def top_binarization_hsv(image, configuration):
"""
:param image:
:param configuration:
:return:
"""
configuration.print_value()
# c = configuration.cubicle_domain
# image_cropped = image[c[0]:c[1], c[2]:c[3]]
hsv_image = ocv2.bgr2hsv(image)
# =======================================================================
# Main area segmentation
main_area_seg = cv2.medianBlur(hsv_image, ksize=9)
main_area_seg = cv2.inRange(main_area_seg,
configuration.roi_main.hsv_min,
configuration.roi_main.hsv_max)
main_area_seg = ocv2.dilate(main_area_seg, iterations=2)
main_area_seg = ocv2.erode(main_area_seg, iterations=2)
#
# mask_cropped = configuration.roi_main.mask[c[0]:c[1], c[2]:c[3]]
# main_area_seg = cv2.bitwise_and(main_area_seg,
# main_area_seg,
# mask=mask_cropped)
# image_out = numpy.zeros([image.shape[0], image.shape[1]], 'uint8')
# image_out[c[0]:c[1], c[2]:c[3]] = main_area_seg
return main_area_seg
def side_binarization_hsv(image, configuration):
"""
Binarization of side image for Lemnatech cabin based on hsv segmentation.
Based on Michael pipeline
:param image: BGR image
:param configuration: Object BinarizationConfig
:return: Binary image
"""
# elementMorph = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
c = configuration.cubicle_domain
image_cropped = image[c[0]:c[1], c[2]:c[3]]
hsv_image = ocv2.bgr2hsv(image_cropped)
# =======================================================================
# Main area segmentation
main_area_seg = cv2.medianBlur(hsv_image, ksize=9)
main_area_seg = cv2.inRange(main_area_seg,
configuration.roi_main.hsv_min,
configuration.roi_main.hsv_max)
main_area_seg = ocv2.dilate(main_area_seg, iterations=2)
main_area_seg = ocv2.erode(main_area_seg, iterations=2)
mask_cropped = configuration.roi_main.mask[c[0]:c[1], c[2]:c[3]]
main_area_seg = cv2.bitwise_and(main_area_seg,
main_area_seg,
mask=mask_cropped)
# =======================================================================
# Band area segmentation
background_cropped = configuration.background[c[0]:c[1], c[2]:c[3]]
hsv_background = ocv2.bgr2hsv(background_cropped)
grayscale_background = hsv_background[:, :, 0]
grayscale_image = hsv_image[:, :, 0]
band_area_seg = cv2.subtract(grayscale_image, grayscale_background)
retval, band_area_seg = cv2.threshold(band_area_seg,
122,
255,
cv2.THRESH_BINARY)
mask_cropped = configuration.roi_orange_band.mask[c[0]:c[1], c[2]:c[3]]
band_area_seg = cv2.bitwise_and(band_area_seg,
band_area_seg,
mask=mask_cropped)
band_area_seg = ocv2.erode(band_area_seg, iterations=5)
band_area_seg = ocv2.dilate(band_area_seg, iterations=5)
# =======================================================================
# Pot area segmentation
pot_area_seg = cv2.inRange(hsv_image,
configuration.roi_pot.hsv_min,
configuration.roi_pot.hsv_max)
mask_cropped = configuration.roi_pot.mask[c[0]:c[1], c[2]:c[3]]
pot_area_seg = cv2.bitwise_and(pot_area_seg,
pot_area_seg,
mask=mask_cropped)
# =======================================================================
# Full segmented image
image_seg = cv2.add(main_area_seg, band_area_seg)
image_seg = cv2.add(image_seg, pot_area_seg)
image_out = numpy.zeros([image.shape[0], image.shape[1]], 'uint8')
image_out[c[0]:c[1], c[2]:c[3]] = image_seg
return image_out
