def process(self):
intensity = self.intensity_spin.value()
invert = self.invert_check.isChecked()
equalize = self.equalize_check.isChecked()
blue_mode = self.blue_combo.currentIndex()
dx, dy = cv.spatialGradient(cv.cvtColor(self.image, cv.COLOR_BGR2GRAY))
if invert:
dx = -dx
dy = -dy
red = ((dx.astype(np.float32) / np.max(np.abs(dx)) * 127) +
127).astype(np.uint8)
green = ((dy.astype(np.float32) / np.max(np.abs(dy)) * 127) +
127).astype(np.uint8)
if blue_mode == 0:
blue = np.zeros_like(red)
elif blue_mode == 1:
blue = np.full_like(red, 255)
elif blue_mode == 2:
blue = normalize_mat(np.linalg.norm(cv.merge((red, green)),
axis=2))
else:
blue = None
gradient = cv.merge([blue, green, red])
if intensity > 0:
gradient = cv.LUT(gradient, create_lut(intensity, intensity))
if equalize:
gradient = equalize_image(gradient)
self.grad_viewer.update_processed(gradient)
def process(self):
minmax = np.zeros_like(self.image)
minimum = self.min_combo.currentIndex()
maximum = self.max_combo.currentIndex()
radius = self.filter_spin.value()
if radius > 0:
start = time()
radius += 2
if minimum < 4:
low = self.blk_filter(self.low, radius)
if minimum <= 2:
minmax[:, :, 2 - minimum] = low
else:
minmax = np.repeat(low[:, :, np.newaxis], 3, axis=2)
if maximum < 4:
high = self.blk_filter(self.high, radius)
if maximum <= 2:
minmax[:, :, 2 - maximum] += high
else:
minmax += np.repeat(high[:, :, np.newaxis], 3, axis=2)
minmax = normalize_mat(minmax)
self.info_message.emit(
self.tr('Min/Max Filter = {}'.format(elapsed_time(start))))
else:
if minimum == 0:
minmax[self.low] = [0, 0, 255]
elif minimum == 1:
minmax[self.low] = [0, 255, 0]
elif minimum == 2:
minmax[self.low] = [255, 0, 0]
elif minimum == 3:
minmax[self.low] = [255, 255, 255]
if maximum == 0:
minmax[self.high] = [0, 0, 255]
elif maximum == 1:
minmax[self.high] = [0, 255, 0]
elif maximum == 2:
minmax[self.high] = [255, 0, 0]
elif maximum == 3:
minmax[self.high] = [255, 255, 255]
self.viewer.update_processed(minmax)