app_old.py

import base64
from io import BytesIO
from typing import Tuple, Optional

import cv2
import gradio as gr
import numpy as np
from PIL import Image

try:
    import mediapipe as mp  # type: ignore
    HAS_MEDIAPIPE = True
except Exception:  # pragma: no cover - optional dependency
    HAS_MEDIAPIPE = False


def _ensure_rgb_uint8(image: np.ndarray) -> np.ndarray:
    """Convert an input image array to RGB uint8 format.

    Gradio provides images as numpy arrays in RGB order with dtype uint8 by default,
    but we defensively normalize here in case inputs vary.
    """
    if image is None:
        raise ValueError("No image provided")

    if isinstance(image, Image.Image):
        image = np.array(image.convert("RGB"))
    elif image.dtype != np.uint8:
        image = image.astype(np.uint8)

    if image.ndim == 2:
        image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
    elif image.shape[2] == 4:
        image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
    return image


def _central_crop_bbox(width: int, height: int, frac: float = 0.6) -> Tuple[int, int, int, int]:
    """Return a central crop bounding box (x1, y1, x2, y2) covering `frac` of width/height."""
    frac = float(np.clip(frac, 0.2, 1.0))
    crop_w = int(width * frac)
    crop_h = int(height * frac)
    x1 = (width - crop_w) // 2
    y1 = (height - crop_h) // 2
    x2 = x1 + crop_w
    y2 = y1 + crop_h
    return x1, y1, x2, y2


def _detect_face_bbox_mediapipe(image_rgb: np.ndarray) -> Optional[Tuple[int, int, int, int]]:
    """Detect a face bounding box using MediaPipe Face Detection and return (x1, y1, x2, y2).

    Returns None if detection fails or mediapipe is unavailable.
    """
    if not HAS_MEDIAPIPE:
        return None
    height, width = image_rgb.shape[:2]
    try:
        with mp.solutions.face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.5) as detector:
            results = detector.process(image_rgb)
            detections = results.detections or []
            if not detections:
                return None
            # Pick the largest bbox
            def bbox_area(det):
                bbox = det.location_data.relative_bounding_box
                return max(0.0, bbox.width) * max(0.0, bbox.height)

            best = max(detections, key=bbox_area)
            rb = best.location_data.relative_bounding_box
            x1 = int(np.clip(rb.xmin * width, 0, width - 1))
            y1 = int(np.clip(rb.ymin * height, 0, height - 1))
            x2 = int(np.clip((rb.xmin + rb.width) * width, 0, width))
            y2 = int(np.clip((rb.ymin + rb.height) * height, 0, height))

            # Expand a bit to include cheeks/forehead
            pad_x = int(0.08 * width)
            pad_y = int(0.12 * height)
            x1 = int(np.clip(x1 - pad_x, 0, width - 1))
            y1 = int(np.clip(y1 - pad_y, 0, height - 1))
            x2 = int(np.clip(x2 + pad_x, 0, width))
            y2 = int(np.clip(y2 + pad_y, 0, height))

            if x2 - x1 < 10 or y2 - y1 < 10:
                return None
            return x1, y1, x2, y2
    except Exception:
        return None


def _binary_open_close(mask: np.ndarray, kernel_size: int = 5, iterations: int = 1) -> np.ndarray:
    """Apply morphological open then close to clean the binary mask."""
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))
    opened = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=iterations)
    closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, kernel, iterations=iterations)
    return closed


def _skin_mask_ycrcb(image_rgb: np.ndarray) -> np.ndarray:
    """Skin detection using YCrCb thresholding.

    Returns a binary mask (uint8 0/255) where 255 denotes skin-like pixels.
    Thresholds are chosen to be reasonably inclusive for diverse skin tones.
    """
    image_ycrcb = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2YCrCb)
    Y, Cr, Cb = cv2.split(image_ycrcb)

    # Typical skin ranges in YCrCb space
    cr_min, cr_max = 133, 180
    cb_min, cb_max = 77, 140

    mask_cr = cv2.inRange(Cr, cr_min, cr_max)
    mask_cb = cv2.inRange(Cb, cb_min, cb_max)
    mask = cv2.bitwise_and(mask_cr, mask_cb)

    mask = _binary_open_close(mask, kernel_size=5, iterations=1)
    mask = cv2.GaussianBlur(mask, (5, 5), 0)
    _, mask = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    return mask


def _skin_mask_hsv(image_rgb: np.ndarray) -> np.ndarray:
    """Auxiliary HSV-based skin detection mask."""
    image_hsv = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2HSV)
    H, S, V = cv2.split(image_hsv)

    # Skin hues tend to be in the lower range; saturation moderate; value reasonably bright
    h_min, h_max = 0, 50
    s_min, s_max = int(0.20 * 255), int(0.80 * 255)
    v_min = int(0.20 * 255)

    mask_h = cv2.inRange(H, h_min, h_max)
    mask_s = cv2.inRange(S, s_min, s_max)
    mask_v = cv2.inRange(V, v_min, 255)
    mask = cv2.bitwise_and(cv2.bitwise_and(mask_h, mask_s), mask_v)
    mask = _binary_open_close(mask, kernel_size=5, iterations=1)
    return mask


def _combine_masks(mask1: np.ndarray, mask2: np.ndarray) -> np.ndarray:
    if mask1 is None:
        return mask2
    if mask2 is None:
        return mask1
    combined = cv2.bitwise_and(mask1, mask2)
    return combined


def _compute_skin_color_hex(image_rgb: np.ndarray, mask: np.ndarray) -> Tuple[str, np.ndarray]:
    """Compute a robust representative skin color as a hex string and return also the RGB color.

    Uses median across masked pixels to reduce influence of highlights/shadows.
    """
    if mask is None or mask.size == 0:
        raise ValueError("Invalid mask for skin color computation")

    # boolean mask for indexing
    mask_bool = mask.astype(bool)
    if not np.any(mask_bool):
        raise ValueError("No skin pixels detected")

    skin_pixels = image_rgb[mask_bool]

    # Robust median to mitigate outliers
    median_color = np.median(skin_pixels, axis=0)
    median_color = np.clip(median_color, 0, 255).astype(np.uint8)

    r, g, b = int(median_color[0]), int(median_color[1]), int(median_color[2])
    hex_code = f"#{r:02X}{g:02X}{b:02X}"
    return hex_code, median_color


def _solid_color_image(color_rgb: np.ndarray, size: Tuple[int, int] = (160, 160)) -> np.ndarray:
    swatch = np.zeros((size[1], size[0], 3), dtype=np.uint8)
    swatch[:, :] = color_rgb
    return swatch


def detect_skin_tone(image: np.ndarray, center_focus: bool = True, use_face_detector: bool = False) -> Tuple[str, np.ndarray, np.ndarray]:
    """Main pipeline: returns (hex_code, color_swatch_image, debug_mask_overlay).

    - image: input image as numpy array (H, W, 3) RGB uint8
    - center_focus: if True, prioritizes central crop region to avoid background/hands
    """
    rgb = _ensure_rgb_uint8(image)
    height, width = rgb.shape[:2]

    # Optionally restrict to detected face, else center crop, else full image
    face_bbox: Optional[Tuple[int, int, int, int]] = None
    if use_face_detector:
        face_bbox = _detect_face_bbox_mediapipe(rgb)

    if face_bbox is not None:
        x1, y1, x2, y2 = face_bbox
        central_rgb = rgb[y1:y2, x1:x2]
    elif center_focus:
        x1, y1, x2, y2 = _central_crop_bbox(width, height, frac=0.7)
        central_rgb = rgb[y1:y2, x1:x2]
    else:
        x1, y1, x2, y2 = 0, 0, width, height
        central_rgb = rgb

    mask_ycrcb = _skin_mask_ycrcb(central_rgb)
    mask_hsv = _skin_mask_hsv(central_rgb)
    combined_mask = _combine_masks(mask_ycrcb, mask_hsv)

    # If too few pixels, relax to YCrCb only
    if np.count_nonzero(combined_mask) < 100:
        combined_mask = mask_ycrcb

    # If still too few, fallback to a small central patch without masking
    if np.count_nonzero(combined_mask) < 100:
        patch_frac = 0.2
        px1, py1, px2, py2 = _central_crop_bbox(central_rgb.shape[1], central_rgb.shape[0], frac=patch_frac)
        patch = central_rgb[py1:py2, px1:px2]
        median_color = np.median(patch.reshape(-1, 3), axis=0).astype(np.uint8)
        r, g, b = int(median_color[0]), int(median_color[1]), int(median_color[2])
        hex_code = f"#{r:02X}{g:02X}{b:02X}"

        # Build outputs
        swatch = _solid_color_image(median_color)
        # Debug overlay: show the central patch
        debug_overlay = rgb.copy()
        cv2.rectangle(debug_overlay, (x1 + px1, y1 + py1), (x1 + px2, y1 + py2), (255, 0, 0), 2)
        return hex_code, swatch, debug_overlay

    # Compute color from masked central region
    hex_code, color_rgb = _compute_skin_color_hex(central_rgb, combined_mask)

    # Prepare swatch and debug visualization
    swatch = _solid_color_image(color_rgb)

    # Place mask back into full image coordinates for visualization
    full_mask = np.zeros((height, width), dtype=np.uint8)
    full_mask[y1:y2, x1:x2] = combined_mask
    color_mask = cv2.cvtColor(full_mask, cv2.COLOR_GRAY2RGB)
    overlay = cv2.addWeighted(rgb, 0.8, color_mask, 0.2, 0)

    return hex_code, swatch, overlay


def _hex_html(hex_code: str) -> str:
    style = (
        "display:flex;align-items:center;gap:12px;padding:8px 0;"
    )
    swatch_style = (
        f"width:20px;height:20px;border-radius:4px;background:{hex_code};"
        "border:1px solid #ccc;"
    )
    return (
        f"<div style='{style}'>"
        f"<div style='{swatch_style}'></div>"
        f"<span style='font-family:monospace;font-size:16px'>{hex_code}</span>"
        "</div>"
    )


with gr.Blocks(title="Skin Tone Detector") as demo:
    gr.Markdown(
        """
        ### Skin Tone Hex Detector
        Upload a face image. The app estimates a representative skin tone and returns a HEX color.
        """
    )

    with gr.Row():
        with gr.Column():
            input_image = gr.Image(
                label="Upload face image",
                type="numpy",
                image_mode="RGB",
                height=360,
            )
            center_focus = gr.Checkbox(value=True, label="Center focus (ignore edges)")
            use_face_det = gr.Checkbox(value=True if HAS_MEDIAPIPE else False, label="Use face detection (MediaPipe)")
            run_btn = gr.Button("Detect Skin Tone", variant="primary")

        with gr.Column():
            hex_output = gr.HTML(label="HEX Color")
            swatch_output = gr.Image(label="Color Swatch", type="numpy")
            debug_output = gr.Image(label="Mask Overlay", type="numpy")
            if not HAS_MEDIAPIPE:
                gr.Markdown("MediaPipe not installed or unavailable. Face detection toggle will be ignored.")

    def _run(image: Optional[np.ndarray], center_focus: bool, use_face_det_flag: bool):
        if image is None:
            return _hex_html("#000000"), np.zeros((160, 160, 3), dtype=np.uint8), None
        hex_code, swatch, debug = detect_skin_tone(
            image,
            center_focus=center_focus,
            use_face_detector=(use_face_det_flag and HAS_MEDIAPIPE),
        )
        return _hex_html(hex_code), swatch, debug

    run_btn.click(_run, inputs=[input_image, center_focus, use_face_det], outputs=[hex_output, swatch_output, debug_output])
    input_image.change(_run, inputs=[input_image, center_focus, use_face_det], outputs=[hex_output, swatch_output, debug_output])


if __name__ == "__main__":
    demo.launch()