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import cv2
import numpy as np
def apply_gaussian_blur(mask, kernel_size=5):
"""Apply Gaussian blur to smooth the mask edges."""
return cv2.GaussianBlur(mask, (kernel_size, kernel_size), 0)
def apply_threshold(mask, threshold=127):
"""Apply binary threshold to sharpen the mask."""
_, binary_mask = cv2.threshold(mask, threshold, 255, cv2.THRESH_BINARY)
return binary_mask
def refine_edges(mask, kernel_size=3):
"""Refine edges using morphological operations."""
kernel = np.ones((kernel_size, kernel_size), np.uint8)
eroded = cv2.erode(mask, kernel, iterations=1)
dilated = cv2.dilate(mask, kernel, iterations=1)
refined = dilated - eroded
return cv2.bitwise_or(eroded, refined)
def apply_contour_smoothing(mask):
"""Smooth contours of the mask."""
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
smooth_mask = np.zeros_like(mask)
for contour in contours:
epsilon = 0.02 * cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, epsilon, True)
cv2.drawContours(smooth_mask, [approx], 0, 255, -1)
return smooth_mask
def refine_mask(mask, blur_kernel=5, edge_kernel=3, threshold_value=127):
"""Apply a series of refinement operations to the mask."""
mask = apply_gaussian_blur(mask, blur_kernel)
mask = apply_threshold(mask, threshold_value)
mask = refine_edges(mask, edge_kernel)
mask = apply_contour_smoothing(mask)
return mask
def apply_morphology(mask, kernel_size=3):
"""Apply morphological operations to clean up the mask."""
kernel = np.ones((kernel_size, kernel_size), np.uint8)
# Opening operation to remove small noise
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
# Closing operation to fill small holes
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
return mask
def remove_small_objects(mask, min_size=100):
"""Remove small objects from the mask based on area."""
# Ensure mask is binary and single channel
if len(mask.shape) > 2:
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
_, binary_mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)
num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(binary_mask, connectivity=8)
for i in range(1, num_labels): # Start from 1 to skip the background
if stats[i, cv2.CC_STAT_AREA] < min_size:
binary_mask[labels == i] = 0
return binary_mask
def clean_mask(mask, morph_kernel_size=3, min_object_size=100):
"""Apply both morphological operations and small object removal."""
mask = apply_morphology(mask, kernel_size=morph_kernel_size)
mask = remove_small_objects(mask, min_size=min_object_size)
return mask |