Merge pull request #23108 from crackwitz:issue-23107

Usage of imread(): magic number 0, unchecked result

* docs: rewrite 0/1 to IMREAD_GRAYSCALE/IMREAD_COLOR in imread()

* samples, apps: rewrite 0/1 to IMREAD_GRAYSCALE/IMREAD_COLOR in imread()

* tests: rewrite 0/1 to IMREAD_GRAYSCALE/IMREAD_COLOR in imread()

* doc/py_tutorials: check imread() result

apps/traincascade/imagestorage.cpp

@@ -54,7 +54,7 @@ bool CvCascadeImageReader::NegReader::nextImg()
     size_t count = imgFilenames.size();
     for( size_t i = 0; i < count; i++ )
     {
-        src = imread( imgFilenames[last++], 0 );
+        src = imread( imgFilenames[last++], IMREAD_GRAYSCALE );
         if( src.empty() ){
             last %= count;
             continue;

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)
 im2,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)
 im2, 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)
 im2,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)
 

doc/tutorials/introduction/linux_eclipse/linux_eclipse.markdown

@@ -55,7 +55,7 @@ Making a project
     int main( int argc, char** argv )
     {
       Mat image;
-      image = imread( argv[1], 1 );
+      image = imread( argv[1], IMREAD_COLOR );
 
       if( argc != 2 || !image.data )
         {

doc/tutorials/introduction/linux_gcc_cmake/linux_gcc_cmake.markdown

@@ -35,7 +35,7 @@ int main(int argc, char** argv )
     }
 
     Mat image;
-    image = imread( argv[1], 1 );
+    image = imread( argv[1], IMREAD_COLOR );
 
     if ( !image.data )
     {

modules/calib3d/test/test_chesscorners.cpp

@@ -216,7 +216,7 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
 
         /* read the image */
         String img_file = board_list[idx * 2];
-        Mat gray = imread( folder + img_file, 0);
+        Mat gray = imread( folder + img_file, IMREAD_GRAYSCALE);
 
         if( gray.empty() )
         {

modules/calib3d/test/test_stereomatching.cpp

@@ -456,8 +456,8 @@ void CV_StereoMatchingTest::run(int)
         string datasetFullDirName = dataPath + DATASETS_DIR + datasetName + "/";
         Mat leftImg = imread(datasetFullDirName + LEFT_IMG_NAME);
         Mat rightImg = imread(datasetFullDirName + RIGHT_IMG_NAME);
-        Mat trueLeftDisp = imread(datasetFullDirName + TRUE_LEFT_DISP_NAME, 0);
-        Mat trueRightDisp = imread(datasetFullDirName + TRUE_RIGHT_DISP_NAME, 0);
+        Mat trueLeftDisp = imread(datasetFullDirName + TRUE_LEFT_DISP_NAME, IMREAD_GRAYSCALE);
+        Mat trueRightDisp = imread(datasetFullDirName + TRUE_RIGHT_DISP_NAME, IMREAD_GRAYSCALE);
         Rect calcROI;
 
         if( leftImg.empty() || rightImg.empty() || trueLeftDisp.empty() )
@@ -835,9 +835,9 @@ TEST_P(Calib3d_StereoBM_BufferBM, memAllocsTest)
     const int SADWindowSize = get<1>(get<1>(GetParam()));
 
     String path = cvtest::TS::ptr()->get_data_path() + "cv/stereomatching/datasets/teddy/";
-    Mat leftImg = imread(path + "im2.png", 0);
+    Mat leftImg = imread(path + "im2.png", IMREAD_GRAYSCALE);
     ASSERT_FALSE(leftImg.empty());
-    Mat rightImg = imread(path + "im6.png", 0);
+    Mat rightImg = imread(path + "im6.png", IMREAD_GRAYSCALE);
     ASSERT_FALSE(rightImg.empty());
     Mat leftDisp;
     {
@@ -923,9 +923,9 @@ TEST(Calib3d_StereoSGBM, regression) { CV_StereoSGBMTest test; test.safe_run();
 TEST(Calib3d_StereoSGBM_HH4, regression)
 {
     String path = cvtest::TS::ptr()->get_data_path() + "cv/stereomatching/datasets/teddy/";
-    Mat leftImg = imread(path + "im2.png", 0);
+    Mat leftImg = imread(path + "im2.png", IMREAD_GRAYSCALE);
     ASSERT_FALSE(leftImg.empty());
-    Mat rightImg = imread(path + "im6.png", 0);
+    Mat rightImg = imread(path + "im6.png", IMREAD_GRAYSCALE);
     ASSERT_FALSE(rightImg.empty());
     Mat testData = imread(path + "disp2_hh4.png",-1);
     ASSERT_FALSE(testData.empty());

modules/features2d/test/test_descriptors_regression.cpp

@@ -406,7 +406,7 @@ TEST( Features2d_DescriptorExtractor, batch_ORB )
     for( i = 0; i < n; i++ )
     {
         string imgname = format("%s/img%d.png", path.c_str(), i+1);
-        Mat img = imread(imgname, 0);
+        Mat img = imread(imgname, IMREAD_GRAYSCALE);
         imgs.push_back(img);
     }
 
@@ -434,7 +434,7 @@ TEST( Features2d_DescriptorExtractor, batch_SIFT )
     for( i = 0; i < n; i++ )
     {
         string imgname = format("%s/img%d.png", path.c_str(), i+1);
-        Mat img = imread(imgname, 0);
+        Mat img = imread(imgname, IMREAD_GRAYSCALE);
         imgs.push_back(img);
     }
 

modules/imgcodecs/misc/java/test/ImgcodecsTest.java

@@ -45,7 +45,7 @@ public class ImgcodecsTest extends OpenCVTestCase {
     }
 
     public void testImreadStringInt() {
-        dst = Imgcodecs.imread(OpenCVTestRunner.LENA_PATH, 0);
+        dst = Imgcodecs.imread(OpenCVTestRunner.LENA_PATH, Imgcodecs.IMREAD_GRAYSCALE);
         assertFalse(dst.empty());
         assertEquals(1, dst.channels());
         assertTrue(512 == dst.cols());

modules/imgproc/test/test_connectedcomponents.cpp

@@ -81,7 +81,7 @@ void CV_ConnectedComponentsTest::run(int /* start_from */)
     int ccltype[] = { cv::CCL_DEFAULT, cv::CCL_WU, cv::CCL_GRANA, cv::CCL_BOLELLI, cv::CCL_SAUF, cv::CCL_BBDT, cv::CCL_SPAGHETTI };
 
     string exp_path = string(ts->get_data_path()) + "connectedcomponents/ccomp_exp.png";
-    Mat exp = imread(exp_path, 0);
+    Mat exp = imread(exp_path, IMREAD_GRAYSCALE);
     Mat orig = imread(string(ts->get_data_path()) + "connectedcomponents/concentric_circles.png", 0);
 
     if (orig.empty())

modules/imgproc/test/test_imgproc_umat.cpp

@@ -53,7 +53,7 @@ protected:
     void run(int)
     {
         string imgpath = string(ts->get_data_path()) + "shared/lena.png";
-        Mat img = imread(imgpath, 1), gray, smallimg, result;
+        Mat img = imread(imgpath, IMREAD_COLOR), gray, smallimg, result;
         UMat uimg = img.getUMat(ACCESS_READ), ugray, usmallimg, uresult;
 
         cvtColor(img, gray, COLOR_BGR2GRAY);

modules/imgproc/test/test_watershed.cpp

@@ -59,7 +59,7 @@ CV_WatershedTest::~CV_WatershedTest() {}
 void CV_WatershedTest::run( int /* start_from */)
 {
     string exp_path = string(ts->get_data_path()) + "watershed/wshed_exp.png";
-    Mat exp = imread(exp_path, 0);
+    Mat exp = imread(exp_path, IMREAD_GRAYSCALE);
     Mat orig = imread(string(ts->get_data_path()) + "inpaint/orig.png");
     FileStorage fs(string(ts->get_data_path()) + "watershed/comp.xml", FileStorage::READ);
 

modules/java/test/android_test/src/org/opencv/test/OpenCVTestCase.java

@@ -149,7 +149,7 @@ public class OpenCVTestCase extends TestCase {
         rgba128 = new Mat(matSize, matSize, CvType.CV_8UC4, Scalar.all(128));
 
         rgbLena = Imgcodecs.imread(OpenCVTestRunner.LENA_PATH);
-        grayChess = Imgcodecs.imread(OpenCVTestRunner.CHESS_PATH, 0);
+        grayChess = Imgcodecs.imread(OpenCVTestRunner.CHESS_PATH, Imgcodecs.IMREAD_GRAYSCALE);
 
         gray255_32f_3d = new Mat(new int[]{matSize, matSize, matSize}, CvType.CV_32F, new Scalar(255.0));
 

modules/java/test/pure_test/src/org/opencv/test/OpenCVTestCase.java

@@ -175,7 +175,7 @@ public class OpenCVTestCase extends TestCase {
         rgba128 = new Mat(matSize, matSize, CvType.CV_8UC4, Scalar.all(128));
 
         rgbLena = Imgcodecs.imread(OpenCVTestRunner.LENA_PATH);
-        grayChess = Imgcodecs.imread(OpenCVTestRunner.CHESS_PATH, 0);
+        grayChess = Imgcodecs.imread(OpenCVTestRunner.CHESS_PATH, Imgcodecs.IMREAD_GRAYSCALE);
 
         gray255_32f_3d = new Mat(new int[]{matSize, matSize, matSize}, CvType.CV_32F, new Scalar(255.0));
 

modules/objdetect/test/test_cascadeandhog.cpp

@@ -137,7 +137,7 @@ int CV_DetectorTest::prepareData( FileStorage& _fs )
                 String filename;
                 it >> filename;
                 imageFilenames.push_back(filename);
-                Mat img = imread( dataPath+filename, 1 );
+                Mat img = imread( dataPath+filename, IMREAD_COLOR );
                 images.push_back( img );
             }
         }

modules/photo/test/test_denoising.cpp

@@ -157,7 +157,7 @@ TEST(Photo_White, issue_2646)
 TEST(Photo_Denoising, speed)
 {
     string imgname = string(cvtest::TS::ptr()->get_data_path()) + "shared/5MP.png";
-    Mat src = imread(imgname, 0), dst;
+    Mat src = imread(imgname, IMREAD_GRAYSCALE), dst;
 
     double t = (double)getTickCount();
     fastNlMeansDenoising(src, dst, 5, 7, 21);

modules/videoio/test/test_ffmpeg.cpp

@@ -194,7 +194,7 @@ public:
         {
             string filename = ts->get_data_path() + "readwrite/ordinary.bmp";
             VideoCapture cap(filename, CAP_FFMPEG);
-            Mat img0 = imread(filename, 1);
+            Mat img0 = imread(filename, IMREAD_COLOR);
             Mat img, img_next;
             cap >> img;
             cap >> img_next;

samples/cpp/3calibration.cpp

@@ -250,7 +250,7 @@ int main( int argc, char** argv )
         {
             int k1 = k == 0 ? 2 : k == 1 ? 0 : 1;
             printf("%s\n", imageList[i*3+k].c_str());
-            view = imread(imageList[i*3+k], 1);
+            view = imread(imageList[i*3+k], IMREAD_COLOR);
 
             if(!view.empty())
             {
@@ -338,7 +338,7 @@ int main( int argc, char** argv )
         {
             int k1 = k == 0 ? 2 : k == 1 ? 0 : 1;
             int k2 = k == 0 ? 1 : k == 1 ? 0 : 2;
-            view = imread(imageList[i*3+k], 1);
+            view = imread(imageList[i*3+k], IMREAD_COLOR);
 
             if(view.empty())
                 continue;

samples/cpp/calibration.cpp

@@ -456,7 +456,7 @@ int main( int argc, char** argv )
             view0.copyTo(view);
         }
         else if( i < (int)imageList.size() )
-            view = imread(imageList[i], 1);
+            view = imread(imageList[i], IMREAD_COLOR);
 
         if(view.empty())
         {
@@ -581,7 +581,7 @@ int main( int argc, char** argv )
 
         for( i = 0; i < (int)imageList.size(); i++ )
         {
-            view = imread(imageList[i], 1);
+            view = imread(imageList[i], IMREAD_COLOR);
             if(view.empty())
                 continue;
             remap(view, rview, map1, map2, INTER_LINEAR);

samples/cpp/facedetect.cpp

@@ -145,7 +145,7 @@ int main( int argc, const char** argv )
                         len--;
                     buf[len] = '\0';
                     cout << "file " << buf << endl;
-                    image = imread( buf, 1 );
+                    image = imread( buf, IMREAD_COLOR );
                     if( !image.empty() )
                     {
                         detectAndDraw( image, cascade, nestedCascade, scale, tryflip );

samples/cpp/pca.cpp

@@ -59,7 +59,7 @@ static void read_imgList(const string& filename, vector<Mat>& images) {
     }
     string line;
     while (getline(file, line)) {
-        images.push_back(imread(line, 0));
+        images.push_back(imread(line, IMREAD_GRAYSCALE));
     }
 }
 

samples/cpp/stereo_calib.cpp

@@ -80,7 +80,7 @@ StereoCalib(const vector<string>& imagelist, Size boardSize, float squareSize, b
         for( k = 0; k < 2; k++ )
         {
             const string& filename = imagelist[i*2+k];
-            Mat img = imread(filename, 0);
+            Mat img = imread(filename, IMREAD_GRAYSCALE);
             if(img.empty())
                 break;
             if( imageSize == Size() )
@@ -298,7 +298,7 @@ StereoCalib(const vector<string>& imagelist, Size boardSize, float squareSize, b
     {
         for( k = 0; k < 2; k++ )
         {
-            Mat img = imread(goodImageList[i*2+k], 0), rimg, cimg;
+            Mat img = imread(goodImageList[i*2+k], IMREAD_GRAYSCALE), rimg, cimg;
             remap(img, rimg, rmap[k][0], rmap[k][1], INTER_LINEAR);
             cvtColor(rimg, cimg, COLOR_GRAY2BGR);
             Mat canvasPart = !isVerticalStereo ? canvas(Rect(w*k, 0, w, h)) : canvas(Rect(0, h*k, w, h));

samples/cpp/tutorial_code/snippets/imgproc_HoughLinesCircles.cpp

@@ -8,7 +8,7 @@ using namespace std;
 int main(int argc, char** argv)
 {
     Mat img, gray;
-    if( argc != 2 || !(img=imread(argv[1], 1)).data)
+    if( argc != 2 || !(img=imread(argv[1], IMREAD_COLOR)).data)
         return -1;
     cvtColor(img, gray, COLOR_BGR2GRAY);
     // smooth it, otherwise a lot of false circles may be detected

samples/cpp/tutorial_code/snippets/imgproc_HoughLinesP.cpp

@@ -7,7 +7,7 @@ using namespace std;
 int main(int argc, char** argv)
 {
     Mat src, dst, color_dst;
-    if( argc != 2 || !(src=imread(argv[1], 0)).data)
+    if( argc != 2 || !(src=imread(argv[1], IMREAD_GRAYSCALE)).data)
         return -1;
 
     Canny( src, dst, 50, 200, 3 );

samples/cpp/tutorial_code/snippets/imgproc_calcHist.cpp

@@ -6,7 +6,7 @@ using namespace cv;
 int main( int argc, char** argv )
 {
     Mat src, hsv;
-    if( argc != 2 || !(src=imread(argv[1], 1)).data )
+    if( argc != 2 || !(src=imread(argv[1], IMREAD_COLOR)).data )
         return -1;
 
     cvtColor(src, hsv, COLOR_BGR2HSV);

samples/cpp/tutorial_code/snippets/imgproc_drawContours.cpp

@@ -9,7 +9,7 @@ int main( int argc, char** argv )
     Mat src;
     // the first command-line parameter must be a filename of the binary
     // (black-n-white) image
-    if( argc != 2 || !(src=imread(argv[1], 0)).data)
+    if( argc != 2 || !(src=imread(argv[1], IMREAD_GRAYSCALE)).data)
         return -1;
 
     Mat dst = Mat::zeros(src.rows, src.cols, CV_8UC3);

samples/cpp/watershed.cpp

@@ -54,7 +54,7 @@ int main( int argc, char** argv )
         return 0;
     }
     string filename = samples::findFile(parser.get<string>("@input"));
-    Mat img0 = imread(filename, 1), imgGray;
+    Mat img0 = imread(filename, IMREAD_COLOR), imgGray;
 
     if( img0.empty() )
     {

samples/python/calibrate.py

@@ -57,7 +57,7 @@ def main():
 
     def processImage(fn):
         print('processing %s... ' % fn)
-        img = cv.imread(fn, 0)
+        img = cv.imread(fn, cv.IMREAD_GRAYSCALE)
         if img is None:
             print("Failed to load", fn)
             return None

samples/python/mouse_and_match.py

@@ -69,7 +69,7 @@ class App():
             if ext == "png" or ext == "jpg" or ext == "bmp" or ext == "tiff" or ext == "pbm":
                 print(infile)
 
-                img = cv.imread(infile,1)
+                img = cv.imread(infile, cv.IMREAD_COLOR)
                 if img is None:
                     continue
                 self.sel = (0,0,0,0)