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| import gradio as gr | |
| from PIL import Image, ImageFilter | |
| import numpy as np | |
| import torch | |
| import cv2 | |
| from transformers import AutoImageProcessor, AutoModelForDepthEstimation | |
| # Load depth estimation model | |
| image_processor = AutoImageProcessor.from_pretrained("depth-anything/Depth-Anything-V2-Small-hf") | |
| model = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Small-hf") | |
| def apply_gaussian_blur(image, mask): | |
| """Applies Gaussian blur to the background based on a user-drawn mask.""" | |
| if mask is None: | |
| return image # If no mask is provided, return the original image | |
| # Ensure mask is grayscale and resized to match image dimensions | |
| mask_pil = Image.fromarray(mask).convert("L").resize(image.size) | |
| mask_array = np.array(mask_pil) | |
| # Create a blurred background | |
| blurred_background = image.filter(ImageFilter.GaussianBlur(radius=15)) | |
| # Convert images to NumPy arrays | |
| img_array = np.array(image) | |
| blurred_array = np.array(blurred_background) | |
| # Create a boolean mask (foreground = True, background = False) | |
| foreground_mask = mask_array > 0 | |
| foreground_mask_3d = np.stack([foreground_mask] * 3, axis=-1) | |
| # Blend the original image with the blurred background | |
| final_image_array = np.where(foreground_mask_3d, img_array, blurred_array) | |
| final_image = Image.fromarray(final_image_array.astype(np.uint8)) | |
| return final_image | |
| def apply_lens_blur(image): | |
| """Applies depth-based lens blur using a pre-trained model.""" | |
| # Resize image to 512x512 for processing | |
| resized_image = image.resize((512, 512)) | |
| image_np = np.array(resized_image) | |
| # Prepare image for the model | |
| inputs = image_processor(images=resized_image, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| predicted_depth = outputs.predicted_depth | |
| # Interpolate depth map to match the image size | |
| prediction = torch.nn.functional.interpolate( | |
| predicted_depth.unsqueeze(1), | |
| size=resized_image.size[::-1], | |
| mode="bicubic", | |
| align_corners=False, | |
| ).squeeze() | |
| # Convert prediction to a NumPy array | |
| depth_map = prediction.cpu().numpy() | |
| # Normalize the depth map | |
| depth_norm = (depth_map - np.min(depth_map)) / (np.max(depth_map) - np.min(depth_map)) | |
| num_blur_levels = 5 | |
| blurred_layers = [] | |
| for i in range(num_blur_levels): | |
| sigma = i * 0.5 | |
| if sigma == 0: | |
| blurred = image_np | |
| else: | |
| blurred = cv2.GaussianBlur(image_np, (15, 15), sigmaX=sigma, sigmaY=sigma, borderType=cv2.BORDER_REPLICATE) | |
| blurred_layers.append(blurred) | |
| depth_indices = ((1 - depth_norm) * (num_blur_levels - 1)).astype(np.uint8) | |
| final_blurred_image = np.zeros_like(image_np) | |
| for y in range(image_np.shape[0]): | |
| for x in range(image_np.shape[1]): | |
| depth_index = depth_indices[y, x] | |
| final_blurred_image[y, x] = blurred_layers[depth_index][y, x] | |
| # Convert the final blurred image back to a PIL Image | |
| final_blurred_pil_image = Image.fromarray(final_blurred_image) | |
| return final_blurred_pil_image | |
| def process_image(image, mask, blur_type): | |
| """Processes the image based on the selected blur type.""" | |
| if blur_type == "Gaussian Blur": | |
| return apply_gaussian_blur(image, mask) | |
| elif blur_type == "Lens Blur": | |
| return apply_lens_blur(image) | |
| else: | |
| return image | |
| interface = gr.Interface( | |
| fn=process_image, | |
| inputs=[ | |
| gr.Image(type="pil", label="Upload an Image"), | |
| gr.Sketchpad(height=256, width=256, label="Draw Mask (Only for Gaussian Blur)"), | |
| gr.Radio(["Gaussian Blur", "Lens Blur"], label="Choose Blur Effect") | |
| ], | |
| outputs=gr.Image(type="pil"), | |
| title="Gaussian & Lens Blur Effects", | |
| description="Upload an image and select either Gaussian blur (with mask) or depth-based lens blur." | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch(share=True) | |