enh_col=ImageEnhance.Color(img)
color = 2.0
new_img = enh_col.enhance(color)

new_img = cv2.cvtColor(np.asarray(new_img), cv2.COLOR_RGB2BGR)

# 油畫(huà)效果
def oil_effect(img):
    h, w, n = img.shape
    new_img = np.zeros((h - 2, w, n), dtype=np.uint8)
    for i in range(h - 2):
        for j in range(w):
            if random.randint(1, 10) % 3 == 0:
                new_img[i, j] = img[i - 1, j]
            elif random.randint(1, 10) % 2 == 0:
                new_img[i, j] = img[i + 1, j]
            else:
                new_img[i, j] = img[i + 2, j]
    return new_img


# 圖像增強(qiáng)
def img_add():
    img = Image.open("oil.jpg")
    enh_col = ImageEnhance.Color(img)
    color = 2.0
    new_img = enh_col.enhance(color)
    new_img = cv2.cvtColor(np.asarray(new_img), cv2.COLOR_RGB2BGR)
    return new_img


if __name__ == "__main__":
    img = cv2.imread("49.jpg")
    oil_img = oil_effect(img)
    cv2.imwrite("oil.jpg", oil_img)
    cv2.imshow("0", img)
    cv2.imshow("1", img_add())
    cv2.waitKey()
    cv2.destroyAllWindows()

import cv2

img = cv2.imread('img.jpg')

res = cv2.stylization(img, sigma_s=60, sigma_r=0.6)

# sigma_s controls the size of the neighborhood. Range 1 - 200

# sigma_r controls the how dissimilar colors within the neighborhood will be averaged. A larger sigma_r results in large regions of constant color. Range 0 - 1