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import numpy as np
import cv2
import matplotlib.pyplot as plt
from PIL import Image
import io
def otsu_threshold(image):
hist, bin_edges = np.histogram(image.flatten(), bins=256, range=[0, 256])
hist = hist.astype(float)
total_pixels = image.size
pixel_probability = hist / total_pixels
max_variance = 0
optimal_threshold = 0
for threshold in range(1, 256):
weight_background = np.sum(pixel_probability[:threshold])
weight_foreground = np.sum(pixel_probability[threshold:])
if weight_background == 0 or weight_foreground == 0:
continue
mean_background = np.sum(np.arange(threshold) * pixel_probability[:threshold]) / weight_background
mean_foreground = np.sum(np.arange(threshold, 256) * pixel_probability[threshold:]) / weight_foreground
variance = weight_background * weight_foreground * (mean_background - mean_foreground) ** 2
if variance > max_variance:
max_variance = variance
optimal_threshold = threshold
segmented_image = np.zeros_like(image)
segmented_image[image >= optimal_threshold] = 255
return optimal_threshold, segmented_image
def generate_segmented_image(image_path):
# Convert PIL to OpenCV format
print(f"Image path: {image_path}")
image = Image.open(image_path)
image_np = np.array(image)
original_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
if len(original_image.shape) == 3:
gray_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
else:
gray_image = original_image.copy()
blurred = cv2.GaussianBlur(gray_image, (5, 5), 0)
# Our implementation
our_threshold, our_segmented = otsu_threshold(blurred)
# OpenCV's implementation
opencv_threshold, opencv_segmented = cv2.threshold(
blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU
)
# Create histogram figure
fig, ax = plt.subplots(figsize=(6, 4))
ax.hist(gray_image.ravel(), 256, [0, 256], color='gray')
ax.axvline(x=our_threshold, color='red', linestyle='--', label=f'Ours: {our_threshold}')
ax.axvline(x=opencv_threshold, color='green', linestyle='--', label=f'OpenCV: {opencv_threshold}')
ax.set_title("Histogram with Thresholds")
ax.legend()
# Convert Matplotlib figure to image
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
hist_image = Image.open(buf)
plt.close(fig) # Close the figure to free memory
return (
image,
Image.fromarray(our_segmented),
Image.fromarray(opencv_segmented),
hist_image,
f"Our Threshold: {our_threshold}\nOpenCV Threshold: {opencv_threshold}",
)
if __name__ == "__main__":
#example usage
# Ensure you have the image path set correctly
image_path = '/home/akshat/projects/CSL7360_Project/bird.jpeg'
image = cv2.imread('/home/akshat/projects/CSL7360_Project/bird.jpeg')
# Call the function
generate_segmented_image(image)
# # Optionally, save results to files
# cv2.imwrite("our_segmented.png", our_segmented)
# cv2.imwrite("opencv_segmented.png", opencv_segmented)
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