def create_average_overlay(masks):
binary = create_average_mask(masks)
# mask contours
contours_layer = (
np.zeros((binary.shape[0], binary.shape[1], 3), dtype=np.uint8) + 255
)
for mask in masks:
m = mask * 255
contour = get_carrot_contour(m)
# x offset: difference in length
offset_x = binary.shape[1] - mask.shape[1]
# y offset: difference in height
offset_y = int((binary.shape[0] - mask.shape[0]) / 2)
cv2.drawContours(
contours_layer, [contour], -1, (0, 0, 0), 2, offset=(offset_x, offset_y)
)
# avg contour
contour = get_carrot_contour(binary)
avg_overlay = np.zeros((binary.shape[0], binary.shape[1], 3), dtype=np.uint8) + 255
cv2.drawContours(avg_overlay, [contour], -1, (0, 0, 255), -1)
alpha = 0.7
cv2.addWeighted(avg_overlay, alpha, contours_layer, 1 - alpha, 0, contours_layer)
return contours_layer
def assemble_instance(file):
instance = get_attributes_from_filename(file)
if "kernel" in file:
image_type = "kernel"
elif "in-shell" in file:
image_type = "in-shell"
instance["type"] = image_type
image = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
white_pixels = cv2.countNonZero(image)
instance["white_pixels"] = white_pixels
scale = instance.get("Scale", None)
if scale:
instance["area"] = round(white_pixels / float(scale) ** 2, 3)
else:
instance["area"] = "no scale"
contour = get_carrot_contour(image)
# https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.html
# cnts = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# print(type(cnts))
# c = max(cnts, key=cv2.contourArea)
# color_image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
# cv2.drawContours(color_image, [contour], -1, (0, 0, 255), 3)
x, y, w, h = cv2.boundingRect(contour)
# color_image = cv2.rectangle(color_image, (x, y), (x + w, y + h), (0, 255, 0), 2)
instance["width"] = w
instance["height"] = h
# https://stackoverflow.com/questions/31281235/anomaly-with-ellipse-fitting-when-using-cv2-ellipse-with-different-parameters
# https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_gui/py_drawing_functions/py_drawing_functions.html#drawing-ellipse
ellipse = cv2.fitEllipse(contour)
ellipse_major_axis, ellipse_minor_axis = ellipse[1]
ellipse_angle = ellipse[2]
instance["ellipse_major_axis"] = round(ellipse_major_axis, 2)
instance["ellipse_minor_axis"] = round(ellipse_minor_axis, 2)
instance["ellipse_angle"] = round(ellipse_angle, 2)
# cv2.ellipse(color_image, ellipse, (0, 255, 0), 2)
# print(ellipse[1], ellipse[2])
rect = cv2.minAreaRect(contour)
_, width_height, _ = rect
min_area_width, min_area_height = width_height
instance["min_area_width"] = round(min_area_width, 2)
instance["min_area_height"] = round(min_area_height, 2)
# print(min_area_width, min_area_height)
# box = cv2.boxPoints(rect)
# box = np.int0(box)
# cv2.drawContours(color_image, [box], 0, (255, 0, 0), 2)
# print(len(cnts))
# cv2.imwrite("/Users/creimers/Downloads/glibba.png", color_image)
return instance
def create_nut_mask(nut_image, masking_method):
"""
create a binary mask of the nut
"""
output = np.zeros(nut_image.shape, dtype=np.uint8)
mask = masking_method(nut_image)
contour = get_carrot_contour(mask)
if contour is not None and contour.shape[0] > 100:
cv2.drawContours(output, [contour], -1, (255, 255, 255), -1)
return output
def create_nut_mask_overlay(nut_image, masking_method):
overlay = nut_image.copy()
output = nut_image.copy()
mask = masking_method(nut_image)
contour = get_carrot_contour(mask)
overlay_color = (0, 255, 0)
alpha = 0.5
cv2.drawContours(overlay, [contour], -1, overlay_color, -1)
cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output)
return output
def create_ellipse_overlay(nut_image, masking_method):
# https://docs.opencv.org/master/dd/d49/tutorial_py_contour_features.html
image = nut_image.copy()
mask = masking_method(nut_image)
contour = get_carrot_contour(mask)
if contour is not None:
ellipse = cv2.fitEllipse(contour)
cv2.ellipse(image, ellipse, (0, 255, 0), 2)
_, cols = image.shape[:2]
[vx, vy, x, y] = cv2.fitLine(contour, cv2.DIST_L2, 0, 0.01, 0.01)
lefty = int((-x * vy / vx) + y)
righty = int(((cols - x) * vy / vx) + y)
cv2.line(image, (cols - 1, righty), (0, lefty), (0, 0, 255), 2)
return image