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import gradio as gr
from PIL import Image
import os
import cv2
import numpy as np
import mediapipe as mp
# Load hairstyles from 'hairstyles' folder
def load_hairstyles():
folder = "hairstyles"
if not os.path.exists(folder):
return []
return [
Image.open(os.path.join(folder, f)).convert("RGBA")
for f in sorted(os.listdir(folder)) if f.endswith(".png")
]
hairstyles = load_hairstyles()
mp_face_detection = mp.solutions.face_detection
# Apply hairstyle using face detection to auto-align on head
def apply_hairstyle(user_img, style_index):
if user_img is None or not hairstyles:
return None
# Convert to CV2 image for detection
img_cv2 = np.array(user_img.convert("RGB"))
img_cv2 = cv2.cvtColor(img_cv2, cv2.COLOR_RGB2BGR)
h_img, w_img = img_cv2.shape[:2]
with mp_face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.5) as face_detection:
results = face_detection.process(cv2.cvtColor(img_cv2, cv2.COLOR_BGR2RGB))
if not results.detections:
return user_img # No face detected, return original
# Use first face
detection = results.detections[0]
bbox = detection.location_data.relative_bounding_box
x = int(bbox.xmin * w_img)
y = int(bbox.ymin * h_img)
w = int(bbox.width * w_img)
h = int(bbox.height * h_img)
# Estimate head top
top_y = max(y - int(h * 0.6), 0)
# Load and resize hairstyle
hairstyle = hairstyles[style_index]
new_width = int(w * 1.1) # Slightly wider than face
new_height = int(hairstyle.height * (new_width / hairstyle.width))
resized_hair = hairstyle.resize((new_width, new_height))
# Create output image
user_img = user_img.convert("RGBA")
composite = Image.new("RGBA", user_img.size)
paste_x = x - int((new_width - w) / 2)
paste_y = top_y
composite.paste(resized_hair, (paste_x, paste_y), resized_hair)
final = Image.alpha_composite(user_img, composite)
return final.convert("RGB")
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("## 💇 Salon Virtual Hairstyle Try-On (Face-Aligned, No Adjustment Needed)")
with gr.Row():
with gr.Column():
image_input = gr.Image(type="pil", label="📷 Upload Your Image")
style_slider = gr.Slider(0, max(len(hairstyles)-1, 0), step=1, label="🎨 Select Hairstyle")
apply_btn = gr.Button("✨ Apply Hairstyle")
with gr.Column():
result_output = gr.Image(label="🔍 Result Preview")
apply_btn.click(
fn=apply_hairstyle,
inputs=[image_input, style_slider],
outputs=result_output
)
demo.launch()
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