def edgeDetection(image):
# Apply OpenCV median filter to denoise the image
image = cv2.medianBlur(image, 3)
# Save and or show images for testing purposes.
cv2.imwrite("edgeDetection_after_medianBlur.jpg", image)
# cv2.imshow('image',image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# Compute imageGradient in x direction
imageX = imageGradientX(image)
# Remove last row from imageX
imageX = imageX[: imageX.shape[0] - 1, :]
imageY = imageGradientY(image)
imageY = imageY[:, : imageY.shape[1] - 1]
image = (imageX + imageY) ** 1.4
cv2.imwrite("edgeDetection_after_adding_gradient_X_Y.jpg", image)
image = convertToBlackAndWhite(image)
cv2.imwrite("edgeDetection_after_convertToBW.jpg", image)
# image = cv2.medianBlur(image, 3)
# cv2.imwrite("edgeDetection_after_medianBlur_Final.jpg", image)
return image
def test_imageGradientX(self):
test_output = imageGradientX(self.testImage)
self.assertEqual(type(test_output), type(self.testImage))
self.assertEqual(test_output.shape[0], self.testImage.shape[0])
self.assertEqual(test_output.shape[1], self.testImage.shape[1] - 1)
print "\n\nSUCCESS: imageGradientX returns the correct output type.\n"
def test_imageGradientX(self):
test_output = imageGradientX(self.testImage)
self.assertEqual(type(test_output), type(self.testImage))
self.assertEqual(test_output.shape[0], self.testImage.shape[0])
self.assertEqual(test_output.shape[1], self.testImage.shape[1] - 1)
print "\n\nSUCCESS: imageGradientX returns the correct output type.\n"
