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import gradio as gr
from PIL import Image, ImageFilter
import numpy as np
import cv2
import torch
from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation, DPTFeatureExtractor, DPTForDepthEstimation
# Load models
segformer_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b1-finetuned-ade-512-512")
segformer_model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b1-finetuned-ade-512-512")
dpt_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
dpt_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
# Gaussian Blur Background Function
def gaussian_blur_background(image):
# Preprocess image for segmentation
inputs = segformer_extractor(images=image, return_tensors="pt")
outputs = segformer_model(**inputs)
logits = outputs.logits
segmentation = torch.argmax(logits, dim=1)[0].numpy()
# Create a binary mask for 'person' class (class index 12)
human_mask = (segmentation == 12).astype(np.uint8) * 255
human_mask_image = Image.fromarray(human_mask).resize(image.size)
# Apply Gaussian blur to the entire image
blurred_background = image.filter(ImageFilter.GaussianBlur(15))
# Composite the original image with blurred background using the mask
composite_image = Image.composite(image, blurred_background, human_mask_image)
return composite_image
# Depth-Based Lens Blur Function
def lens_blur(image):
# Preprocess image for depth estimation
inputs = dpt_extractor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = dpt_model(**inputs)
depth_map = outputs.predicted_depth.squeeze().cpu().numpy()
# Normalize depth map to range [0, 15] and invert for blur intensity
depth_map = (depth_map - depth_map.min()) / (depth_map.max() - depth_map.min()) * 15
depth_map = 15 - depth_map
depth_map_resized = cv2.resize(depth_map, (image.width, image.height))
# Convert image to OpenCV format
image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
blurred_image = np.zeros_like(image_cv, dtype=np.float32)
# Apply variable blur based on depth
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)
blurred_image_pil = Image.fromarray(cv2.cvtColor(blurred_image, cv2.COLOR_BGR2RGB))
return blurred_image_pil
# Gradio Interface
def process_image(image, effect):
if effect == "Gaussian Blur Background":
return gaussian_blur_background(image)
elif effect == "Lens Blur":
return lens_blur(image)
with gr.Blocks() as demo:
gr.Markdown("# BokehBot: Gaussian and Lens Blur Effects")
with gr.Row():
with gr.Column():
uploaded_image = gr.Image(type="pil", label="Upload an Image")
effect = gr.Radio(["Gaussian Blur Background", "Lens Blur"], label="Choose Effect")
process_button = gr.Button("Apply Effect")
with gr.Column():
output_image = gr.Image(type="pil", label="Processed Image")
process_button.click(process_image, inputs=[uploaded_image, effect], outputs=output_image)
demo.launch()
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