python 并行计算 opencv_opencv-python计算影像

图片去燥

主要函数

cv.fastNlMeansDenoising()对灰度图像去燥

cv.fastNlMeansDenoisingColored() 对彩色图像去燥

cv.fastNlMeansDenoisingMulti()对灰度图片序列去燥

cv.fastNlMeansDenoisingColoredMulti()对彩色图片序列去燥

例子cv.fastNlMeansDenoisingColored()

import numpy as np

import cv2 as cv

from matplotlib import pyplot as plt

img = cv.imread('die.png')

dst = cv.fastNlMeansDenoisingColored(img,None,10,10,7,21)

plt.subplot(121),plt.imshow(img)

plt.subplot(122),plt.imshow(dst)

plt.show()

例子 cv.fastNlMeansDenoisingMulti()

import numpy as np

import cv2 as cv

from matplotlib import pyplot as plt

cap = cv.VideoCapture('vtest.avi')

# create a list of first 5 frames

img = [cap.read()[1] for i in xrange(5)]

# convert all to grayscale

gray = [cv.cvtColor(i, cv.COLOR_BGR2GRAY) for i in img]

# convert all to float64

gray = [np.float64(i) for i in gray]

# create a noise of variance 25

noise = np.random.randn(*gray[1].shape)*10

# Add this noise to images

noisy = [i+noise for i in gray]

# Convert back to uint8

noisy = [np.uint8(np.clip(i,0,255)) for i in noisy]

# Denoise 3rd frame considering all the 5 frames

dst = cv.fastNlMeansDenoisingMulti(noisy, 2, 5, None, 4, 7, 35)

plt.subplot(131),plt.imshow(gray[2],'gray')

plt.subplot(132),plt.imshow(noisy[2],'gray')

plt.subplot(133),plt.imshow(dst,'gray')

plt.show()

https://docs.opencv.org/3.4/d5/d69/tutorial_py_non_local_means.html

图片修补

首先需要创建一个保护罩

import numpy as np

import cv2 as cv

img = cv.imread('messi_2.jpg')

mask = cv.imread('mask2.png',0)

dst = cv.inpaint(img,mask,3,cv.INPAINT_TELEA)

cv.imshow('dst',dst)

cv.waitKey(0)

cv.destroyAllWindows()

https://docs.opencv.org/3.4/df/d3d/tutorial_py_inpainting.html

图片高动态范围成像

Goal

Learn how to generate and display HDR image from an exposure sequence.

Use exposure fusion to merge an exposure sequence.

1加载不同曝光的图片到列表中

import cv2 as cv

import numpy as np

# Loading exposure images into a list

img_fn = ["img0.jpg", "img1.jpg", "img2.jpg", "img3.jpg"]

img_list = [cv.imread(fn) for fn in img_fn]

exposure_times = np.array([15.0, 2.5, 0.25, 0.0333], dtype=np.float32)

2合并图片成HDR

# Merge exposures to HDR image

merge_debevec = cv.createMergeDebevec()

hdr_debevec = merge_debevec.process(img_list, times=exposure_times.copy())

merge_robertson = cv.createMergeRobertson()

hdr_robertson = merge_robertson.process(img_list, times=exposure_times.copy())

3对合并的图片调整

# Tonemap HDR image

tonemap1 = cv.createTonemap(gamma=2.2)

res_debevec = tonemap1.process(hdr_debevec.copy())

4使用Mertens fusion合并图片

# Exposure fusion using Mertens

merge_mertens = cv.createMergeMertens()

res_mertens = merge_mertens.process(img_list)

5转换成8位格式的图片并保存

# Convert datatype to 8-bit and save

res_debevec_8bit = np.clip(res_debevec*255, 0, 255).astype('uint8')

res_robertson_8bit = np.clip(res_robertson*255, 0, 255).astype('uint8')

res_mertens_8bit = np.clip(res_mertens*255, 0, 255).astype('uint8')

cv.imwrite("ldr_debevec.jpg", res_debevec_8bit)

cv.imwrite("ldr_robertson.jpg", res_robertson_8bit)

cv.imwrite("fusion_mertens.jpg", res_mertens_8bit)

对摄像头的曲线评估

# Estimate camera response function (CRF)

cal_debevec = cv.createCalibrateDebevec()

crf_debevec = cal_debevec.process(img_list, times=exposure_times)

hdr_debevec = merge_debevec.process(img_list, times=exposure_times.copy(), response=crf_debevec.copy())

cal_robertson = cv.createCalibrateRobertson()

crf_robertson = cal_robertson.process(img_list, times=exposure_times)

hdr_robertson = merge_robertson.process(img_list, times=exposure_times.copy(), response=crf_robertson.copy())

https://docs.opencv.org/3.4/d2/df0/tutorial_py_hdr.html

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