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| import os | |
| import imageio | |
| from PIL import Image | |
| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| import paddlehub as hub | |
| import onnxruntime | |
| # Download and setup models | |
| os.system("wget https://huggingface.co/Carve/LaMa-ONNX/resolve/main/lama_fp32.onnx") | |
| os.system("pip install onnxruntime imageio") | |
| os.makedirs("data", exist_ok=True) | |
| os.makedirs("dataout", exist_ok=True) | |
| # Load LaMa ONNX model | |
| sess_options = onnxruntime.SessionOptions() | |
| lama_model = onnxruntime.InferenceSession('lama_fp32.onnx', sess_options=sess_options) | |
| # Load U^2-Net model for automatic masking | |
| u2net_model = hub.Module(name='U2Net') | |
| # --- Helper Functions --- | |
| def prepare_image(image, target_size=(512, 512)): | |
| """Resizes and preprocesses image for LaMa model.""" | |
| if isinstance(image, Image.Image): | |
| image = image.resize(target_size) | |
| image = np.array(image) | |
| elif isinstance(image, np.ndarray): | |
| image = cv2.resize(image, target_size) | |
| else: | |
| raise ValueError("Input image should be either PIL Image or numpy array!") | |
| # Normalize to [0, 1] and convert to CHW format | |
| image = image.astype(np.float32) / 255.0 | |
| if image.ndim == 3: | |
| image = np.transpose(image, (2, 0, 1)) | |
| elif image.ndim == 2: | |
| image = image[np.newaxis, ...] | |
| return image[np.newaxis, ...] # Add batch dimension | |
| def generate_mask(image, method="automatic"): | |
| """Generates mask from image using U^2-Net or user input.""" | |
| if method == "automatic": | |
| input_size = 320 # Adjust based on U^2-Net requirements | |
| result = u2net_model.Segmentation( | |
| images=[cv2.cvtColor(image, cv2.COLOR_RGB2BGR)], | |
| paths=None, | |
| batch_size=1, | |
| input_size=input_size, | |
| output_dir='output', | |
| visualization=False | |
| ) | |
| mask = Image.fromarray(result[0]['mask']) | |
| mask = mask.resize((512, 512)) # Resize to match LaMa input | |
| mask.save("./data/data_mask.png") | |
| else: # "manual" | |
| mask = imageio.imread("./data/data_mask.png") | |
| mask = Image.fromarray(mask).convert("L") # Ensure grayscale | |
| mask = mask.resize((512, 512)) | |
| return prepare_image(mask, (512, 512)) | |
| def inpaint_image(image, mask): | |
| """Performs inpainting using the LaMa model.""" | |
| outputs = lama_model.run(None, {'image': image, 'mask': mask}) | |
| output = outputs[0][0] | |
| output = output.transpose(1, 2, 0) | |
| output = (output * 255).astype(np.uint8) | |
| return Image.fromarray(output) | |
| # --- Gradio Interface --- | |
| def process_image(input_image, mask_option): | |
| """Main function for Gradio interface.""" | |
| imageio.imwrite("./data/data.png", input_image) | |
| image = prepare_image(input_image) | |
| mask = generate_mask(input_image, method=mask_option) | |
| inpainted_image = inpaint_image(image, mask) | |
| inpainted_image = inpainted_image.resize(Image.open("./data/data.png").size) | |
| inpainted_image.save("./dataout/data_mask.png") | |
| return "./dataout/data_mask.png", "./data/data_mask.png" | |
| iface = gr.Interface( | |
| fn=process_image, | |
| inputs=[ | |
| gr.Image(label="Input Image", type="numpy"), | |
| gr.Radio(choices=["automatic", ], | |
| type="value", label="Masking Option") | |
| ], | |
| outputs=[ | |
| gr.Image(type="filepath", label="Inpainted Image"), | |
| gr.Image(type="filepath", label="Generated Mask") | |
| ], | |
| title="LaMa Image Inpainting", | |
| description="Image inpainting with LaMa and U^2-Net. Upload your image and choose automatic.", | |
| ) | |
| iface.launch() |