CODEWITHSUNDEEP
pythonpython-3.ximage-processing
Gaganpreet Singh
Gaganpreet Singh

Reputation: 73

Calculating the green colour percentage in an image

I want to calculate the green colour percentage in an image

I have calculated by iterating through each pixel in an image and check the color of each pixel. Finally, I keep the count of the number of green pixels and find the total percentage in whole image.

def green_color_optimized(screenpath):
    start_time = time.time()
    img = image.load_img(screenpath,target_size=(300,300,3))
    x = image.img_to_array(img)
    print("Image size: ", x.shape)
    count_green = 0
    for i in range(0,x.shape[0]):
      for j in range(0,x.shape[1]):
        pixel = list(map(int, x[i,j].tolist()))
        if sum(pixel) != 0:
          green_pixel = 100*(pixel[1]/sum(pixel))
          blue_pixel = 100*(pixel[2]/sum(pixel))
          red_pixel = 100*(pixel[0]/sum(pixel))
          if green_pixel > red_pixel and green_pixel > blue_pixel:
            if green_pixel > 35:
              count_green += 1
    green_percent = round(100*(count_green/(x.shape[0]*x.shape[1])),2)

With this code, each image takes around 200ms to process; and I want to process 1 million images. How can I optimise the code?

Upvotes: 1

Views: 694

Answers (1)

Nitesh Menon
Nitesh Menon

Reputation: 141

Presuming that x is a numpy array, you should always vectorize your matrix operations. The following runs ~200 times faster:

# Your original function, with the file i/o removed for timing comparison 
def green_original(x):
    count_green = 0
    for i in range(0,x.shape[0]):
      for j in range(0,x.shape[1]):
        pixel = list(map(int, x[i,j].tolist()))
        if sum(pixel) != 0:
          green_pixel = 100*(pixel[1]/sum(pixel))
          blue_pixel = 100*(pixel[2]/sum(pixel))
          red_pixel = 100*(pixel[0]/sum(pixel))
          if green_pixel > red_pixel and green_pixel > blue_pixel:
            if green_pixel > 35:
              count_green += 1
    green_percent = round(100*(count_green/(x.shape[0]*x.shape[1])),2)
    return green_percent


def green_vectorized(x):
    mask = (img[:,:,1] > img[:,:,0]) & (img[:,:,1] > img[:,:,2]) & ((img[:,:,1]/np.sum(img, axis=2)) > .35)
    round(100 * np.sum(mask)/(x.shape[0]*x.shape[1]), 2)

img = np.ones(shape=(300,300,3))
img[0:150,0:150, 1] = 134
%timeit green_original(img)
%timeit green_vectorized(img)

Your Version

81.7 ms ± 6.24 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Vectorized Version

461 µs ± 78.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Upvotes: 2

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