def _removeSmallerContours(self, image):
cv2.rectangle(image, (0, 0), (640, 480), (0, 0, 0), thickness=1)
contours, _ = cv2.findContours(image.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
bestContour = max(contours, key=cv2.contourArea)
for contour in contours:
if not numpy.array_equal(contour, bestContour):
leftmostX = findLeftmostPoint(contour)[0]
topmostY = findTopmostPoint(contour)[1]
rightmostX = findRightmostPoint(contour)[0]
bottommostY = findBottommostPoint(contour)[1]
cv2.rectangle(image, (leftmostX, topmostY), (rightmostX, bottommostY), (255, 255, 255), thickness=-1)
contours, _ = cv2.findContours(image.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
bestContour = max(contours, key=cv2.contourArea)
for contour in contours:
if not numpy.array_equal(contour, bestContour):
leftmostX = findLeftmostPoint(contour)[0]
topmostY = findTopmostPoint(contour)[1]
rightmostX = findRightmostPoint(contour)[0]
bottommostY = findBottommostPoint(contour)[1]
cv2.rectangle(image, (leftmostX, topmostY), (rightmostX, bottommostY), (0, 0, 0), thickness=-1)
cv2.rectangle(image, (0, 0), (640, 480), (0, 0, 0), thickness=169)
return image
def discriminateContours(self, contours, rgbImage):
bestContours = []
for contour in contours:
bottomPointX, bottomPointY = findBottommostPoint(contour)
pointsBelow = [(bottomPointX, bottomPointY + j * 5) for j in range(1, 5)]
pointBelowContourX, pointBelowContourY = bottomPointX, bottomPointY + 10
for x, y in pointsBelow:
if not all(coordinate < self.BLACK_COLOR_THRESHOLD for coordinate in rgbImage[y, x]):
break
else:
bestContours.append(contour)
return bestContours