Numpy gradient() with OpenCV is never negative

Question

I've been playing around with OpenCV and Numpy and I just noticed that when taking the gradient of a grayscale image, it is never negative. I haven't tried it with colour. Why is this happening?

import cv2
import numpy as np

video_capture = cv2.VideoCapture(0)
ret, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

Gx,Gy = np.gradient(gray)
print "Gradient X"
print Gx[Gx<0]
print "\n\nGradient Y"
print Gy[Gy<0]

test_gx,test_gy = np.gradient(np.random.rand(10,10))
print "\n\nRandom Gradient X"
print test_gx[test_gx<0]
print "\n\nRandom Gradient Y"
print test_gy[test_gy<0]

Output:

Gradient X
[]


Gradient Y
[]


Random Gradient X
[-0.29390267 -0.57673461 -0.44496392 -0.18760622 -0.37758506 -0.02940484
 -0.09905821 -0.12909104 -0.22726427 -0.17175216 -0.08635539 -0.02969735
 -0.05939525 -0.02111877 -0.29544231 -0.00770492 -0.31914318 -0.12239945
 -0.30133711 -0.08622408 -0.04524624 -0.03998993 -0.40993412 -0.13088891
 -0.2491598  -0.14143661 -0.04846196 -0.30055182 -0.00323793 -0.49329475
 -0.07413882 -0.17564328 -0.13582564 -0.13390455 -0.07373904 -0.09886662
 -0.08773134 -0.06185525 -0.00729722 -0.18979578 -0.17536514 -0.25615883
 -0.26232646 -0.05403582 -0.05968006 -0.26843946 -0.26621363 -0.22504563
 -0.26470668 -0.02397445 -0.0782202  -0.0476783  -0.13333021]


Random Gradient Y
[-0.29521569 -0.23485359 -0.15549854 -0.00142858 -0.07242038 -0.32181099
 -0.26111095 -0.10534067 -0.20442231 -0.05366269 -0.01339253 -0.01597691
 -0.10289234 -0.14128584 -0.1705936  -0.14574768 -0.17571418 -0.04868263
 -0.46254485 -0.11305848 -0.208527   -0.03967778 -0.06671698 -0.35017431
 -0.68122837 -0.37782762 -0.30486289 -0.23501836 -0.25857174 -0.33494929
 -0.27348378 -0.319753   -0.06541161 -0.29203723 -0.1875851  -0.07090711
 -0.07814288 -0.20096383 -0.31743231 -0.17801282 -0.02341537 -0.11358367
 -0.3985152  -0.07670008 -0.02248808 -0.35775219 -0.28470273]

Answer 1

By default, the image from the camera in an unsigned integer, so it cannot have negative values. Try doing this instead:

Gx,Gy = np.gradient(gray.astype('float32'))