HSV模型原理以及利用HSV模型随机增强图像

• 图像HSV模型简介
• RGB模型转HSV模型
• opencv关于HSV模型实验
• 随机增强图像HSV

图像HSV模型简介

RGB模型转HSV模型

opencv关于HSV模型实验

import cv2
import numpy as np

rgb = np.array([110, 20, 50], dtype=np.uint8).reshape((1, 1, 3))
hsv = cv2.cvtColor(rgb, cv2.COLOR_RGB2HSV)
print(hsv)

import cv2
import numpy as np


hue = np.zeros((100, 256, 1), dtype=np.uint8)
sat = np.tile(np.arange(0, 256, dtype=np.uint8).reshape((1, 256, 1)),
             (100, 1, 1))
val = np.ones((100, 256, 1), dtype=np.uint8) * 255
img_hsv = cv2.merge((hue, sat, val))
img = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)
# img = cv2.resize(img, (720, 100))
cv2.imshow("img", img)
cv2.waitKey(0)

import cv2
import numpy as np


hue = np.zeros((100, 256, 1), dtype=np.uint8)
sat = np.ones((100, 256, 1), dtype=np.uint8) * 255
val = np.tile(np.arange(0, 256, dtype=np.uint8).reshape((1, 256, 1)),
             (100, 1, 1))
img_hsv = cv2.merge((hue, sat, val))
img = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)
# img = cv2.resize(img, (720, 100))
cv2.imshow("img", img)
cv2.waitKey(0)

随机增强图像HSV

import cv2
import numpy as np


def augment_hsv(img, h_gain=0.5, s_gain=0.5, v_gain=0.5):
    r = np.random.uniform(-1, 1, 3) * [h_gain, s_gain, v_gain] + 1  # random gains
    hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
    dtype = img.dtype  # uint8

    x = np.arange(0, 256, dtype=np.int16)
    lut_hue = ((x * r[0]) % 180).astype(dtype)
    lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
    lut_val = np.clip(x * r[2], 0, 255).astype(dtype)

    img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype)
    aug_img = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)
    return aug_img