computer_graphics.1.py

import numpy as np
import matplotlib.pyplot as plt
IMG_URL = "/tmp/img_res.jpg"
CM = plt.cm.gist_gray

def rgb2gray(img):
    # массив весов для конвертации в grayscale, RGB
    # W = np.array([0.2126, 0.7152, 0.0722])
    W = np.array([0.299, 0.587, 0.114])
    # W = np.array([0.333, 0.333, 0.333])
    
    # matrix multiplication: img * W
    
    return np.dot(img, W).astype(np.uint8)

def calc_gray_hist(img):
    out = np.zeros(shape=(256, ), dtype=np.uint32)
    for vl in img.flatten():
        out[vl] += 1

    return out
    
def gamma_correction(img, c, y):
    # g = c * img^y
    return c * np.power(img, y)
    
def put_impulse_noise(img):
    lim = img.size/16
    noise_items_count = int(np.abs(np.random.normal(0, lim)))
    if noise_items_count > lim:
        noise_items_count = lim
    
    rnd_indeces = np.random.randint(0, img.size, noise_items_count)
    img_flat = img.copy().reshape(img.size)
    orig_shape = img.shape
    
    img_flat[rnd_indeces] = 255
    return img_flat.reshape(orig_shape)
    
def median_filter(img, size):
    img = img.copy()
    MEDIAN_FILTER_SIZE = size
    assert MEDIAN_FILTER_SIZE % 2 == 1
    # skip boundariess
    # @TODO: implement using boundaries
    skip_size = (MEDIAN_FILTER_SIZE - 1 )/2
    
    for x in xrange(skip_size, img.shape[0]-skip_size):
        for y in xrange(skip_size, img.shape[1]-skip_size):
            sub_matrix = img[x-skip_size:x+skip_size+1, y-skip_size:y+skip_size+1]
            items = np.sort(sub_matrix.flatten())
            img[x,y] = items[items.size/2]
            
    return img
    
def show_img_with_hist(img):
    fig = plt.figure()

    ax1 = fig.add_subplot(121)
    ax2 = fig.add_subplot(122)
    
    img[img > 255] = 255
    
    ax1.imshow(img, cmap=CM, vmin=0, vmax=256)
    ax2.plot(calc_gray_hist(img))
    plt.show()
    
img = plt.imread(IMG_URL)

img_gray = rgb2gray(img) # matrix multiplication
img_hist = calc_gray_hist(img_gray)
img_corr = gamma_correction(img_gray, c=1.0, y=1.0/1.05)

show_img_with_hist(img_gray)
show_img_with_hist(img_corr)

img_noise = put_impulse_noise(img_gray)
show_img_with_hist(img_noise)

show_img_with_hist(median_filter(img_noise, 11))