OpenCV半小时掌握基本操作之直方图

calcHist(images, channels, mask, histSize, ranges, hist=None, accumulate=None)

import cv2
from matplotlib import pyplot as plt

plt.style.use("fivethirtyeight")

# 读取图片, 并转换成灰度图
img = cv2.imread("girl.jpg", 0)

# 获取直方图
hist = cv2.calcHist([img], [0], None, [256], [0, 256])
print(hist)

# 直方图展示
plt.figure(figsize=(12, 6))
plt.plot(hist)
plt.title("hist of image")
plt.show()

import cv2
from matplotlib import pyplot as plt

plt.style.use("fivethirtyeight")
plt.figure(figsize=(12, 6))

# 读取图片
img = cv2.imread("girl.jpg")

# 颜色通道
color = ["b", "g", "r"]

# 获取直方图
for i, c in enumerate(color):
    hist = cv2.calcHist([img], [i], None, [256], [0, 256])
    plt.plot(hist, color=c)

# 直方图展示
plt.legend(["B Channel", "G Channel", "R Channel"])
plt.title("RGB hist of image")

plt.show()

import numpy as np
import cv2
from matplotlib import pyplot as plt

plt.style.use("fivethirtyeight")

# 读取图片, 并转换成灰度图
img = cv2.imread("girl.jpg", 0)

# 创建mask
mask = np.zeros(img.shape, np.uint8)
mask[280:1000, 420:1500] = 255

# 获取mask后的图像
masked_img = cv2.bitwise_and(img, img, mask=mask)

# 直方图
hist_full = cv2.calcHist([img], [0], None, [256], [0, 256])
hist_mask = cv2.calcHist([img], [0], mask, [256], [0, 256])

# 图片展示
f, ax = plt.subplots(2, 2, figsize=(12, 9))
ax[0, 0].imshow(img, 'gray')
ax[0, 0].set_title("original image")
ax[0, 1].imshow(mask, 'gray')
ax[0, 1].set_title("mask")
ax[1, 0].imshow(masked_img, 'gray')
ax[1, 0].set_title("masked image")
ax[1, 1].plot(hist_full)
ax[1, 1].plot(hist_mask)
ax[1, 1].set_title("original vs masked hist")

plt.show()

cv2.equalizeHist(src, dst=None)

import cv2
from matplotlib import pyplot as plt

plt.style.use("fivethirtyeight")

# 读取图片, 并转换成灰度图
img = cv2.imread("girl.jpg", 0)

# 均衡化
img_equ = cv2.equalizeHist(img)

# 直方图
f, ax = plt.subplots(2, 2, figsize=(16, 16))
ax[0, 0].imshow(img, "gray")
ax[0, 0].set_title("before")
ax[0, 1].imshow(img_equ, "gray")
ax[0, 1].set_title("after")
ax[1, 0].hist(img.ravel(), 256)
ax[1, 1].hist(img_equ.ravel(), 256)

plt.show()