import cv2

import numpy as np

img = cv2.imread("G:\\MyLearning\\TensorFlow_deep_learn\\data\\lena.jpg")

img_hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

turn_green_hsv = img_hsv.copy()

turn_green_hsv[:,:,0] = (turn_green_hsv[:,:,0] - 30 ) % 180

turn_green_img = cv2.cvtColor(turn_green_hsv,cv2.COLOR_HSV2BGR)

cv2.imshow("test",turn_green_img)

cv2.waitKey(0)

import cv2

img = cv2.imread("G:\\MyLearning\\TensorFlow_deep_learn\\data\\lena.jpg")

img_hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

less_color_hsv = img_hsv.copy()

less_color_hsv[:, :, 1] = less_color_hsv[:, :, 1] * 0.6

turn_green_img = cv2.cvtColor(less_color_hsv, cv2.COLOR_HSV2BGR)

cv2.imshow("test",turn_green_img)

cv2.waitKey(0)

import cv2

img = cv2.imread("G:\\MyLearning\\TensorFlow_deep_learn\\data\\lena.jpg")

img_hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

less_color_hsv = img_hsv.copy()

less_color_hsv[:, :, 2] = less_color_hsv[:, :, 2] * 0.6

turn_green_img = cv2.cvtColor(less_color_hsv, cv2.COLOR_HSV2BGR)

cv2.imshow("test",turn_green_img)

cv2.waitKey(0)

import cv2

import numpy as np

import matplotlib.pyplot as plt

img = plt.imread("G:\\MyLearning\\TensorFlow_deep_learn\\data\\lena.jpg")

gamma_change = [np.power(x/255,0.4) * 255 for x in range(256)]

gamma_img =  np.round(np.array(gamma_change)).astype(np.uint8)

img_corrected = cv2.LUT(img, gamma_img)

plt.subplot(121)

plt.imshow(img)

plt.subplot(122)

plt.imshow(img_corrected)

plt.show()

import cv2

import numpy as np

img = cv2.imread("G:\\MyLearning\\TensorFlow_deep_learn\\data\\lena.jpg")

M_copy_img = np.array([[0, 0.8, -200],[0.8, 0, -100]], dtype=np.float32)

img_change = cv2.warpAffine(img, M_copy_img,(300,300))

cv2.imshow("test",img_change)

cv2.waitKey(0)

import cv2

import random

img = cv2.imread("G:\\MyLearning\\TensorFlow_deep_learn\\data\\lena.jpg")

width,height,depth = img.shape

img_width_box = width * 0.2

img_height_box = height * 0.2

for _ in range(9):

    start_pointX = random.uniform(0, img_width_box)

    start_pointY = random.uniform(0, img_height_box)

    copyImg = img[int(start_pointX):200, int(start_pointY):200]

    cv2.imshow("test", copyImg)

    cv2.waitKey(0)

import cv2

img = cv2.imread("G:\\MyLearning\\TensorFlow_deep_learn\\data\\lena.jpg")

rows,cols,depth = img.shape

img_change = cv2.getRotationMatrix2D((cols/2,rows/2),45,1)

res = cv2.warpAffine(img,img_change,(rows,cols))

cv2.imshow("test",res)

cv2.waitKey(0)

import cv2

import  numpy as np

img = cv2.imread("G:\\MyLearning\\TensorFlow_deep_learn\\data\\lena.jpg")

img_hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

turn_green_hsv = img_hsv.copy()

turn_green_hsv[:,:,0] = (turn_green_hsv[:,:,0] + np.random.random() ) % 180

turn_green_hsv[:,:,1] = (turn_green_hsv[:,:,1] + np.random.random() ) % 180

turn_green_hsv[:,:,2] = (turn_green_hsv[:,:,2] + np.random.random() ) % 180

turn_green_img = cv2.cvtColor(turn_green_hsv,cv2.COLOR_HSV2BGR)

cv2.imshow("test",turn_green_img)

cv2.waitKey(0)

import cv2

def on_mouse(event, x, y, flags, param):

    rect_start = (0,0)

    rect_end = (0,0)

    if event == cv2.EVENT_LBUTTONDOWN:

        rect_start = (x,y)

    if event == cv2.EVENT_LBUTTONUP:

        rect_end = (x, y)

    cv2.rectangle(img, rect_start, rect_end,(0,255,0), 2)

img = cv2.imread("G:\\MyLearning\\TensorFlow_deep_learn\\data\\lena.jpg")

cv2.namedWindow('test')

cv2.setMouseCallback("test",on_mouse)

while(1):

    cv2.imshow("test",img)

    if cv2.waitKey(1) & 0xFF == ord('q'):

        break

cv2.destroyAllWindows()