app.py

import gradio as gr
import mediapipe as mp
import numpy as np
from PIL import Image
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
from scipy.ndimage import binary_dilation, label

BG_COLOR = np.array([0, 0, 0], dtype=np.uint8) # black
MASK_COLOR = np.array([255, 255, 255], dtype=np.uint8) # white

MODEL_PATH = "checkpoints/selfie_multiclass_256x256.tflite"
category_options = ["hair", "clothes", "background"]
base_options = python.BaseOptions(model_asset_path=MODEL_PATH)
options = vision.ImageSegmenterOptions(base_options=base_options,output_category_mask=True)
segmenter = vision.ImageSegmenter.create_from_options(options)
labels = segmenter.labels

def segment(input_image, category):
    image = mp.Image(image_format=mp.ImageFormat.SRGB, data=np.asarray(input_image))
    segmentation_result = segmenter.segment(image)
    category_mask = segmentation_result.category_mask
    category_mask_np = category_mask.numpy_view()

    if category == "hair":
        target_mask = get_hair_mask(category_mask_np, should_dilate=True)
    elif category == "clothes":
        target_mask = get_clothes_mask(category_mask_np)
    else:
        target_mask = category_mask_np == 0

    # Generate solid color images for showing the output segmentation mask.
    image_data = image.numpy_view()
    fg_image = np.zeros(image_data.shape, dtype=np.uint8)
    fg_image[:] = MASK_COLOR
    bg_image = np.zeros(image_data.shape, dtype=np.uint8)
    bg_image[:] = BG_COLOR

    condition = np.stack((target_mask,) * 3, axis=-1) > 0.2

    output_image = np.where(condition, fg_image, bg_image)
    output_image = Image.fromarray(output_image)
    return output_image

def get_clothes_mask(category_mask_np):
    body_skin_mask = category_mask_np == 2
    clothes_mask = category_mask_np == 4
    combined_mask = np.logical_or(body_skin_mask, clothes_mask)
    combined_mask = binary_dilation(combined_mask, iterations=4)
    return combined_mask

def get_hair_mask(category_mask_np, should_dilate=False):
    hair_mask = category_mask_np == 1
    hair_mask = binary_dilation(hair_mask, iterations=4)
    if not should_dilate:
        return hair_mask
    body_skin_mask = category_mask_np == 2
    face_skin_mask = category_mask_np == 3
    clothes_mask = category_mask_np == 4

    face_indices = np.where(face_skin_mask)
    min_face_y = np.min(face_indices[0])

    labeled_hair, hair_features = label(hair_mask)
    top_hair_mask = np.zeros_like(hair_mask)
    for i in range(1, hair_features + 1):
        component_mask = labeled_hair == i
        component_indices = np.where(component_mask)
        min_component_y = np.min(component_indices[0])
        if min_component_y <= min_face_y:
            top_hair_mask[component_mask] = True
    
    expanded_face_mask = binary_dilation(face_skin_mask, iterations=40)
    # Combine the reference masks (body, clothes)
    reference_mask = np.logical_or(body_skin_mask, clothes_mask)
    # Exclude the expanded face mask from the reference mask
    reference_mask = np.logical_and(reference_mask, ~expanded_face_mask)

    # Expand the hair mask downward until it reaches the reference areas
    expanded_hair_mask = top_hair_mask
    while not np.any(np.logical_and(expanded_hair_mask, reference_mask)):
        expanded_hair_mask = binary_dilation(expanded_hair_mask, iterations=10)
    
    # Trim the expanded_hair_mask
    # 1. Remove the area above hair_mask by 20 pixels
    hair_indices = np.where(hair_mask)
    min_hair_y = np.min(hair_indices[0]) - 20
    expanded_hair_mask[:min_hair_y, :] = 0

    # 2. Remove the areas on both sides that exceed the clothing coordinates
    clothes_indices = np.where(clothes_mask)
    min_clothes_x = np.min(clothes_indices[1])
    max_clothes_x = np.max(clothes_indices[1])
    expanded_hair_mask[:, :min_clothes_x] = 0
    expanded_hair_mask[:, max_clothes_x+1:] = 0
    
    # exclude the face-skin, body-skin and clothes areas
    expanded_hair_mask = np.logical_and(expanded_hair_mask, ~face_skin_mask)
    expanded_hair_mask = np.logical_and(expanded_hair_mask, ~body_skin_mask)
    expanded_hair_mask = np.logical_and(expanded_hair_mask, ~clothes_mask)
    # combine the hair mask with the expanded hair mask
    expanded_hair_mask = np.logical_or(hair_mask, expanded_hair_mask)

    return expanded_hair_mask

with gr.Blocks() as app:
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(type='pil', label='Upload image')
            category = gr.Dropdown(label='Category', choices=category_options, value=category_options[0])
            submit_btn = gr.Button(value='Submit', variant='primary')
        with gr.Column():
            output_image = gr.Image(type='pil', label='Image Output')

    submit_btn.click(
        fn=segment,
        inputs=[
            input_image,
            category,
        ],
        outputs=[output_image]
    )

app.launch(debug=False, show_error=True)