Merge remote-tracking branch 'upstream/3.4' into merge-3.4

doc/py_tutorials/py_calib3d/py_depthmap/py_depthmap.markdown

@@ -41,8 +41,8 @@ import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt
 
-imgL = cv.imread('tsukuba_l.png',0)
-imgR = cv.imread('tsukuba_r.png',0)
+imgL = cv.imread('tsukuba_l.png', cv.IMREAD_GRAYSCALE)
+imgR = cv.imread('tsukuba_r.png', cv.IMREAD_GRAYSCALE)
 
 stereo = cv.StereoBM_create(numDisparities=16, blockSize=15)
 disparity = stereo.compute(imgL,imgR)

doc/py_tutorials/py_calib3d/py_epipolar_geometry/py_epipolar_geometry.markdown

@@ -76,8 +76,8 @@ import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt
 
-img1 = cv.imread('myleft.jpg',0)  #queryimage # left image
-img2 = cv.imread('myright.jpg',0) #trainimage # right image
+img1 = cv.imread('myleft.jpg', cv.IMREAD_GRAYSCALE)  #queryimage # left image
+img2 = cv.imread('myright.jpg', cv.IMREAD_GRAYSCALE) #trainimage # right image
 
 sift = cv.SIFT_create()
 

doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown

@@ -25,6 +25,7 @@ Let's load a color image first:
 >>> import cv2 as cv
 
 >>> img = cv.imread('messi5.jpg')
+>>> assert img is not None, "file could not be read, check with os.path.exists()"
 @endcode
 You can access a pixel value by its row and column coordinates. For BGR image, it returns an array
 of Blue, Green, Red values. For grayscale image, just corresponding intensity is returned.
@@ -173,6 +174,7 @@ from matplotlib import pyplot as plt
 BLUE = [255,0,0]
 
 img1 = cv.imread('opencv-logo.png')
+assert img1 is not None, "file could not be read, check with os.path.exists()"
 
 replicate = cv.copyMakeBorder(img1,10,10,10,10,cv.BORDER_REPLICATE)
 reflect = cv.copyMakeBorder(img1,10,10,10,10,cv.BORDER_REFLECT)

doc/py_tutorials/py_core/py_image_arithmetics/py_image_arithmetics.markdown

@@ -50,6 +50,8 @@ Here \f$\gamma\f$ is taken as zero.
 @code{.py}
 img1 = cv.imread('ml.png')
 img2 = cv.imread('opencv-logo.png')
+assert img1 is not None, "file could not be read, check with os.path.exists()"
+assert img2 is not None, "file could not be read, check with os.path.exists()"
 
 dst = cv.addWeighted(img1,0.7,img2,0.3,0)
 
@@ -76,6 +78,8 @@ bitwise operations as shown below:
 # Load two images
 img1 = cv.imread('messi5.jpg')
 img2 = cv.imread('opencv-logo-white.png')
+assert img1 is not None, "file could not be read, check with os.path.exists()"
+assert img2 is not None, "file could not be read, check with os.path.exists()"
 
 # I want to put logo on top-left corner, So I create a ROI
 rows,cols,channels = img2.shape

doc/py_tutorials/py_core/py_optimization/py_optimization.markdown

@@ -37,6 +37,7 @@ of odd sizes ranging from 5 to 49. (Don't worry about what the result will look
 goal):
 @code{.py}
 img1 = cv.imread('messi5.jpg')
+assert img1 is not None, "file could not be read, check with os.path.exists()"
 
 e1 = cv.getTickCount()
 for i in range(5,49,2):

doc/py_tutorials/py_feature2d/py_brief/py_brief.markdown

@@ -63,7 +63,7 @@ import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt
 
-img = cv.imread('simple.jpg',0)
+img = cv.imread('simple.jpg', cv.IMREAD_GRAYSCALE)
 
 # Initiate FAST detector
 star = cv.xfeatures2d.StarDetector_create()

doc/py_tutorials/py_feature2d/py_fast/py_fast.markdown

@@ -98,7 +98,7 @@ import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt
 
-img = cv.imread('blox.jpg',0) # `<opencv_root>/samples/data/blox.jpg`
+img = cv.imread('blox.jpg', cv.IMREAD_GRAYSCALE) # `<opencv_root>/samples/data/blox.jpg`
 
 # Initiate FAST object with default values
 fast = cv.FastFeatureDetector_create()

doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown

@@ -40,8 +40,8 @@ from matplotlib import pyplot as plt
 
 MIN_MATCH_COUNT = 10
 
-img1 = cv.imread('box.png',0)          # queryImage
-img2 = cv.imread('box_in_scene.png',0) # trainImage
+img1 = cv.imread('box.png', cv.IMREAD_GRAYSCALE)          # queryImage
+img2 = cv.imread('box_in_scene.png', cv.IMREAD_GRAYSCALE) # trainImage
 
 # Initiate SIFT detector
 sift = cv.SIFT_create()

doc/py_tutorials/py_feature2d/py_orb/py_orb.markdown

@@ -67,7 +67,7 @@ import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt
 
-img = cv.imread('simple.jpg',0)
+img = cv.imread('simple.jpg', cv.IMREAD_GRAYSCALE)
 
 # Initiate ORB detector
 orb = cv.ORB_create()

doc/py_tutorials/py_feature2d/py_surf_intro/py_surf_intro.markdown

@@ -76,7 +76,7 @@ and descriptors.
 First we will see a simple demo on how to find SURF keypoints and descriptors and draw it. All
 examples are shown in Python terminal since it is just same as SIFT only.
 @code{.py}
->>> img = cv.imread('fly.png',0)
+>>> img = cv.imread('fly.png', cv.IMREAD_GRAYSCALE)
 
 # Create SURF object. You can specify params here or later.
 # Here I set Hessian Threshold to 400

doc/py_tutorials/py_imgproc/py_canny/py_canny.markdown

@@ -83,7 +83,8 @@ import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt
 
-img = cv.imread('messi5.jpg',0)
+img = cv.imread('messi5.jpg', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 edges = cv.Canny(img,100,200)
 
 plt.subplot(121),plt.imshow(img,cmap = 'gray')

doc/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.markdown

@@ -24,7 +24,8 @@ The function **cv.moments()** gives a dictionary of all moment values calculated
 import numpy as np
 import cv2 as cv
 
-img = cv.imread('star.jpg',0)
+img = cv.imread('star.jpg', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 ret,thresh = cv.threshold(img,127,255,0)
 contours,hierarchy = cv.findContours(thresh, 1, 2)
 

doc/py_tutorials/py_imgproc/py_contours/py_contours_begin/py_contours_begin.markdown

@@ -29,6 +29,7 @@ import numpy as np
 import cv2 as cv
 
 im = cv.imread('test.jpg')
+assert im is not None, "file could not be read, check with os.path.exists()"
 imgray = cv.cvtColor(im, cv.COLOR_BGR2GRAY)
 ret, thresh = cv.threshold(imgray, 127, 255, 0)
 contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)

doc/py_tutorials/py_imgproc/py_contours/py_contours_more_functions/py_contours_more_functions.markdown

@@ -41,6 +41,7 @@ import cv2 as cv
 import numpy as np
 
 img = cv.imread('star.jpg')
+assert img is not None, "file could not be read, check with os.path.exists()"
 img_gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
 ret,thresh = cv.threshold(img_gray, 127, 255,0)
 contours,hierarchy = cv.findContours(thresh,2,1)
@@ -92,8 +93,10 @@ docs.
 import cv2 as cv
 import numpy as np
 
-img1 = cv.imread('star.jpg',0)
-img2 = cv.imread('star2.jpg',0)
+img1 = cv.imread('star.jpg', cv.IMREAD_GRAYSCALE)
+img2 = cv.imread('star2.jpg', cv.IMREAD_GRAYSCALE)
+assert img1 is not None, "file could not be read, check with os.path.exists()"
+assert img2 is not None, "file could not be read, check with os.path.exists()"
 
 ret, thresh = cv.threshold(img1, 127, 255,0)
 ret, thresh2 = cv.threshold(img2, 127, 255,0)

doc/py_tutorials/py_imgproc/py_filtering/py_filtering.markdown

@@ -29,6 +29,7 @@ import cv2 as cv
 from matplotlib import pyplot as plt
 
 img = cv.imread('opencv_logo.png')
+assert img is not None, "file could not be read, check with os.path.exists()"
 
 kernel = np.ones((5,5),np.float32)/25
 dst = cv.filter2D(img,-1,kernel)
@@ -70,6 +71,7 @@ import numpy as np
 from matplotlib import pyplot as plt
 
 img = cv.imread('opencv-logo-white.png')
+assert img is not None, "file could not be read, check with os.path.exists()"
 
 blur = cv.blur(img,(5,5))
 

doc/py_tutorials/py_imgproc/py_geometric_transformations/py_geometric_transformations.markdown

@@ -28,6 +28,7 @@ import numpy as np
 import cv2 as cv
 
 img = cv.imread('messi5.jpg')
+assert img is not None, "file could not be read, check with os.path.exists()"
 
 res = cv.resize(img,None,fx=2, fy=2, interpolation = cv.INTER_CUBIC)
 
@@ -49,7 +50,8 @@ function. See the below example for a shift of (100,50):
 import numpy as np
 import cv2 as cv
 
-img = cv.imread('messi5.jpg',0)
+img = cv.imread('messi5.jpg', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 rows,cols = img.shape
 
 M = np.float32([[1,0,100],[0,1,50]])
@@ -87,7 +89,8 @@ where:
 To find this transformation matrix, OpenCV provides a function, **cv.getRotationMatrix2D**. Check out the
 below example which rotates the image by 90 degree with respect to center without any scaling.
 @code{.py}
-img = cv.imread('messi5.jpg',0)
+img = cv.imread('messi5.jpg', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 rows,cols = img.shape
 
 # cols-1 and rows-1 are the coordinate limits.
@@ -108,6 +111,7 @@ which is to be passed to **cv.warpAffine**.
 Check the below example, and also look at the points I selected (which are marked in green color):
 @code{.py}
 img = cv.imread('drawing.png')
+assert img is not None, "file could not be read, check with os.path.exists()"
 rows,cols,ch = img.shape
 
 pts1 = np.float32([[50,50],[200,50],[50,200]])
@@ -137,6 +141,7 @@ matrix.
 See the code below:
 @code{.py}
 img = cv.imread('sudoku.png')
+assert img is not None, "file could not be read, check with os.path.exists()"
 rows,cols,ch = img.shape
 
 pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]])

doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.markdown

@@ -93,6 +93,7 @@ import cv2 as cv
 from matplotlib import pyplot as plt
 
 img = cv.imread('messi5.jpg')
+assert img is not None, "file could not be read, check with os.path.exists()"
 mask = np.zeros(img.shape[:2],np.uint8)
 
 bgdModel = np.zeros((1,65),np.float64)
@@ -122,7 +123,8 @@ remaining background with gray. Then loaded that mask image in OpenCV, edited or
 got with corresponding values in newly added mask image. Check the code below:*
 @code{.py}
 # newmask is the mask image I manually labelled
-newmask = cv.imread('newmask.png',0)
+newmask = cv.imread('newmask.png', cv.IMREAD_GRAYSCALE)
+assert newmask is not None, "file could not be read, check with os.path.exists()"
 
 # wherever it is marked white (sure foreground), change mask=1
 # wherever it is marked black (sure background), change mask=0

doc/py_tutorials/py_imgproc/py_gradients/py_gradients.markdown

@@ -42,7 +42,8 @@ import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt
 
-img = cv.imread('dave.jpg',0)
+img = cv.imread('dave.jpg', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 
 laplacian = cv.Laplacian(img,cv.CV_64F)
 sobelx = cv.Sobel(img,cv.CV_64F,1,0,ksize=5)
@@ -79,7 +80,8 @@ import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt
 
-img = cv.imread('box.png',0)
+img = cv.imread('box.png', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 
 # Output dtype = cv.CV_8U
 sobelx8u = cv.Sobel(img,cv.CV_8U,1,0,ksize=5)

doc/py_tutorials/py_imgproc/py_histograms/py_2d_histogram/py_2d_histogram.markdown

@@ -38,6 +38,7 @@ import numpy as np
 import cv2 as cv
 
 img = cv.imread('home.jpg')
+assert img is not None, "file could not be read, check with os.path.exists()"
 hsv = cv.cvtColor(img,cv.COLOR_BGR2HSV)
 
 hist = cv.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
@@ -55,6 +56,7 @@ import cv2 as cv
 from matplotlib import pyplot as plt
 
 img = cv.imread('home.jpg')
+assert img is not None, "file could not be read, check with os.path.exists()"
 hsv = cv.cvtColor(img,cv.COLOR_BGR2HSV)
 
 hist, xbins, ybins = np.histogram2d(h.ravel(),s.ravel(),[180,256],[[0,180],[0,256]])
@@ -89,6 +91,7 @@ import cv2 as cv
 from matplotlib import pyplot as plt
 
 img = cv.imread('home.jpg')
+assert img is not None, "file could not be read, check with os.path.exists()"
 hsv = cv.cvtColor(img,cv.COLOR_BGR2HSV)
 hist = cv.calcHist( [hsv], [0, 1], None, [180, 256], [0, 180, 0, 256] )
 

doc/py_tutorials/py_imgproc/py_histograms/py_histogram_backprojection/py_histogram_backprojection.markdown

@@ -38,10 +38,12 @@ import cv2 as cvfrom matplotlib import pyplot as plt
 
 #roi is the object or region of object we need to find
 roi = cv.imread('rose_red.png')
+assert roi is not None, "file could not be read, check with os.path.exists()"
 hsv = cv.cvtColor(roi,cv.COLOR_BGR2HSV)
 
 #target is the image we search in
 target = cv.imread('rose.png')
+assert target is not None, "file could not be read, check with os.path.exists()"
 hsvt = cv.cvtColor(target,cv.COLOR_BGR2HSV)
 
 # Find the histograms using calcHist. Can be done with np.histogram2d also
@@ -85,9 +87,11 @@ import numpy as np
 import cv2 as cv
 
 roi = cv.imread('rose_red.png')
+assert roi is not None, "file could not be read, check with os.path.exists()"
 hsv = cv.cvtColor(roi,cv.COLOR_BGR2HSV)
 
 target = cv.imread('rose.png')
+assert target is not None, "file could not be read, check with os.path.exists()"
 hsvt = cv.cvtColor(target,cv.COLOR_BGR2HSV)
 
 # calculating object histogram

doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.markdown

@@ -77,7 +77,8 @@ and its parameters :
 So let's start with a sample image. Simply load an image in grayscale mode and find its full
 histogram.
 @code{.py}
-img = cv.imread('home.jpg',0)
+img = cv.imread('home.jpg', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 hist = cv.calcHist([img],[0],None,[256],[0,256])
 @endcode
 hist is a 256x1 array, each value corresponds to number of pixels in that image with its
@@ -121,7 +122,8 @@ import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt
 
-img = cv.imread('home.jpg',0)
+img = cv.imread('home.jpg', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 plt.hist(img.ravel(),256,[0,256]); plt.show()
 @endcode
 You will get a plot as below :
@@ -136,6 +138,7 @@ import cv2 as cv
 from matplotlib import pyplot as plt
 
 img = cv.imread('home.jpg')
+assert img is not None, "file could not be read, check with os.path.exists()"
 color = ('b','g','r')
 for i,col in enumerate(color):
     histr = cv.calcHist([img],[i],None,[256],[0,256])
@@ -164,7 +167,8 @@ We used cv.calcHist() to find the histogram of the full image. What if you want
 of some regions of an image? Just create a mask image with white color on the region you want to
 find histogram and black otherwise. Then pass this as the mask.
 @code{.py}
-img = cv.imread('home.jpg',0)
+img = cv.imread('home.jpg', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 
 # create a mask
 mask = np.zeros(img.shape[:2], np.uint8)

doc/py_tutorials/py_imgproc/py_histograms/py_histogram_equalization/py_histogram_equalization.markdown

@@ -30,7 +30,8 @@ import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt
 
-img = cv.imread('wiki.jpg',0)
+img = cv.imread('wiki.jpg', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 
 hist,bins = np.histogram(img.flatten(),256,[0,256])
 
@@ -81,7 +82,8 @@ output is our histogram equalized image.
 
 Below is a simple code snippet showing its usage for same image we used :
 @code{.py}
-img = cv.imread('wiki.jpg',0)
+img = cv.imread('wiki.jpg', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 equ = cv.equalizeHist(img)
 res = np.hstack((img,equ)) #stacking images side-by-side
 cv.imwrite('res.png',res)
@@ -124,7 +126,8 @@ Below code snippet shows how to apply CLAHE in OpenCV:
 import numpy as np
 import cv2 as cv
 
-img = cv.imread('tsukuba_l.png',0)
+img = cv.imread('tsukuba_l.png', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 
 # create a CLAHE object (Arguments are optional).
 clahe = cv.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))

doc/py_tutorials/py_imgproc/py_houghcircles/py_houghcircles.markdown

@@ -23,7 +23,8 @@ explained in the documentation. So we directly go to the code.
 import numpy as np
 import cv2 as cv
 
-img = cv.imread('opencv-logo-white.png',0)
+img = cv.imread('opencv-logo-white.png', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 img = cv.medianBlur(img,5)
 cimg = cv.cvtColor(img,cv.COLOR_GRAY2BGR)
 

doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.markdown

@@ -38,7 +38,8 @@ Here, as an example, I would use a 5x5 kernel with full of ones. Let's see it ho
 import cv2 as cv
 import numpy as np
 
-img = cv.imread('j.png',0)
+img = cv.imread('j.png', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 kernel = np.ones((5,5),np.uint8)
 erosion = cv.erode(img,kernel,iterations = 1)
 @endcode

doc/py_tutorials/py_imgproc/py_pyramids/py_pyramids.markdown

@@ -31,6 +31,7 @@ Similarly while expanding, area becomes 4 times in each level. We can find Gauss
 **cv.pyrDown()** and **cv.pyrUp()** functions.
 @code{.py}
 img = cv.imread('messi5.jpg')
+assert img is not None, "file could not be read, check with os.path.exists()"
 lower_reso = cv.pyrDown(higher_reso)
 @endcode
 Below is the 4 levels in an image pyramid.
@@ -84,6 +85,8 @@ import numpy as np,sys
 
 A = cv.imread('apple.jpg')
 B = cv.imread('orange.jpg')
+assert A is not None, "file could not be read, check with os.path.exists()"
+assert B is not None, "file could not be read, check with os.path.exists()"
 
 # generate Gaussian pyramid for A
 G = A.copy()

doc/py_tutorials/py_imgproc/py_template_matching/py_template_matching.markdown

@@ -38,9 +38,11 @@ import cv2 as cv
 import numpy as np
 from matplotlib import pyplot as plt
 
-img = cv.imread('messi5.jpg',0)
+img = cv.imread('messi5.jpg', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 img2 = img.copy()
-template = cv.imread('template.jpg',0)
+template = cv.imread('template.jpg', cv.IMREAD_GRAYSCALE)
+assert template is not None, "file could not be read, check with os.path.exists()"
 w, h = template.shape[::-1]
 
 # All the 6 methods for comparison in a list
@@ -113,8 +115,10 @@ import numpy as np
 from matplotlib import pyplot as plt
 
 img_rgb = cv.imread('mario.png')
+assert img_rgb is not None, "file could not be read, check with os.path.exists()"
 img_gray = cv.cvtColor(img_rgb, cv.COLOR_BGR2GRAY)
-template = cv.imread('mario_coin.png',0)
+template = cv.imread('mario_coin.png', cv.IMREAD_GRAYSCALE)
+assert template is not None, "file could not be read, check with os.path.exists()"
 w, h = template.shape[::-1]
 
 res = cv.matchTemplate(img_gray,template,cv.TM_CCOEFF_NORMED)

doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.markdown

@@ -37,7 +37,8 @@ import cv2 as cv
 import numpy as np
 from matplotlib import pyplot as plt
 
-img = cv.imread('gradient.png',0)
+img = cv.imread('gradient.png', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 ret,thresh1 = cv.threshold(img,127,255,cv.THRESH_BINARY)
 ret,thresh2 = cv.threshold(img,127,255,cv.THRESH_BINARY_INV)
 ret,thresh3 = cv.threshold(img,127,255,cv.THRESH_TRUNC)
@@ -85,7 +86,8 @@ import cv2 as cv
 import numpy as np
 from matplotlib import pyplot as plt
 
-img = cv.imread('sudoku.png',0)
+img = cv.imread('sudoku.png', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 img = cv.medianBlur(img,5)
 
 ret,th1 = cv.threshold(img,127,255,cv.THRESH_BINARY)
@@ -133,7 +135,8 @@ import cv2 as cv
 import numpy as np
 from matplotlib import pyplot as plt
 
-img = cv.imread('noisy2.png',0)
+img = cv.imread('noisy2.png', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 
 # global thresholding
 ret1,th1 = cv.threshold(img,127,255,cv.THRESH_BINARY)
@@ -183,7 +186,8 @@ where
 It actually finds a value of t which lies in between two peaks such that variances to both classes
 are minimal. It can be simply implemented in Python as follows:
 @code{.py}
-img = cv.imread('noisy2.png',0)
+img = cv.imread('noisy2.png', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 blur = cv.GaussianBlur(img,(5,5),0)
 
 # find normalized_histogram, and its cumulative distribution function

doc/py_tutorials/py_imgproc/py_transforms/py_fourier_transform/py_fourier_transform.markdown

@@ -54,7 +54,8 @@ import cv2 as cv
 import numpy as np
 from matplotlib import pyplot as plt
 
-img = cv.imread('messi5.jpg',0)
+img = cv.imread('messi5.jpg', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 f = np.fft.fft2(img)
 fshift = np.fft.fftshift(f)
 magnitude_spectrum = 20*np.log(np.abs(fshift))
@@ -121,7 +122,8 @@ import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt
 
-img = cv.imread('messi5.jpg',0)
+img = cv.imread('messi5.jpg', cv.IMREAD_GRAYSCALE)
+assert img is not None, "file could not be read, check with os.path.exists()"
 
 dft = cv.dft(np.float32(img),flags = cv.DFT_COMPLEX_OUTPUT)
 dft_shift = np.fft.fftshift(dft)
@@ -184,7 +186,8 @@ So how do we find this optimal size ? OpenCV provides a function, **cv.getOptima
 this. It is applicable to both **cv.dft()** and **np.fft.fft2()**. Let's check their performance
 using IPython magic command %timeit.
 @code{.py}
-In [16]: img = cv.imread('messi5.jpg',0)
+In [15]: img = cv.imread('messi5.jpg', cv.IMREAD_GRAYSCALE)
+In [16]: assert img is not None, "file could not be read, check with os.path.exists()"
 In [17]: rows,cols = img.shape
 In [18]: print("{} {}".format(rows,cols))
 342 548

doc/py_tutorials/py_imgproc/py_watershed/py_watershed.markdown

@@ -49,6 +49,7 @@ import cv2 as cv
 from matplotlib import pyplot as plt
 
 img = cv.imread('coins.png')
+assert img is not None, "file could not be read, check with os.path.exists()"
 gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
 ret, thresh = cv.threshold(gray,0,255,cv.THRESH_BINARY_INV+cv.THRESH_OTSU)
 @endcode

doc/py_tutorials/py_photo/py_inpainting/py_inpainting.markdown

@@ -56,7 +56,7 @@ import numpy as np
 import cv2 as cv
 
 img = cv.imread('messi_2.jpg')
-mask = cv.imread('mask2.png',0)
+mask = cv.imread('mask2.png', cv.IMREAD_GRAYSCALE)
 
 dst = cv.inpaint(img,mask,3,cv.INPAINT_TELEA)