Python cv2.matchShapes() Examples
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code examples of cv2.matchShapes().
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Example #1
Source File: RegionOfInterest.py From DoNotSnap with GNU General Public License v3.0 | 7 votes |
def findEllipses(edges): contours, _ = cv2.findContours(edges.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) ellipseMask = np.zeros(edges.shape, dtype=np.uint8) contourMask = np.zeros(edges.shape, dtype=np.uint8) pi_4 = np.pi * 4 for i, contour in enumerate(contours): if len(contour) < 5: continue area = cv2.contourArea(contour) if area <= 100: # skip ellipses smaller then 10x10 continue arclen = cv2.arcLength(contour, True) circularity = (pi_4 * area) / (arclen * arclen) ellipse = cv2.fitEllipse(contour) poly = cv2.ellipse2Poly((int(ellipse[0][0]), int(ellipse[0][1])), (int(ellipse[1][0] / 2), int(ellipse[1][1] / 2)), int(ellipse[2]), 0, 360, 5) # if contour is circular enough if circularity > 0.6: cv2.fillPoly(ellipseMask, [poly], 255) continue # if contour has enough similarity to an ellipse similarity = cv2.matchShapes(poly.reshape((poly.shape[0], 1, poly.shape[1])), contour, cv2.cv.CV_CONTOURS_MATCH_I2, 0) if similarity <= 0.2: cv2.fillPoly(contourMask, [poly], 255) return ellipseMask, contourMask
Example #2
Source File: mask_analysis.py From deepgaze with MIT License | 5 votes |
def matchMaxAreaWithShape(self, mask, shape): """it returns a value which identify the similarity between the largest area contour and a shape. The lower the result, the better match it is. It is calculated based on the hu-moment values. For example if we have three shapes: A=star, B=rotated dilatated star, C=square Matching Image A with itself = 0.0 Matching Image A with Image B = 0.001946 Matching Image A with Image C = 0.326911 @param mask the binary image to use in the function @param shape the contour to compare """ cnt = self.returnMaxAreaContour(mask) return cv2.matchShapes(cnt, shape, 1, 0.0)