initial commit

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

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+from __future__ import print_function
+import cv2 as cv
+import numpy as np
+import argparse
+import random as rng
+
+rng.seed(12345)
+
+def thresh_callback(val):
+    threshold = val
+
+    ## [Canny]
+    # Detect edges using Canny
+    canny_output = cv.Canny(src_gray, threshold, threshold * 2)
+    ## [Canny]
+
+    ## [findContours]
+    # Find contours
+    contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+    ## [findContours]
+
+    ## [allthework]
+    # Approximate contours to polygons + get bounding rects and circles
+    contours_poly = [None]*len(contours)
+    boundRect = [None]*len(contours)
+    centers = [None]*len(contours)
+    radius = [None]*len(contours)
+    for i, c in enumerate(contours):
+        contours_poly[i] = cv.approxPolyDP(c, 3, True)
+        boundRect[i] = cv.boundingRect(contours_poly[i])
+        centers[i], radius[i] = cv.minEnclosingCircle(contours_poly[i])
+    ## [allthework]
+
+    ## [zeroMat]
+    drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
+    ## [zeroMat]
+
+    ## [forContour]
+    # Draw polygonal contour + bonding rects + circles
+    for i in range(len(contours)):
+        color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
+        cv.drawContours(drawing, contours_poly, i, color)
+        cv.rectangle(drawing, (int(boundRect[i][0]), int(boundRect[i][1])), \
+          (int(boundRect[i][0]+boundRect[i][2]), int(boundRect[i][1]+boundRect[i][3])), color, 2)
+        cv.circle(drawing, (int(centers[i][0]), int(centers[i][1])), int(radius[i]), color, 2)
+    ## [forContour]
+
+    ## [showDrawings]
+    # Show in a window
+    cv.imshow('Contours', drawing)
+    ## [showDrawings]
+
+## [setup]
+# Load source image
+parser = argparse.ArgumentParser(description='Code for Creating Bounding boxes and circles for contours tutorial.')
+parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
+args = parser.parse_args()
+
+src = cv.imread(cv.samples.findFile(args.input))
+if src is None:
+    print('Could not open or find the image:', args.input)
+    exit(0)
+
+# Convert image to gray and blur it
+src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
+src_gray = cv.blur(src_gray, (3,3))
+## [setup]
+
+## [createWindow]
+# Create Window
+source_window = 'Source'
+cv.namedWindow(source_window)
+cv.imshow(source_window, src)
+## [createWindow]
+## [trackbar]
+max_thresh = 255
+thresh = 100 # initial threshold
+cv.createTrackbar('Canny thresh:', source_window, thresh, max_thresh, thresh_callback)
+thresh_callback(thresh)
+## [trackbar]
+
+cv.waitKey()

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

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+from __future__ import print_function
+import cv2 as cv
+import numpy as np
+import argparse
+import random as rng
+
+rng.seed(12345)
+
+def thresh_callback(val):
+    threshold = val
+
+    ## [Canny]
+    # Detect edges using Canny
+    canny_output = cv.Canny(src_gray, threshold, threshold * 2)
+    ## [Canny]
+
+    ## [findContours]
+    # Find contours
+    contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+    ## [findContours]
+
+    # Find the rotated rectangles and ellipses for each contour
+    minRect = [None]*len(contours)
+    minEllipse = [None]*len(contours)
+    for i, c in enumerate(contours):
+        minRect[i] = cv.minAreaRect(c)
+        if c.shape[0] > 5:
+            minEllipse[i] = cv.fitEllipse(c)
+
+    # Draw contours + rotated rects + ellipses
+    ## [zeroMat]
+    drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
+    ## [zeroMat]
+    ## [forContour]
+    for i, c in enumerate(contours):
+        color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
+        # contour
+        cv.drawContours(drawing, contours, i, color)
+        # ellipse
+        if c.shape[0] > 5:
+            cv.ellipse(drawing, minEllipse[i], color, 2)
+        # rotated rectangle
+        box = cv.boxPoints(minRect[i])
+        box = np.intp(box) #np.intp: Integer used for indexing (same as C ssize_t; normally either int32 or int64)
+        cv.drawContours(drawing, [box], 0, color)
+    ## [forContour]
+
+    ## [showDrawings]
+    # Show in a window
+    cv.imshow('Contours', drawing)
+    ## [showDrawings]
+
+## [setup]
+# Load source image
+parser = argparse.ArgumentParser(description='Code for Creating Bounding rotated boxes and ellipses for contours tutorial.')
+parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
+args = parser.parse_args()
+
+src = cv.imread(cv.samples.findFile(args.input))
+if src is None:
+    print('Could not open or find the image:', args.input)
+    exit(0)
+
+# Convert image to gray and blur it
+src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
+src_gray = cv.blur(src_gray, (3,3))
+## [setup]
+
+## [createWindow]
+# Create Window
+source_window = 'Source'
+cv.namedWindow(source_window)
+cv.imshow(source_window, src)
+## [createWindow]
+## [trackbar]
+max_thresh = 255
+thresh = 100 # initial threshold
+cv.createTrackbar('Canny Thresh:', source_window, thresh, max_thresh, thresh_callback)
+thresh_callback(thresh)
+## [trackbar]
+
+cv.waitKey()

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

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+from __future__ import print_function
+import cv2 as cv
+import numpy as np
+import argparse
+import random as rng
+
+rng.seed(12345)
+
+def thresh_callback(val):
+    threshold = val
+
+    # Detect edges using Canny
+    canny_output = cv.Canny(src_gray, threshold, threshold * 2)
+
+    # Find contours
+    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)
+    for i in range(len(contours)):
+        color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
+        cv.drawContours(drawing, contours, i, color, 2, cv.LINE_8, hierarchy, 0)
+
+    # Show in a window
+    cv.imshow('Contours', drawing)
+
+# Load source image
+parser = argparse.ArgumentParser(description='Code for Finding contours in your image tutorial.')
+parser.add_argument('--input', help='Path to input image.', default='HappyFish.jpg')
+args = parser.parse_args()
+
+src = cv.imread(cv.samples.findFile(args.input))
+if src is None:
+    print('Could not open or find the image:', args.input)
+    exit(0)
+
+# Convert image to gray and blur it
+src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
+src_gray = cv.blur(src_gray, (3,3))
+
+# Create Window
+source_window = 'Source'
+cv.namedWindow(source_window)
+cv.imshow(source_window, src)
+max_thresh = 255
+thresh = 100 # initial threshold
+cv.createTrackbar('Canny Thresh:', source_window, thresh, max_thresh, thresh_callback)
+thresh_callback(thresh)
+
+cv.waitKey()

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

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+from __future__ import print_function
+import cv2 as cv
+import numpy as np
+import argparse
+import random as rng
+
+rng.seed(12345)
+
+def thresh_callback(val):
+    threshold = val
+
+    # Detect edges using Canny
+    canny_output = cv.Canny(src_gray, threshold, threshold * 2)
+
+    # Find contours
+    contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+
+    # Find the convex hull object for each contour
+    hull_list = []
+    for i in range(len(contours)):
+        hull = cv.convexHull(contours[i])
+        hull_list.append(hull)
+
+    # Draw contours + hull results
+    drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
+    for i in range(len(contours)):
+        color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
+        cv.drawContours(drawing, contours, i, color)
+        cv.drawContours(drawing, hull_list, i, color)
+
+    # Show in a window
+    cv.imshow('Contours', drawing)
+
+# Load source image
+parser = argparse.ArgumentParser(description='Code for Convex Hull tutorial.')
+parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
+args = parser.parse_args()
+
+src = cv.imread(cv.samples.findFile(args.input))
+if src is None:
+    print('Could not open or find the image:', args.input)
+    exit(0)
+
+# Convert image to gray and blur it
+src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
+src_gray = cv.blur(src_gray, (3,3))
+
+# Create Window
+source_window = 'Source'
+cv.namedWindow(source_window)
+cv.imshow(source_window, src)
+max_thresh = 255
+thresh = 100 # initial threshold
+cv.createTrackbar('Canny thresh:', source_window, thresh, max_thresh, thresh_callback)
+thresh_callback(thresh)
+
+cv.waitKey()

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

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+from __future__ import print_function
+from __future__ import division
+import cv2 as cv
+import numpy as np
+import argparse
+import random as rng
+
+rng.seed(12345)
+
+def thresh_callback(val):
+    threshold = val
+
+    ## [Canny]
+    # Detect edges using Canny
+    canny_output = cv.Canny(src_gray, threshold, threshold * 2)
+    ## [Canny]
+
+    ## [findContours]
+    # Find contours
+    contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+    ## [findContours]
+
+    # Get the moments
+    mu = [None]*len(contours)
+    for i in range(len(contours)):
+        mu[i] = cv.moments(contours[i])
+
+    # Get the mass centers
+    mc = [None]*len(contours)
+    for i in range(len(contours)):
+        # add 1e-5 to avoid division by zero
+        mc[i] = (mu[i]['m10'] / (mu[i]['m00'] + 1e-5), mu[i]['m01'] / (mu[i]['m00'] + 1e-5))
+
+    # Draw contours
+    ## [zeroMat]
+    drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
+    ## [zeroMat]
+    ## [forContour]
+    for i in range(len(contours)):
+        color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
+        cv.drawContours(drawing, contours, i, color, 2)
+        cv.circle(drawing, (int(mc[i][0]), int(mc[i][1])), 4, color, -1)
+    ## [forContour]
+
+    ## [showDrawings]
+    # Show in a window
+    cv.imshow('Contours', drawing)
+    ## [showDrawings]
+
+    # Calculate the area with the moments 00 and compare with the result of the OpenCV function
+    for i in range(len(contours)):
+        print(' * Contour[%d] - Area (M_00) = %.2f - Area OpenCV: %.2f - Length: %.2f' % (i, mu[i]['m00'], cv.contourArea(contours[i]), cv.arcLength(contours[i], True)))
+
+## [setup]
+# Load source image
+parser = argparse.ArgumentParser(description='Code for Image Moments tutorial.')
+parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
+args = parser.parse_args()
+
+src = cv.imread(cv.samples.findFile(args.input))
+if src is None:
+    print('Could not open or find the image:', args.input)
+    exit(0)
+
+# Convert image to gray and blur it
+src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
+src_gray = cv.blur(src_gray, (3,3))
+## [setup]
+
+## [createWindow]
+# Create Window
+source_window = 'Source'
+cv.namedWindow(source_window)
+cv.imshow(source_window, src)
+## [createWindow]
+## [trackbar]
+max_thresh = 255
+thresh = 100 # initial threshold
+cv.createTrackbar('Canny Thresh:', source_window, thresh, max_thresh, thresh_callback)
+thresh_callback(thresh)
+## [trackbar]
+
+cv.waitKey()

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

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+from __future__ import print_function
+from __future__ import division
+import cv2 as cv
+import numpy as np
+
+# Create an image
+r = 100
+src = np.zeros((4*r, 4*r), dtype=np.uint8)
+
+# Create a sequence of points to make a contour
+vert = [None]*6
+vert[0] = (3*r//2, int(1.34*r))
+vert[1] = (1*r, 2*r)
+vert[2] = (3*r//2, int(2.866*r))
+vert[3] = (5*r//2, int(2.866*r))
+vert[4] = (3*r, 2*r)
+vert[5] = (5*r//2, int(1.34*r))
+
+# Draw it in src
+for i in range(6):
+    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)
+
+# Calculate the distances to the contour
+raw_dist = np.empty(src.shape, dtype=np.float32)
+for i in range(src.shape[0]):
+    for j in range(src.shape[1]):
+        raw_dist[i,j] = cv.pointPolygonTest(contours[0], (j,i), True)
+
+minVal, maxVal, _, maxDistPt = cv.minMaxLoc(raw_dist)
+minVal = abs(minVal)
+maxVal = abs(maxVal)
+
+# Depicting the  distances graphically
+drawing = np.zeros((src.shape[0], src.shape[1], 3), dtype=np.uint8)
+for i in range(src.shape[0]):
+    for j in range(src.shape[1]):
+        if raw_dist[i,j] < 0:
+            drawing[i,j,0] = 255 - abs(raw_dist[i,j]) * 255 / minVal
+        elif raw_dist[i,j] > 0:
+            drawing[i,j,2] = 255 - raw_dist[i,j] * 255 / maxVal
+        else:
+            drawing[i,j,0] = 255
+            drawing[i,j,1] = 255
+            drawing[i,j,2] = 255
+
+cv.circle(drawing,maxDistPt, int(maxVal),tuple(255,255,255), 1, cv.LINE_8, 0)
+cv.imshow('Source', src)
+cv.imshow('Distance and inscribed circle', drawing)
+cv.waitKey()