Initial commit app.py

app.py

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+import gradio as gr
+from PIL import Image, ImageFilter
+import numpy as np
+import cv2
+import torch
+from transformers import DPTFeatureExtractor, DPTForDepthEstimation
+
+# Load model and feature extractor
+feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
+model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
+
+# Gaussian Blur function
+def apply_gaussian_blur(image, blur_radius):
+    return image.filter(ImageFilter.GaussianBlur(blur_radius))
+
+# Lens Blur function
+def apply_lens_blur(image):
+    # Get depth map
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        depth_map = outputs.predicted_depth.squeeze().cpu().numpy()
+    depth_map = (depth_map - depth_map.min()) / (depth_map.max() - depth_map.min()) * 15
+    depth_map_resized = cv2.resize(depth_map, (image.width, image.height))
+    depth_map_resized = 15 - depth_map_resized
+
+    # Convert to OpenCV format
+    image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    blurred_image = np.zeros_like(image_cv, dtype=np.float32)
+
+    for blur_radius in range(1, 16):
+        blurred_layer = cv2.GaussianBlur(image_cv, (0, 0), sigmaX=blur_radius)
+        mask = ((depth_map_resized >= (blur_radius - 1)) & (depth_map_resized < blur_radius)).astype(np.float32)
+        mask = cv2.merge([mask] * 3)
+        blurred_image += blurred_layer * mask
+
+    blurred_image = np.clip(blurred_image, 0, 255).astype(np.uint8)
+    return Image.fromarray(cv2.cvtColor(blurred_image, cv2.COLOR_BGR2RGB))
+
+# Gradio app interface
+def process_image(image, effect, blur_radius):
+    if effect == "Gaussian Blur":
+        return apply_gaussian_blur(image, blur_radius)
+    elif effect == "Lens Blur":
+        return apply_lens_blur(image)
+    else:
+        return image
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Gaussian and Lens Blur Effects")
+    with gr.Row():
+        with gr.Column():
+            uploaded_image = gr.Image(type="pil")
+            effect = gr.Radio(["Gaussian Blur", "Lens Blur"], value="Gaussian Blur", label="Effect")
+            blur_radius = gr.Slider(1, 15, value=5, step=1, label="Blur Radius (for Gaussian Blur)")
+            submit_button = gr.Button("Apply Effect")
+        with gr.Column():
+            output_image = gr.Image(type="pil", label="Processed Image")
+
+    submit_button.click(process_image, inputs=[uploaded_image, effect, blur_radius], outputs=output_image)
+
+# Launch the app
+demo.launch()