def draw_tip_leaf(np_image, coef_x=1, coef_y=1):
leaf_image, leaf_mask = extract_leaf(np_image)
centroid = find_centroid(np_image)
contours = find_external_contours(np_image)
contours = find_good_contours(contours, MIN_CONTOUR_PIXELS)
lengths = []
for contour in contours:
lengths_of_contour = []
for pixel in contour:
point = tuple(pixel[0])
length = math.sqrt((coef_x * (centroid[1] - point[0]))**2 +
(coef_y * (centroid[0] - point[1]))**2)
lengths_of_contour.append(length)
lengths.append(lengths_of_contour)
length_leaves = []
locates = []
for length in lengths:
idx = [i for i in range(len(length))]
f = np.polynomial.chebyshev.Chebyshev.fit(
padding_idx(idx,
len(idx) // 2), padding_length(length,
len(length) // 2),
choose_deg(length))
value, locate = find_extreme(f, idx)
length_leaves += [length[i] for i in locate]
locates.append(locate)
length_average = sum(length_leaves) / len(length_leaves)
tip_locates = get_tip_locates(locates, contours)
draw_redlines(np_image, tip_locates)
def find_green_fixels(np_image):
leaf_image, leaf_mask = extract_leaf(np_image)
green_pixels = []
for i in range(leaf_mask.shape[0]):
for j in range(leaf_mask.shape[1]):
if leaf_mask[i, j] != 0:
green_pixels.append((i, j))
return green_pixels
def find_contours(np_image):
leaf_image, leaf_mask = extract_leaf(np_image)
edges = cv2.Canny(leaf_mask, 100, 1200)
edges_pixels = []
for i in range(edges.shape[0]):
for j in range(edges.shape[1]):
if edges[i, j] != 0:
edges_pixels.append((i, j))
return edges_pixels
def find_external_contours(np_image):
leaf_image, leaf_mask = extract_leaf(np_image)
ret, thresh = cv2.threshold(leaf_mask, 100, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
return contours