hand_detection

from skimage import io, color, morphology, measure
import numpy as np
import matplotlib.pyplot as plt

def detect_hand(image_path):
    # Load the image
    image = io.imread(image_path)

    # Check if the image is already in grayscale, if not convert it to RGB
    if image.ndim == 3 and image.shape[2] == 4:
        image = color.rgba2rgb(image)

    # Convert to HSV and detect skin tone range
    hsv_img = color.rgb2hsv(image)
    # These thresholds are for skin tone detection
    lower_skin = np.array([0.05, 0.2, 0.2])
    upper_skin = np.array([0.25, 0.7, 0.8])
    skin_mask = ((hsv_img[:,:,0] > lower_skin[0]) & (hsv_img[:,:,0] < upper_skin[0]) &
                 (hsv_img[:,:,1] > lower_skin[1]) & (hsv_img[:,:,1] < upper_skin[1]) &
                 (hsv_img[:,:,2] > lower_skin[2]) & (hsv_img[:,:,2] < upper_skin[2]))

    # Apply morphological operations to clean up the mask
    cleaned_mask = morphology.remove_small_objects(skin_mask, min_size=500)
    cleaned_mask = morphology.closing(cleaned_mask, morphology.disk(7))

    # Label connected regions
    labeled_mask = measure.label(cleaned_mask)
    regions = measure.regionprops(labeled_mask)

    # Initialize variables to find the bounding box of the combined regions
    min_row, min_col, max_row, max_col = (np.inf, np.inf, -np.inf, -np.inf)

    # Loop over each region to combine regions within the area range and find the overall bounding box
    for region in regions:
        if region.area <= 3000:
            min_row = min(min_row, region.bbox[0])
            min_col = min(min_col, region.bbox[1])
            max_row = max(max_row, region.bbox[2])
            max_col = max(max_col, region.bbox[3])

    # Display the original image with the combined region highlighted
    fig, ax = plt.subplots(figsize=(10, 6))
    ax.imshow(image)

    # Check if the bounding box coordinates have been updated
    if max_row > min_row and max_col > min_col:
        # Draw rectangle around the combined region
        hand_detected = True
        rect = plt.Rectangle((min_col, min_row), max_col - min_col, max_row - min_row, edgecolor='red', linewidth=2, fill=False)
        ax.add_patch(rect)
        ax.set_axis_off()
        plt.show()
    else:
        hand_detected = False
        print("No hand detected.")
      

    plt.close()
    
    return hand_detected


image_path = 'no.jpg'
has_hand = detect_hand(image_path)
if has_hand:
    print(f"Hand detected")
else:
    print("No hand was detected in the image.")