last_opencv.py

from cv2 import *
import sys

bottle_image_name = sys.argv[1]
bottle_image_gray = imread(bottle_image_name,CV_LOAD_IMAGE_GRAYSCALE)
(tresh, im_bw) = threshold(bottle_image_gray, 127, 255, THRESH_BINARY)
imwrite('bw.png',im_bw)
bottle_image_bw = imread('bw.png',CV_LOAD_IMAGE_UNCHANGED)

contours, hierarchy = findContours(bottle_image_bw, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE)
aspect_ratios = []
centroids = []
for contour in contours:

    num_points = len(contour)
    if num_points < 5:
        continue

    # We could use area to help determine the type of object.
    # Small contours are probably false detections (not really a whole object).
    area = contourArea(contour)

    bounding_ellipse = fitEllipse(contour)
    center, radii, angle_degrees = bounding_ellipse

    #print center, radii, angle_degrees

    # Let's define an ellipse's normal orientation to be landscape (width > height).
    # We must ensure that the ellipse's measurements match this orientation.
    if radii[0] < radii[1]:
        radii = (radii[1], radii[0])
        angle_degrees -= 90.0

    # # We could use the angle to help determine the type of object.
    # # A bottle or can's angle is probably approximately a multiple of 90 degrees,
    # # assuming that it is at rest and not falling.

    # # Calculate the aspect ratio (width / height).
    # # For example, 0.5 means the object's height is 2 times its width.
    # # A bottle is probably taller than a can.
    aspect_ratio = radii[0] / radii[1]
    aspect_ratios.append(aspect_ratio)

    m = moments(contour)
    m00 = m['m00']
    m01 = m['m01']
    m10 = m['m10']
    try:
        centroid = (m10 / m00, m01 / m00)
        print centroid
    except:
        print("Tried to divide by zero")
print sum(aspect_ratios) / float(len(aspect_ratios))
imshow("Pic",bottle_image_bw)
waitKey(0)