mustis_version.py

# Import the Libraries
import numpy as np
import cv2
import random
import sys

# load the image
#import os
#desktop = os.path.join(os.path.join(os.path.expanduser('~')), 'Desktop')
#path = desktop+'/test2.jpg'

path = sys.argv[1]

# Reading Image
img = cv2.imread(path, 1)

# See how it looks
cv2.imshow("Original", img)

# Converting the image to Gray Scale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

# Removing Gaussian Noise
blur = cv2.GaussianBlur(gray, (3, 3), 0)

# Applying inverse binary due to white background and adapting thresholding for better results
thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 205, 1)

# Checking to see how it looks
cv2.imshow("Binary", thresh)

# Finding contours with simple retrieval (no hierarchy) and simple/compressed end points
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

# Checking to see how many contours were found
print(len(contours))

# An empty list to store filtered contours
filtered = []

# Looping over all found contours
for c in contours:
    # If it has significant area, add to list
    if cv2.contourArea(c) < 1000: continue
    filtered.append(c)

# Checking the number of filtered contours
print(len(filtered))

# Initialize an equally shaped image
objects = np.zeros([img.shape[0], img.shape[1], 3], 'uint8')

# Looping over filtered contours
for c in filtered:
    # Select a random color to draw the contour
    col = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
    # Draw the contour on the image with above color
    cv2.drawContours(objects, [c], -1, col, -1)
    # Fetch contour area
    area = cv2.contourArea(c)
    # Fetch the perimeter
    p = cv2.arcLength(c, True)
    print(area, p)

# Finally show the processed image
cv2.imshow("Contours", objects)

# Closing protocol
cv2.waitKey(0)
cv2.destroyAllWindows()