backend/modules/visual_checks.py

import cv2
import numpy as np
from PIL import Image
from backend import config
from backend.utils import get_roi
from backend.model_handler import model_handler

def detect_straight_lines(roi_img):
    """Enhanced edge detection focusing on straight lines."""
    gray = cv2.cvtColor(roi_img, cv2.COLOR_BGR2GRAY)
    clahe = cv2.createCLAHE(
        clipLimit=config.DETECTION_PARAMS['clahe_clip_limit'],
        tileGridSize=config.DETECTION_PARAMS['clahe_grid_size']
    )
    enhanced = clahe.apply(gray)
    blurred = cv2.GaussianBlur(
        enhanced,
        config.DETECTION_PARAMS['gaussian_kernel'],
        config.DETECTION_PARAMS['gaussian_sigma']
    )
    edges = cv2.Canny(
        blurred,
        config.DETECTION_PARAMS['canny_low'],
        config.DETECTION_PARAMS['canny_high']
    )
    line_mask = np.zeros_like(edges)
    lines = cv2.HoughLinesP(
        edges,
        rho=1,
        theta=np.pi/180,
        threshold=config.DETECTION_PARAMS['hough_threshold'],
        minLineLength=config.DETECTION_PARAMS['min_line_length'],
        maxLineGap=config.DETECTION_PARAMS['max_line_gap']
    )
    if lines is not None:
        for line in lines:
            x1, y1, x2, y2 = line[0]
            cv2.line(line_mask, (x1, y1), (x2, y2), 255, 2)
    return line_mask

def simple_edge_detection(roi_img):
    """Simple edge detection."""
    gray = cv2.cvtColor(roi_img, cv2.COLOR_BGR2GRAY)
    return cv2.Canny(gray, 50, 150)

def ribbon(image_path):
    """Detect the presence of a ribbon in an image."""
    image = cv2.imread(image_path)
    if image is None:
        raise ValueError(f"Could not read image: {image_path}")

    h, w = image.shape[:2]
    edge_present = []

    for i, roi in enumerate(config.ROIS):
        x1, y1, x2, y2 = [int(coord * (w if i % 2 == 0 else h)) for i, coord in enumerate(roi)]
        roi_img = image[y1:y2, x1:x2]

        if i < 6:  # Straight line detection for ROIs 0-5
            edges = detect_straight_lines(roi_img)
            edge_present.append(np.sum(edges) > edges.size * config.DETECTION_PARAMS['edge_pixel_threshold'])
        else:  # Original method for ROIs 6-8
            edges = simple_edge_detection(roi_img)
            edge_present.append(np.any(edges))

    result = all(edge_present[:6]) and not edge_present[6] and not edge_present[7] and not edge_present[8]
    return {"No Ribbon": 0 if result else 1}

def image_quality(image_path):
    """
    Check if an image is low resolution or poor quality.
    """
    try:
        image = Image.open(image_path)
        width, height = image.size
        pixel_count = width * height

        if width < config.MIN_WIDTH or height < config.MIN_HEIGHT or pixel_count < config.MIN_PIXEL_COUNT:
            return {"Bad Image Quality": 1}

        grayscale_image = image.convert("L")
        pixel_array = np.array(grayscale_image)
        variance = np.var(pixel_array)

        if variance < config.PIXEL_VARIANCE_THRESHOLD:
            return {"Bad Image Quality": 1}

        return {"Bad Image Quality": 0}

    except Exception as e:
        print(f"Error processing image: {e}")
        return {"Bad Image Quality": 1}

def gnc(image_path):
    """Check for gestures/coach marks and display the image."""
    image = get_roi(image_path, *config.GNC)
    gnc_text = model_handler.intern(image, config.PGNC, 900).lower()

    return {"Visual Gesture or Icon": 1 if 'yes' in gnc_text else 0}