File size: 2,187 Bytes
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"""
Created on Tue Dec 10 09:15:10 2024
This code calculates the edge density of an image in blocks. It uses Canny edge detection to identify edges and calculates
the percentage of edge pixels in each block. The results can be used to analyze features such as wrinkles on fruit surfaces.
@author: jishu
"""
import cv2
import numpy as np
def calculate_edge_density(image, block_size=(50, 50)):
"""
Calculates edge density in blocks for a given image.
Parameters:
image_path (str): Path to the input image.
block_size (tuple): Dimensions of the blocks (height, width).
Returns:
list: Edge densities for each block (in percentage).
"""
if image is None:
print("Error: Image not found!")
return None
# Step 2: Resize the image for consistent processing
resized_image = cv2.resize(image, (512, 512))
# Step 3: Apply Gaussian Blur to reduce noise
blurred = cv2.GaussianBlur(resized_image, (5, 5), 0)
# Step 4: Perform Canny edge detection
edges = cv2.Canny(blurred, threshold1=50, threshold2=100)
# Step 5: Display the edge-detected image using OpenCV
# cv2.imshow("Edge Detection", edges)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# Step 6: Calculate edge density for each block
densities = []
height, width = edges.shape
block_height, block_width = block_size
# Loop through the image in blocks
for y in range(0, height, block_height):
for x in range(0, width, block_width):
# Define the block region
block = edges[y:y+block_height, x:x+block_width]
# Handle the case where the block goes out of image bounds
block = block[:block_height, :block_width]
# Calculate edge density
edge_pixels = np.sum(block == 255) # White pixels in the Canny output
total_pixels = block.size
density = (edge_pixels / total_pixels) * 100 # Convert to percentage
densities.append(density)
return np.mean(np.array(densities))
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