Update findContours parameter type

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

@@ -23,7 +23,7 @@ import cv2 as cv
 
 img = cv.imread('star.jpg',0)
 ret,thresh = cv.threshold(img,127,255,0)
-im2,contours,hierarchy = cv.findContours(thresh, 1, 2)
+contours,hierarchy = cv.findContours(thresh, 1, 2)
 
 cnt = contours[0]
 M = cv.moments(cnt)

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

@@ -17,7 +17,7 @@ detection and recognition.
 
 -   For better accuracy, use binary images. So before finding contours, apply threshold or canny
     edge detection.
--   Since OpenCV 3.2, findContours() no longer modifies the source image but returns a modified image as the first of three return parameters.
+-   Since OpenCV 3.2, findContours() no longer modifies the source image.
 -   In OpenCV, finding contours is like finding white object from black background. So remember,
     object to be found should be white and background should be black.
 
@@ -29,11 +29,11 @@ import cv2 as cv
 im = cv.imread('test.jpg')
 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)
+contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
 @endcode
 See, there are three arguments in **cv.findContours()** function, first one is source image, second
-is contour retrieval mode, third is contour approximation method. And it outputs a modified image, the contours and
-hierarchy. contours is a Python list of all the contours in the image. Each individual contour is a
+is contour retrieval mode, third is contour approximation method. And it outputs the contours and hierarchy.
+Contours is a Python list of all the contours in the image. Each individual contour is a
 Numpy array of (x,y) coordinates of boundary points of the object.
 
 @note We will discuss second and third arguments and about hierarchy in details later. Until then,

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

@@ -39,7 +39,7 @@ import numpy as np
 img = cv.imread('star.jpg')
 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)
+contours,hierarchy = cv.findContours(thresh,2,1)
 cnt = contours[0]
 
 hull = cv.convexHull(cnt,returnPoints = False)
@@ -93,9 +93,9 @@ img2 = cv.imread('star2.jpg',0)
 
 ret, thresh = cv.threshold(img1, 127, 255,0)
 ret, thresh2 = cv.threshold(img2, 127, 255,0)
-im2,contours,hierarchy = cv.findContours(thresh,2,1)
+contours,hierarchy = cv.findContours(thresh,2,1)
 cnt1 = contours[0]
-im2,contours,hierarchy = cv.findContours(thresh2,2,1)
+contours,hierarchy = cv.findContours(thresh2,2,1)
 cnt2 = contours[0]
 
 ret = cv.matchShapes(cnt1,cnt2,1,0.0)

modules/imgproc/include/opencv2/imgproc.hpp

@@ -3885,12 +3885,12 @@ parent, or nested contours, the corresponding elements of hierarchy[i] will be n
 contours are extracted from the image ROI and then they should be analyzed in the whole image
 context.
  */
-CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays contours,
+CV_EXPORTS_W void findContours( InputArray image, OutputArrayOfArrays contours,
                               OutputArray hierarchy, int mode,
                               int method, Point offset = Point());
 
 /** @overload */
-CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours,
+CV_EXPORTS void findContours( InputArray image, OutputArrayOfArrays contours,
                               int mode, int method, Point offset = Point());
 
 /** @example samples/cpp/squares.cpp

modules/imgproc/src/contours.cpp

@@ -1874,7 +1874,7 @@ cvFindContours( void*  img,  CvMemStorage*  storage,
     return cvFindContours_Impl(img, storage, firstContour, cntHeaderSize, mode, method, offset, 1);
 }
 
-void cv::findContours( InputOutputArray _image, OutputArrayOfArrays _contours,
+void cv::findContours( InputArray _image, OutputArrayOfArrays _contours,
                    OutputArray _hierarchy, int mode, int method, Point offset )
 {
     CV_INSTRUMENT_REGION();
@@ -1939,7 +1939,7 @@ void cv::findContours( InputOutputArray _image, OutputArrayOfArrays _contours,
     }
 }
 
-void cv::findContours( InputOutputArray _image, OutputArrayOfArrays _contours,
+void cv::findContours( InputArray _image, OutputArrayOfArrays _contours,
                        int mode, int method, Point offset)
 {
     CV_INSTRUMENT_REGION();

modules/python/test/test_shape.py

@@ -10,8 +10,8 @@ def test_computeDistance(self):
         a = self.get_sample('samples/data/shape_sample/1.png', cv.IMREAD_GRAYSCALE)
         b = self.get_sample('samples/data/shape_sample/2.png', cv.IMREAD_GRAYSCALE)
 
-        _, ca, _ = cv.findContours(a, cv.RETR_CCOMP, cv.CHAIN_APPROX_TC89_KCOS)
-        _, cb, _ = cv.findContours(b, cv.RETR_CCOMP, cv.CHAIN_APPROX_TC89_KCOS)
+        ca, _ = cv.findContours(a, cv.RETR_CCOMP, cv.CHAIN_APPROX_TC89_KCOS)
+        cb, _ = cv.findContours(b, cv.RETR_CCOMP, cv.CHAIN_APPROX_TC89_KCOS)
 
         hd = cv.createHausdorffDistanceExtractor()
         sd = cv.createShapeContextDistanceExtractor()

modules/python/test/test_squares.py

@@ -31,7 +31,7 @@ def find_squares(img):
                 bin = cv.dilate(bin, None)
             else:
                 _retval, bin = cv.threshold(gray, thrs, 255, cv.THRESH_BINARY)
-            bin, contours, _hierarchy = cv.findContours(bin, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)
+            contours, _hierarchy = cv.findContours(bin, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)
             for cnt in contours:
                 cnt_len = cv.arcLength(cnt, True)
                 cnt = cv.approxPolyDP(cnt, 0.02*cnt_len, True)

samples/python/contours.py

@@ -54,7 +54,7 @@ def make_image():
     img = make_image()
     h, w = img.shape[:2]
 
-    _, contours0, hierarchy = cv.findContours( img.copy(), cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+    contours0, hierarchy = cv.findContours( img.copy(), cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
     contours = [cv.approxPolyDP(cnt, 3, True) for cnt in contours0]
 
     def update(levels):

samples/python/digits_video.py

@@ -41,7 +41,7 @@ def main():
 
         bin = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY_INV, 31, 10)
         bin = cv.medianBlur(bin, 3)
-        _, contours, heirs = cv.findContours( bin.copy(), cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE)
+        contours, heirs = cv.findContours( bin.copy(), cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE)
         try:
             heirs = heirs[0]
         except:

samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py

@@ -91,7 +91,7 @@
 dist_8u = dist.astype('uint8')
 
 # Find total markers
-_, contours, _ = cv.findContours(dist_8u, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+contours, _ = cv.findContours(dist_8u, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
 
 # Create the marker image for the watershed algorithm
 markers = np.zeros(dist.shape, dtype=np.int32)

samples/python/tutorial_code/ShapeDescriptors/bounding_rects_circles/generalContours_demo1.py

@@ -16,7 +16,7 @@ def thresh_callback(val):
 
     ## [findContours]
     # Find contours
-    _, contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+    contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
     ## [findContours]
 
     ## [allthework]

samples/python/tutorial_code/ShapeDescriptors/bounding_rotated_ellipses/generalContours_demo2.py

@@ -16,7 +16,7 @@ def thresh_callback(val):
 
     ## [findContours]
     # Find contours
-    _, contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+    contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
     ## [findContours]
 
     # Find the rotated rectangles and ellipses for each contour

samples/python/tutorial_code/ShapeDescriptors/find_contours/findContours_demo.py

@@ -13,7 +13,7 @@ def thresh_callback(val):
     canny_output = cv.Canny(src_gray, threshold, threshold * 2)
 
     # Find contours
-    _, contours, hierarchy = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+    contours, hierarchy = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
 
     # Draw contours
     drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)

samples/python/tutorial_code/ShapeDescriptors/hull/hull_demo.py

@@ -13,7 +13,7 @@ def thresh_callback(val):
     canny_output = cv.Canny(src_gray, threshold, threshold * 2)
 
     # Find contours
-    _, contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+    contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
 
     # Find the convex hull object for each contour
     hull_list = []

samples/python/tutorial_code/ShapeDescriptors/moments/moments_demo.py

@@ -17,7 +17,7 @@ def thresh_callback(val):
 
     ## [findContours]
     # Find contours
-    _, contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+    contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
     ## [findContours]
 
     # Get the moments

samples/python/tutorial_code/ShapeDescriptors/point_polygon_test/pointPolygonTest_demo.py

@@ -21,7 +21,7 @@
     cv.line(src, vert[i],  vert[(i+1)%6], ( 255 ), 3)
 
 # Get the contours
-_, contours, _ = cv.findContours(src, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+contours, _ = cv.findContours(src, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
 
 # Calculate the distances to the contour
 raw_dist = np.empty(src.shape, dtype=np.float32)

samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py

@@ -81,7 +81,7 @@ def getOrientation(pts, img):
 
 ## [contours]
 # Find all the contours in the thresholded image
-_, contours, _ = cv.findContours(bw, cv.RETR_LIST, cv.CHAIN_APPROX_NONE)
+contours, _ = cv.findContours(bw, cv.RETR_LIST, cv.CHAIN_APPROX_NONE)
 
 for i, c in enumerate(contours):
     # Calculate the area of each contour