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| import gradio as gr | |
| import face_alignment | |
| from PIL import Image | |
| import numpy as np | |
| import cv2 | |
| import tempfile | |
| # Load model | |
| fa = face_alignment.FaceAlignment('2D', device='cpu', flip_input=False) | |
| def process_mask(mask_img): | |
| """Automatically remove white/transparent background from mask image""" | |
| mask_img = mask_img.convert("RGBA") | |
| datas = mask_img.getdata() | |
| new_data = [] | |
| white_threshold = 240 | |
| for item in datas: | |
| if (item[0] > white_threshold and | |
| item[1] > white_threshold and | |
| item[2] > white_threshold) or item[3] == 0: | |
| new_data.append((255, 255, 255, 0)) | |
| else: | |
| new_data.append(item) | |
| mask_img.putdata(new_data) | |
| return mask_img | |
| # Load and process mask | |
| original_mask = Image.open("mask.jpg").convert("RGBA") | |
| clean_mask = process_mask(original_mask) | |
| def fit_mask_to_face(image): | |
| """Apply mask to the face in the image""" | |
| img_np = np.array(image) | |
| landmarks = fa.get_landmarks(img_np) | |
| if not landmarks: | |
| return image | |
| image_rgb = cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB) | |
| for landmark in landmarks: | |
| left_eye = landmark[36:42] | |
| right_eye = landmark[42:48] | |
| left_pupil = np.mean(left_eye, axis=0).astype(int) | |
| right_pupil = np.mean(right_eye, axis=0).astype(int) | |
| eye_distance = np.linalg.norm(right_pupil - left_pupil).astype(int) | |
| scale_w = int(eye_distance * 3) | |
| scale_h = int(scale_w * clean_mask.size[1] / clean_mask.size[0]) | |
| resized_mask = clean_mask.resize((scale_w, scale_h), Image.Resampling.LANCZOS) | |
| center_x = int((left_pupil[0] + right_pupil[0]) / 2) | |
| center_y = int((left_pupil[1] + right_pupil[1]) / 2) - 20 | |
| top_left_x = center_x - scale_w // 2 | |
| top_left_y = center_y - scale_h // 2 | |
| background_pil = Image.fromarray(image_rgb).convert("RGBA") | |
| background_pil.paste(resized_mask, (top_left_x, top_left_y), resized_mask) | |
| return background_pil.convert("RGB") | |
| return image | |
| def capture_and_process(): | |
| """Capture image from webcam and process it""" | |
| temp_file = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) | |
| temp_path = temp_file.name | |
| cap = cv2.VideoCapture(0) | |
| ret, frame = cap.read() | |
| if ret: | |
| cv2.imwrite(temp_path, frame) | |
| pil_image = Image.open(temp_path) | |
| result = fit_mask_to_face(pil_image) | |
| cap.release() | |
| return result | |
| else: | |
| cap.release() | |
| return None | |
| # Create interface without CSS | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 🎭 Party Face Mask App") | |
| gr.Markdown("Upload an image or capture from your webcam to try on our party mask!") | |
| with gr.Tabs(): | |
| with gr.TabItem("📁 Upload Image"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| upload_input = gr.Image(label="Upload your photo", type="pil") | |
| upload_button = gr.Button("Apply Mask") | |
| with gr.Column(): | |
| upload_output = gr.Image(label="Result", type="pil") | |
| upload_button.click(fn=fit_mask_to_face, inputs=upload_input, outputs=upload_output) | |
| with gr.TabItem("📸 Webcam"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| webcam_button = gr.Button("Capture Photo from Webcam") | |
| with gr.Column(): | |
| webcam_output = gr.Image(label="Result", type="pil") | |
| webcam_button.click(fn=capture_and_process, outputs=webcam_output) | |
| demo.launch() |