generation)
+    cont_slider.change(
+        fn=adjust_contrast,
+        inputs=[colorized_image, cont_slider],
+        outputs=output_image,  # Update output image
+    )
+
+    # Apply hue adjustment to the stored colorized image (no re-generation)
+    hue_slider.change(
+        fn=adjust_hue,
+        inputs=[colorized_image, hue_slider],
+        outputs=output_image,  # Update output image
+    )
+
+    # Apply saturation adjustment to the stored colorized image (no re-generation)
+    sat_slider.change(
+        fn=adjust_saturation,
+        inputs=[colorized_image, sat_slider],
+        outputs=output_image,  # Update output image
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()