圖片去燥

主要函數

cv.fastNlMeansDenoising()對灰度圖像去燥

cv.fastNlMeansDenoisingColored() 對彩色圖像去燥

cv.fastNlMeansDenoisingMulti()對灰度圖片序列去燥

cv.fastNlMeansDenoisingColoredMulti()對彩色圖片序列去燥

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

圖片修補

首先需要創建一個保護罩

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

圖片高動態范圍成像

Goal

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

Use exposure fusion to merge an exposure sequence.

1加載不同曝光的圖片到列表中

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合并圖片成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對合并的圖片調整

# Tonemap HDR image

tonemap1 = cv.createTonemap(gamma=2.2)

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

4使用Mertens fusion合并圖片

# Exposure fusion using Mertens

merge_mertens = cv.createMergeMertens()

res_mertens = merge_mertens.process(img_list)

5轉換成8位格式的圖片并保存

# 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)

對攝像頭的曲線評估

# 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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