def erase_specular(self, eye_img, debug=False):
# Rather arbitrary decision on how large a specularity may be
max_specular_contour_area = sum(eye_img.shape[:2]) / 2
# Extract top 50% of intensities
eye_img_grey = cv2.cvtColor(eye_img, cv2.COLOR_BGR2GRAY)
eye_img_grey_blur = cv2.GaussianBlur(eye_img_grey, (5, 5), 0)
# Close to suppress eyelashes
morph_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
eye_img_grey_blur = cv2.morphologyEx(eye_img_grey_blur,
cv2.MORPH_CLOSE, morph_kernel)
if eye_img_grey_blur is None:
raise eye_extractor.NoEyesFound()
thresh_val = int(np.percentile(eye_img_grey_blur, 50))
_, thresh_img = cv2.threshold(eye_img_grey_blur, thresh_val, 255,
cv2.THRESH_BINARY)
# Find all contours and throw away the big ones
contours, _ = cv2.findContours(np.copy(thresh_img), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
small_contours = filter(
lambda x: cv2.contourArea(x) < max_specular_contour_area, contours)
small_contours_mask = np.zeros_like(eye_img_grey)
cv2.drawContours(small_contours_mask, small_contours, -1, 255, -1)
# Dilate the smallest contours found
small_contours_mask_dilated = cv2.dilate(small_contours_mask,
morph_kernel)
removed_specular_img = cv2.inpaint(eye_img,
small_contours_mask_dilated,
2,
flags=cv2.INPAINT_TELEA)
if debug:
thresh_hierarchy = cv2.cvtColor(eye_img_grey, cv2.COLOR_GRAY2BGR)
cv2.drawContours(thresh_hierarchy, contours, -1, (0, 0, 255), -1)
thresh_hierarchy = cv2.add(
thresh_hierarchy,
cv2.cvtColor(small_contours_mask_dilated, cv2.COLOR_GRAY2BGR))
cv2.drawContours(thresh_hierarchy, small_contours, -1, (255, 0, 0),
-1)
stacked_imgs = np.concatenate(
[eye_img, thresh_hierarchy, removed_specular_img], axis=1)
if debug == 1:
self.full_debug_img = stacked_imgs
elif debug == 2:
self.full_debug_img = image_utils.stack_imgs_vertical(
[self.full_debug_img, stacked_imgs])
cv2.imshow(winname, self.full_debug_img)
elif debug == 3:
cv2.imshow(winname, stacked_imgs)
return removed_specular_img
def find_pupil(
eye_img_bgr, fast_width_grads=25.5, fast_width_iso=80, weight_grads=0.9, weight_iso=0.1, debug_index=False
):
eye_img_r = cv2.split(eye_img_bgr)[2]
fast_size_grads = (int((fast_width_grads / eye_img_bgr.shape[0]) * eye_img_bgr.shape[1]), int(fast_width_grads))
fast_img_grads = cv2.resize(eye_img_r, fast_size_grads)
fast_size_iso = (int(fast_width_iso), int((fast_width_iso / eye_img_r.shape[1]) * eye_img_r.shape[0]))
fast_img_iso = cv2.resize(eye_img_r, fast_size_iso)
c_map_grads = eye_center_locator_gradients.get_center_map(fast_img_grads)
c_map_iso = eye_center_locator_isophote.get_center_map(fast_img_iso)
c_map_norm_grads = cv2.normalize(c_map_grads, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
c_map_big_grads = cv2.resize(c_map_norm_grads, (eye_img_bgr.shape[1], eye_img_bgr.shape[0])).astype(np.uint8)
c_map_norm_iso = cv2.normalize(c_map_iso, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
c_map_big_iso = cv2.resize(c_map_norm_iso, (eye_img_bgr.shape[1], eye_img_bgr.shape[0])).astype(np.uint8)
joint_c_map = cv2.addWeighted(c_map_big_grads, w_grads, c_map_big_iso, w_iso, 1.0)
max_val_index = np.argmax(joint_c_map)
pupil_y0, pupil_x0 = max_val_index // joint_c_map.shape[1], max_val_index % joint_c_map.shape[1]
max_val_index_2 = np.argmax(c_map_big_grads)
pupil_y0_2, pupil_x0_2 = max_val_index_2 // joint_c_map.shape[1], max_val_index_2 % joint_c_map.shape[1]
max_val_index_3 = np.argmax(c_map_big_iso)
pupil_y0_3, pupil_x0_3 = max_val_index_3 // joint_c_map.shape[1], max_val_index_3 % joint_c_map.shape[1]
if debug_index:
debug_img = eye_img_bgr.copy()
joint_c_map = cv2.cvtColor(joint_c_map, cv2.COLOR_GRAY2BGR)
c_map_big_iso = cv2.cvtColor(c_map_big_iso, cv2.COLOR_GRAY2BGR)
c_map_big_grads = cv2.cvtColor(c_map_big_grads, cv2.COLOR_GRAY2BGR)
draw_utils.draw_cross(debug_img, (pupil_x0, pupil_y0), (0, 255, 255), 16, 2)
draw_utils.draw_cross(joint_c_map, (pupil_x0_3, pupil_y0_3), (255, 0, 255), 8, 2)
draw_utils.draw_cross(joint_c_map, (pupil_x0_2, pupil_y0_2), (255, 0, 255), 8, 2)
draw_utils.draw_cross(joint_c_map, (pupil_x0, pupil_y0), (255, 0, 0), 16, 2)
draw_utils.draw_cross(c_map_big_iso, (pupil_x0_3, pupil_y0_3), (255, 0, 0), 16, 2)
draw_utils.draw_cross(c_map_big_grads, (pupil_x0_2, pupil_y0_2), (255, 0, 0), 16, 2)
stacked_imgs = image_utils.stack_imgs_horizontal([debug_img, c_map_big_grads, c_map_big_iso, joint_c_map])
__debug_imgs[debug_index] = stacked_imgs
if debug_index == 2:
full_debug_img = image_utils.stack_imgs_vertical([__debug_imgs[1], __debug_imgs[2]])
cv2.imshow(__winname, full_debug_img)
elif debug_index > 2:
cv2.imshow(__winname, stacked_imgs)
return pupil_x0, pupil_y0
def find_pupil(eye_img_bgr, debug_index=False):
eye_img_r = cv2.cvtColor(eye_img_bgr, cv2.COLOR_BGR2GRAY)
# Scale to small image for faster computation
scale = __fast_width / eye_img_r.shape[1]
small_size = (int(__fast_width),
int((__fast_width / eye_img_r.shape[1]) *
eye_img_r.shape[0]))
eye_img_small = cv2.resize(eye_img_r, small_size)
eye_img_small = cv2.GaussianBlur(eye_img_small, (3, 3), 0)
center_map = get_center_map(eye_img_small)
max_val_index = np.argmax(center_map)
pupil_y0, pupil_x0 = max_val_index // center_map.shape[
1], max_val_index % center_map.shape[1]
# Scale back to original coordinates
pupil_y0, pupil_x0 = int((pupil_y0 + 0.5) / scale), int(
(pupil_x0 + 0.5) / scale)
if debug_index:
eye_img_r_debug = cv2.cvtColor(eye_img_r, cv2.COLOR_GRAY2BGR)
debug_img = eye_img_bgr.copy()
cmap_norm = cv2.normalize(center_map,
0,
255,
norm_type=cv2.NORM_MINMAX).astype(np.uint8)
center_map_big = cv2.resize(
cmap_norm,
(eye_img_r.shape[1], eye_img_r.shape[0])).astype(np.uint8)
center_map_big = cv2.cvtColor(center_map_big, cv2.COLOR_GRAY2BGR)
overlay_img = cv2.addWeighted(center_map_big, 0.9, eye_img_r_debug,
0.1, 1)
draw_utils.draw_cross(debug_img, (pupil_x0, pupil_y0), (0, 255, 255),
6)
draw_utils.draw_cross(overlay_img, (pupil_x0, pupil_y0), (255, 0, 0),
6)
# stacked_small_size = image_utils.stack_imgs_vertical([eye_img_small, cmap_norm])
stacked_imgs = image_utils.stack_imgs_horizontal(
[debug_img, overlay_img])
__debug_imgs[debug_index] = stacked_imgs
if debug_index == 2:
full_debug_img = image_utils.stack_imgs_vertical(
[__debug_imgs[1], __debug_imgs[2]])
cv2.imshow(__winname, full_debug_img)
elif debug_index > 2:
cv2.imshow(__winname, stacked_imgs)
return pupil_x0, pupil_y0
def find_eyelids(eye_img, debug_index):
u_eyelid = find_upper_eyelid(eye_img, debug_index)
l_eyelid = find_lower_eyelid(eye_img, debug_index)
if debug_index == 2:
debug_img_1 = stack_imgs_horizontal([__debug_imgs_upper[1], __debug_imgs_lower[1]])
debug_img_2 = stack_imgs_horizontal([__debug_imgs_upper[2], __debug_imgs_lower[2]])
full_debug_img = stack_imgs_vertical([debug_img_1, debug_img_2])
cv2.imshow(__winname, full_debug_img)
return u_eyelid, l_eyelid
def find_pupil(eye_img_bgr, debug_index=False):
""" Estimates the centre of the pupil using image gradients
"""
eye_img_r = cv2.split(eye_img_bgr)[2] # Extract red channel only
# Scale to small image for faster computation
scale = __fast_width / eye_img_bgr.shape[0]
small_size = (int(
(__fast_width / eye_img_bgr.shape[0]) * eye_img_bgr.shape[1]),
int(__fast_width))
eye_img_small = cv2.resize(eye_img_r, small_size)
center_map = get_center_map(eye_img_small)
max_val_index = np.argmax(center_map)
pupil_y0, pupil_x0 = max_val_index // center_map.shape[
1], max_val_index % center_map.shape[1]
# Scale back to original coordinates
pupil_y0, pupil_x0 = int((pupil_y0 + 0.5) / scale), int(
(pupil_x0 + 0.5) / scale)
if debug_index:
eye_img_r_debug = cv2.cvtColor(eye_img_r, cv2.COLOR_GRAY2BGR)
debug_img = eye_img_bgr.copy()
cmap_norm = cv2.normalize(center_map,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX)
center_map_big = cv2.resize(
cmap_norm, (eye_img_bgr.shape[1], eye_img_bgr.shape[0]))
center_map_big = cv2.cvtColor(center_map_big.astype(np.uint8),
cv2.COLOR_GRAY2BGR)
draw_utils.draw_cross(debug_img, (pupil_x0, pupil_y0), (0, 255, 255),
6)
draw_utils.draw_cross(center_map_big, (pupil_x0, pupil_y0),
(255, 0, 0), 6)
stacked_imgs = image_utils.stack_imgs_horizontal(
[debug_img, eye_img_r_debug, center_map_big])
__debug_imgs[debug_index] = stacked_imgs
if debug_index == 2:
full_debug_img = image_utils.stack_imgs_vertical(
[__debug_imgs[1], __debug_imgs[2]])
cv2.imshow(__winname, full_debug_img)
elif debug_index > 2:
cv2.imshow(__winname, stacked_imgs)
return pupil_x0, pupil_y0
def find_limbus_edge_pts(self, eye_roi, debug=False):
blurred_eye_roi_img = cv2.GaussianBlur(eye_roi.img, (5, 5), 5)
pupil_x0, pupil_y0 = eye_roi.img.shape[1] / 2, eye_roi.img.shape[0] / 2
min_limb_r = int(eye_roi.img.shape[0] * self.limb_r_ratios[0])
max_limb_r = int(eye_roi.img.shape[0] * self.limb_r_ratios[1])
pts_found = set()
pts_found = pts_found.union(
self.cast_rays_spread(bgr_img=blurred_eye_roi_img,
start_pos=(pupil_x0, pupil_y0),
angle_mean=0,
spread=120,
limb_r_range=(min_limb_r, max_limb_r)))
pts_found = pts_found.union(
self.cast_rays_spread(bgr_img=blurred_eye_roi_img,
start_pos=(pupil_x0, pupil_y0),
angle_mean=180,
spread=120,
limb_r_range=(min_limb_r, max_limb_r)))
if debug:
debug_img = blurred_eye_roi_img.copy()
cv2.circle(blurred_eye_roi_img,
(eye_roi.img.shape[1] / 2, eye_roi.img.shape[0] / 2),
min_limb_r, (255, 255, 0))
cv2.circle(blurred_eye_roi_img,
(eye_roi.img.shape[1] / 2, eye_roi.img.shape[0] / 2),
max_limb_r, (255, 255, 0))
draw_cross(debug_img, (pupil_x0, pupil_y0),
color=(255, 255, 0),
width=6)
draw_points(debug_img,
pts_found, (0, 0, 255),
width=1,
thickness=2)
stacked_imgs = np.concatenate(
[eye_roi.img, blurred_eye_roi_img, debug_img], axis=1)
if debug == 1:
self.full_debug_img = stacked_imgs
elif debug == 2:
self.full_debug_img = stack_imgs_vertical(
[self.full_debug_img, stacked_imgs])
cv2.imshow(winname, self.full_debug_img)
elif debug == 3:
cv2.imshow(winname, stacked_imgs)
return pts_found
def find_eyelids(eye_img, debug_index):
u_eyelid = find_upper_eyelid(eye_img, debug_index)
l_eyelid = find_lower_eyelid(eye_img, debug_index)
if debug_index == 2:
debug_img_1 = stack_imgs_horizontal(
[__debug_imgs_upper[1], __debug_imgs_lower[1]])
debug_img_2 = stack_imgs_horizontal(
[__debug_imgs_upper[2], __debug_imgs_lower[2]])
full_debug_img = stack_imgs_vertical([debug_img_1, debug_img_2])
cv2.imshow(__winname, full_debug_img)
return u_eyelid, l_eyelid
def erase_specular(self, eye_img, debug=False):
# Rather arbitrary decision on how large a specularity may be
max_specular_contour_area = sum(eye_img.shape[:2])/2
# Extract top 50% of intensities
eye_img_grey = cv2.cvtColor(eye_img, cv2.COLOR_BGR2GRAY)
eye_img_grey_blur = cv2.GaussianBlur(eye_img_grey, (5, 5), 0)
# Close to suppress eyelashes
morph_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
eye_img_grey_blur = cv2.morphologyEx(eye_img_grey_blur, cv2.MORPH_CLOSE, morph_kernel)
if eye_img_grey_blur is None:
raise eye_extractor.NoEyesFound()
thresh_val = int(np.percentile(eye_img_grey_blur, 50))
_, thresh_img = cv2.threshold(eye_img_grey_blur, thresh_val, 255, cv2.THRESH_BINARY)
# Find all contours and throw away the big ones
contours, _ = cv2.findContours(np.copy(thresh_img), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
small_contours = filter(lambda x : cv2.contourArea(x) < max_specular_contour_area, contours)
small_contours_mask = np.zeros_like(eye_img_grey)
cv2.drawContours(small_contours_mask, small_contours, -1, 255, -1)
# Dilate the smallest contours found
small_contours_mask_dilated = cv2.dilate(small_contours_mask, morph_kernel)
removed_specular_img = cv2.inpaint(eye_img, small_contours_mask_dilated, 2, flags=cv2.INPAINT_TELEA)
if debug:
thresh_hierarchy = cv2.cvtColor(eye_img_grey, cv2.COLOR_GRAY2BGR)
cv2.drawContours(thresh_hierarchy, contours, -1, (0, 0, 255), -1)
thresh_hierarchy = cv2.add(thresh_hierarchy, cv2.cvtColor(small_contours_mask_dilated, cv2.COLOR_GRAY2BGR))
cv2.drawContours(thresh_hierarchy, small_contours, -1, (255, 0, 0), -1)
stacked_imgs = np.concatenate([eye_img, thresh_hierarchy, removed_specular_img],axis=1)
if debug == 1:
self.full_debug_img = stacked_imgs
elif debug == 2:
self.full_debug_img = image_utils.stack_imgs_vertical([self.full_debug_img, stacked_imgs])
cv2.imshow(winname, self.full_debug_img)
elif debug == 3:
cv2.imshow(winname, stacked_imgs);
return removed_specular_img
def find_pupil(eye_img_bgr, debug_index=False):
eye_img_r = cv2.cvtColor(eye_img_bgr, cv2.COLOR_BGR2GRAY)
# Scale to small image for faster computation
scale = __fast_width / eye_img_r.shape[1]
small_size = (int(__fast_width), int((__fast_width / eye_img_r.shape[1]) * eye_img_r.shape[0]))
eye_img_small = cv2.resize(eye_img_r, small_size)
eye_img_small = cv2.GaussianBlur(eye_img_small, (3, 3), 0)
center_map = get_center_map(eye_img_small)
max_val_index = np.argmax(center_map)
pupil_y0, pupil_x0 = max_val_index // center_map.shape[1], max_val_index % center_map.shape[1]
# Scale back to original coordinates
pupil_y0, pupil_x0 = int((pupil_y0 + 0.5) / scale), int((pupil_x0 + 0.5) / scale)
if debug_index:
eye_img_r_debug = cv2.cvtColor(eye_img_r, cv2.COLOR_GRAY2BGR)
debug_img = eye_img_bgr.copy()
cmap_norm = cv2.normalize(center_map, 0, 255, norm_type=cv2.NORM_MINMAX).astype(np.uint8)
center_map_big = cv2.resize(cmap_norm, (eye_img_r.shape[1], eye_img_r.shape[0])).astype(np.uint8)
center_map_big = cv2.cvtColor(center_map_big, cv2.COLOR_GRAY2BGR)
overlay_img = cv2.addWeighted(center_map_big, 0.9, eye_img_r_debug, 0.1, 1)
draw_utils.draw_cross(debug_img, (pupil_x0, pupil_y0), (0, 255, 255), 6)
draw_utils.draw_cross(overlay_img, (pupil_x0, pupil_y0), (255, 0, 0), 6)
# stacked_small_size = image_utils.stack_imgs_vertical([eye_img_small, cmap_norm])
stacked_imgs = image_utils.stack_imgs_horizontal([debug_img, overlay_img])
__debug_imgs[debug_index] = stacked_imgs
if debug_index == 2:
full_debug_img = image_utils.stack_imgs_vertical([__debug_imgs[1], __debug_imgs[2]]);
cv2.imshow(__winname, full_debug_img)
elif debug_index > 2:
cv2.imshow(__winname, stacked_imgs);
return pupil_x0, pupil_y0
def find_pupil(eye_img_bgr, debug_index=False):
""" Estimates the centre of the pupil using image gradients
"""
eye_img_r = cv2.split(eye_img_bgr)[2] # Extract red channel only
# Scale to small image for faster computation
scale = __fast_width / eye_img_bgr.shape[0]
small_size = (int((__fast_width / eye_img_bgr.shape[0]) * eye_img_bgr.shape[1]), int(__fast_width))
eye_img_small = cv2.resize(eye_img_r, small_size)
center_map = get_center_map(eye_img_small)
max_val_index = np.argmax(center_map)
pupil_y0, pupil_x0 = max_val_index // center_map.shape[1], max_val_index % center_map.shape[1]
# Scale back to original coordinates
pupil_y0, pupil_x0 = int((pupil_y0 + 0.5) / scale), int((pupil_x0 + 0.5) / scale)
if debug_index:
eye_img_r_debug = cv2.cvtColor(eye_img_r, cv2.COLOR_GRAY2BGR)
debug_img = eye_img_bgr.copy()
cmap_norm = cv2.normalize(center_map, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
center_map_big = cv2.resize(cmap_norm, (eye_img_bgr.shape[1], eye_img_bgr.shape[0]))
center_map_big = cv2.cvtColor(center_map_big.astype(np.uint8), cv2.COLOR_GRAY2BGR)
draw_utils.draw_cross(debug_img, (pupil_x0, pupil_y0), (0, 255, 255), 6)
draw_utils.draw_cross(center_map_big, (pupil_x0, pupil_y0), (255, 0, 0), 6)
stacked_imgs = image_utils.stack_imgs_horizontal([debug_img, eye_img_r_debug, center_map_big])
__debug_imgs[debug_index] = stacked_imgs
if debug_index == 2:
full_debug_img = image_utils.stack_imgs_vertical([__debug_imgs[1], __debug_imgs[2]]);
cv2.imshow(__winname, full_debug_img)
elif debug_index > 2:
cv2.imshow(__winname, stacked_imgs);
return pupil_x0, pupil_y0
def find_limbus_edge_pts(self, eye_roi, debug=False):
blurred_eye_roi_img = cv2.GaussianBlur(eye_roi.img, (5, 5), 5)
pupil_x0, pupil_y0 = eye_roi.img.shape[1] / 2, eye_roi.img.shape[0] / 2
min_limb_r = int(eye_roi.img.shape[0] * self.limb_r_ratios[0])
max_limb_r = int(eye_roi.img.shape[0] * self.limb_r_ratios[1])
pts_found = set()
pts_found = pts_found.union(self.cast_rays_spread(bgr_img=blurred_eye_roi_img,
start_pos=(pupil_x0, pupil_y0),
angle_mean=0, spread=120,
limb_r_range=(min_limb_r, max_limb_r)))
pts_found = pts_found.union(self.cast_rays_spread(bgr_img=blurred_eye_roi_img,
start_pos=(pupil_x0, pupil_y0),
angle_mean=180, spread=120,
limb_r_range=(min_limb_r, max_limb_r)))
if debug:
debug_img = blurred_eye_roi_img.copy()
cv2.circle(blurred_eye_roi_img, (eye_roi.img.shape[1] / 2, eye_roi.img.shape[0] / 2), min_limb_r, (255, 255, 0))
cv2.circle(blurred_eye_roi_img, (eye_roi.img.shape[1] / 2, eye_roi.img.shape[0] / 2), max_limb_r, (255, 255, 0))
draw_cross(debug_img, (pupil_x0, pupil_y0), color=(255, 255, 0), width=6)
draw_points(debug_img, pts_found, (0, 0, 255), width=1, thickness=2)
stacked_imgs = np.concatenate([eye_roi.img, blurred_eye_roi_img, debug_img], axis=1)
if debug == 1:
self.full_debug_img = stacked_imgs
elif debug == 2:
self.full_debug_img = stack_imgs_vertical([self.full_debug_img, stacked_imgs])
cv2.imshow(winname, self.full_debug_img)
elif debug == 3:
cv2.imshow(winname, stacked_imgs);
return pts_found
def get_limb_pts(eye_img, phi=20, angle_step=1, debug_index=False):
polar_img_w = 360 / angle_step # Polar image has one column per angle of interest
phi_range_1 = ((90 - phi) / angle_step, (90 + phi) / angle_step
) # Ranges of angles to be ignored (too close to lids)
phi_range_2 = ((270 - phi) / angle_step, (270 + phi) / angle_step)
eye_img_grey = cv2.cvtColor(eye_img, cv2.COLOR_BGR2GRAY) # Do BGR-grey
eye_img_grey = cv2.medianBlur(eye_img_grey, 5)
# Scale to fixed size image for re-using transform matrix
scale = eye_img.shape[0] / float(__fixed_width)
img_fixed_size = cv2.resize(eye_img_grey, (__fixed_width, __fixed_width))
# Transform image into polar coords and blur
img_polar = linpolar(img_fixed_size,
trans_w=polar_img_w,
trans_h=__fixed_width / 2)
img_polar = cv2.GaussianBlur(img_polar, (5, 5), 0)
# Take the segment between min & max radii and filter with Gabor kernel
img_polar_seg = img_polar[__min_limb_r:__max_limb_r, :]
filter_img = cv2.filter2D(img_polar_seg, -1, __gabor_kern)
# Black out ignored angles
filter_img.T[phi_range_1[0]:phi_range_1[1]] = 0
filter_img.T[phi_range_2[0]:phi_range_2[1]] = 0
# In polar image, x <-> theta, y <-> magnitude
pol_ys = np.argmax(filter_img,
axis=0) # Take highest filter response as limbus points
pol_xs = np.arange(filter_img.shape[1])[pol_ys > 0]
mags = (pol_ys + __min_limb_r)[pol_ys > 0]
thts = np.radians(pol_xs * angle_step)
# Translate each point back into fixed img coords
xs, ys = cv2.polarToCart(mags.astype(float), thts)
xs = (
xs + __fixed_width / 2
) * scale # Shift and scale cart. coords back to original eye-ROI coords
ys = (ys + __fixed_width / 2) * scale
# Points returned in form
# [[ x1 y1]
# [ x2 y2]
# ...
# [ xn yn]]
pts_cart = np.concatenate([xs, ys], axis=1)
# --------------------- Debug Drawing ---------------------
if debug_index != False:
debug_img = eye_img.copy()
debug_polar = cv2.cvtColor(img_polar, cv2.COLOR_GRAY2BGR)
cv2.imwrite("polar.jpg", debug_polar)
cv2.line(debug_polar, (0, __min_limb_r),
(img_polar.shape[1], __min_limb_r), (255, 255, 0))
cv2.line(debug_polar, (0, __max_limb_r),
(img_polar.shape[1], __max_limb_r), (255, 255, 0))
cv2.circle(debug_img, (debug_img.shape[1] / 2, debug_img.shape[0] / 2),
int(debug_img.shape[0] * __limb_r_ratios[0]), (255, 255, 0))
cv2.circle(debug_img, (debug_img.shape[1] / 2, debug_img.shape[0] / 2),
int(debug_img.shape[0] * __limb_r_ratios[1]), (255, 255, 0))
pts_polar = np.squeeze(np.dstack([pol_xs, mags]))
draw_points(debug_polar, pts_polar, (0, 0, 255), width=1)
draw_points(debug_img, pts_cart, (0, 0, 255), width=1)
stacked_imgs_polar = stack_imgs_vertical([debug_polar, filter_img])
stacked_imgs = stack_imgs_horizontal(
[debug_img, eye_img_grey, stacked_imgs_polar])
__debug_imgs[debug_index] = stacked_imgs
if debug_index == 2:
full_debug_img = stack_imgs_vertical(
[__debug_imgs[1], __debug_imgs[2]])
cv2.imshow(__winname, full_debug_img)
elif debug_index > 2:
cv2.imshow(__winname, stacked_imgs)
# --------------------- Debug Drawing ---------------------
return pts_cart
def get_limb_pts(eye_img, phi=20, angle_step=1, debug_index=False):
polar_img_w = 360 / angle_step # Polar image has one column per angle of interest
phi_range_1 = ((90 - phi) / angle_step, (90 + phi) / angle_step) # Ranges of angles to be ignored (too close to lids)
phi_range_2 = ((270 - phi) / angle_step, (270 + phi) / angle_step)
eye_img_grey = cv2.cvtColor(eye_img, cv2.COLOR_BGR2GRAY) # Do BGR-grey
eye_img_grey = cv2.medianBlur(eye_img_grey, 5)
# Scale to fixed size image for re-using transform matrix
scale = eye_img.shape[0] / float(__fixed_width)
img_fixed_size = cv2.resize(eye_img_grey, (__fixed_width, __fixed_width))
# Transform image into polar coords and blur
img_polar = linpolar(img_fixed_size, trans_w=polar_img_w, trans_h=__fixed_width / 2)
img_polar = cv2.GaussianBlur(img_polar, (5, 5), 0)
# Take the segment between min & max radii and filter with Gabor kernel
img_polar_seg = img_polar[__min_limb_r:__max_limb_r, :]
filter_img = cv2.filter2D(img_polar_seg, -1, __gabor_kern)
# Black out ignored angles
filter_img.T[ phi_range_1[0] : phi_range_1[1] ] = 0
filter_img.T[ phi_range_2[0] : phi_range_2[1] ] = 0
# In polar image, x <-> theta, y <-> magnitude
pol_ys = np.argmax(filter_img, axis=0) # Take highest filter response as limbus points
pol_xs = np.arange(filter_img.shape[1])[pol_ys > 0]
mags = (pol_ys + __min_limb_r)[pol_ys > 0]
thts = np.radians(pol_xs * angle_step)
# Translate each point back into fixed img coords
xs, ys = cv2.polarToCart(mags.astype(float), thts)
xs = (xs + __fixed_width / 2) * scale # Shift and scale cart. coords back to original eye-ROI coords
ys = (ys + __fixed_width / 2) * scale
# Points returned in form
# [[ x1 y1]
# [ x2 y2]
# ...
# [ xn yn]]
pts_cart = np.concatenate([xs, ys], axis=1)
# --------------------- Debug Drawing ---------------------
if debug_index != False:
debug_img = eye_img.copy()
debug_polar = cv2.cvtColor(img_polar, cv2.COLOR_GRAY2BGR)
cv2.imwrite("polar.jpg",debug_polar)
cv2.line(debug_polar, (0, __min_limb_r), (img_polar.shape[1], __min_limb_r), (255, 255, 0))
cv2.line(debug_polar, (0, __max_limb_r), (img_polar.shape[1], __max_limb_r), (255, 255, 0))
cv2.circle(debug_img, (debug_img.shape[1] / 2, debug_img.shape[0] / 2), int(debug_img.shape[0] * __limb_r_ratios[0]), (255, 255, 0))
cv2.circle(debug_img, (debug_img.shape[1] / 2, debug_img.shape[0] / 2), int(debug_img.shape[0] * __limb_r_ratios[1]), (255, 255, 0))
pts_polar = np.squeeze(np.dstack([pol_xs, mags]))
draw_points(debug_polar, pts_polar, (0, 0, 255), width=1)
draw_points(debug_img, pts_cart, (0, 0, 255), width=1)
stacked_imgs_polar = stack_imgs_vertical([debug_polar, filter_img])
stacked_imgs = stack_imgs_horizontal([debug_img, eye_img_grey, stacked_imgs_polar])
__debug_imgs[debug_index] = stacked_imgs
if debug_index == 2:
full_debug_img = stack_imgs_vertical([__debug_imgs[1], __debug_imgs[2]]);
cv2.imshow(__winname, full_debug_img)
elif debug_index > 2:
cv2.imshow(__winname, stacked_imgs);
# --------------------- Debug Drawing ---------------------
return pts_cart
def find_lower_eyelid(eye_img, debug_index):
line_y_offset = 0 # Amount to shift eye-lid by after detection
img_blue = cv2.split(eye_img)[2]
img_w, img_h = eye_img.shape[:2]
# Indexes to extract window sub-images
w_y1, w_y2 = int(img_h * __l_win_rats_h[0]), int(img_h *
sum(__l_win_rats_h[:2]))
wl_x1, wl_x2 = int(img_w * __l_win_rats_w_l[0]), int(
img_w * sum(__l_win_rats_w_l[:2]))
wr_x1, wr_x2 = int(img_w * __l_win_rats_w_r[0]), int(
img_w * sum(__l_win_rats_w_r[:2]))
# Split image into two halves
window_img_l = img_blue[w_y1:w_y2, wl_x1:wl_x2]
window_img_r = img_blue[w_y1:w_y2, wr_x1:wr_x2]
window_img_l = cv2.GaussianBlur(window_img_l, (5, 5), 20)
window_img_r = cv2.GaussianBlur(window_img_r, (5, 5), 20)
filter_img_l = cv2.filter2D(window_img_l, -1, __gabor_kern_diag)
filter_img_r = cv2.filter2D(window_img_r, -1,
cv2.flip(__gabor_kern_diag, 1))
filter_img = np.concatenate([filter_img_l, filter_img_r], axis=1)
# In polar image, x <-> theta, y <-> magnitude
max_vals = np.max(filter_img, axis=0)
ys = np.argmax(filter_img,
axis=0) # Take highest filter response as limbus points
xs = (np.arange(filter_img.shape[1]) + wl_x1)[max_vals > __min_thresh]
ys = (ys + w_y1)[max_vals > __min_thresh]
l_lid_pts = np.squeeze(np.dstack([xs, ys]), axis=0)
# Only RANSAC fit eyelid if there are enough points
if l_lid_pts.size < __min_num_pts_u * 2:
eyelid_lower_line = None
else:
eyelid_lower_line = ransac_line(l_lid_pts)
if eyelid_lower_line is not None:
a, b = eyelid_lower_line
b = b + line_y_offset
eyelid_lower_line = a, b
if debug_index:
debug_img = eye_img.copy()
filter_img = cv2.cvtColor(filter_img, cv2.COLOR_GRAY2BGR)
if l_lid_pts.size > 2:
draw_points(debug_img, l_lid_pts, (0, 0, 255), 1, 2)
if eyelid_lower_line is not None:
cv2.line(debug_img, (0, int(b)), (img_w, int(a * img_w + b)),
(0, 255, 0))
window_img = np.concatenate([window_img_l, window_img_r], axis=1)
stacked_windows = stack_imgs_vertical([window_img, filter_img])
stacked_imgs = stack_imgs_horizontal([stacked_windows, debug_img])
__debug_imgs_lower[debug_index] = stacked_imgs
if debug_index > 2:
cv2.imshow(__winname + repr(debug_index) + "l", stacked_imgs)
return eyelid_lower_line
def find_upper_eyelid(eye_img, debug_index):
u_2_win_rats_w = [0.0, 1.0, 0.0] # Margins around ROI windows
u_2_win_rats_h = [0.0, 0.5, 0.5]
# FIXME - using r channel?
img_blue = cv2.split(eye_img)[2]
img_w, img_h = eye_img.shape[:2]
# Indexes to extract window sub-images
w_y1, w_y2 = int(img_h * u_2_win_rats_h[0]), int(img_h *
sum(u_2_win_rats_h[:2]))
w_x1, w_x2 = int(img_w * u_2_win_rats_w[0]), int(img_w *
sum(u_2_win_rats_w[:2]))
# Split image into two halves
window_img = img_blue[w_y1:w_y2, w_x1:w_x2]
# Supress eyelashes
morph_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
window_img = cv2.morphologyEx(window_img, cv2.MORPH_CLOSE, morph_kernel)
# Filter right half with inverse kernel of left half to ignore iris/sclera boundary
filter_img_win = cv2.filter2D(window_img, -1, __gabor_kern_horiz)
# Copy windows back into correct places in full filter image
filter_img = np.zeros(eye_img.shape[:2], dtype=np.uint8)
filter_img[w_y1:w_y2, w_x1:w_x2] = filter_img_win
# Mask with circles
cv2.circle(filter_img,
(3 * filter_img.shape[1] / 7, filter_img.shape[0] / 2),
filter_img.shape[1] / 4, 0, -1)
cv2.circle(filter_img,
(4 * filter_img.shape[1] / 7, filter_img.shape[0] / 2),
filter_img.shape[1] / 4, 0, -1)
ys = np.argmax(filter_img, axis=0)
xs = np.arange(filter_img.shape[1])[ys > 0]
ys = (ys)[ys > 0]
u_lid_pts = []
for i, x in enumerate(xs):
col = filter_img.T[x]
start_ind, end_ind = ys[i] + 5, min(ys[i] + 100, len(col) - 2)
col_window = col[start_ind:end_ind]
max_col = np.max(col)
max_win = np.max(col_window)
if max_col - max_win < 50:
new_y = np.argmax(col_window) + ys[i] + 5
u_lid_pts.append((x, new_y))
else:
u_lid_pts.append((x, ys[i]))
# Only RANSAC fit eyelid if there are enough points
if len(u_lid_pts) < __min_num_pts_u * 2:
eyelid_upper_parabola = None
u_lid_pts = []
else:
u_lid_pts_l = [(x, y) for (x, y) in u_lid_pts
if x < filter_img.shape[1] / 2]
u_lid_pts_r = [(x, y) for (x, y) in u_lid_pts
if x > filter_img.shape[1] / 2]
# Fit eye_img coord points of sclera-segs to degree 2 polynomial
# a(x^2) + b(x) + c
eyelid_upper_parabola = ransac_parabola(u_lid_pts_l,
u_lid_pts_r,
ransac_iters_max=5,
refine_iters_max=2,
max_err=4)
if eyelid_upper_parabola is not None:
a, b, c = eyelid_upper_parabola
c = c - __parabola_y_offset
eyelid_upper_parabola = a, b, c
# --------------------- Debug Drawing ---------------------
if debug_index:
debug_img = eye_img.copy()
if eyelid_upper_parabola is not None:
lid_xs = np.arange(21) * img_w / 20
lid_ys = a * lid_xs**2 + b * lid_xs + c
lid_pts = np.dstack([lid_xs, lid_ys]).astype(int)
cv2.polylines(debug_img, lid_pts, False, (0, 255, 0), 1)
draw_points(debug_img, u_lid_pts, (0, 0, 255), 1, 2)
filter_img = cv2.cvtColor(filter_img, cv2.COLOR_GRAY2BGR)
draw_points(filter_img, u_lid_pts, (0, 0, 255), 1, 2)
stacked_windows = stack_imgs_vertical([window_img, filter_img])
stacked_imgs = stack_imgs_horizontal([stacked_windows, debug_img])
__debug_imgs_upper[debug_index] = stacked_imgs
if debug_index > 2:
cv2.imshow(__winname + repr(debug_index) + "u", stacked_imgs)
# --------------------- Debug Drawing ---------------------
return eyelid_upper_parabola
def find_pupil(eye_img_bgr,
fast_width_grads=25.5,
fast_width_iso=80,
weight_grads=0.9,
weight_iso=0.1,
debug_index=False):
eye_img_r = cv2.split(eye_img_bgr)[2]
fast_size_grads = (int(
(fast_width_grads / eye_img_bgr.shape[0]) * eye_img_bgr.shape[1]),
int(fast_width_grads))
fast_img_grads = cv2.resize(eye_img_r, fast_size_grads)
fast_size_iso = (int(fast_width_iso),
int((fast_width_iso / eye_img_r.shape[1]) *
eye_img_r.shape[0]))
fast_img_iso = cv2.resize(eye_img_r, fast_size_iso)
c_map_grads = eye_center_locator_gradients.get_center_map(fast_img_grads)
c_map_iso = eye_center_locator_isophote.get_center_map(fast_img_iso)
c_map_norm_grads = cv2.normalize(c_map_grads,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX)
c_map_big_grads = cv2.resize(
c_map_norm_grads,
(eye_img_bgr.shape[1], eye_img_bgr.shape[0])).astype(np.uint8)
c_map_norm_iso = cv2.normalize(c_map_iso,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX)
c_map_big_iso = cv2.resize(
c_map_norm_iso,
(eye_img_bgr.shape[1], eye_img_bgr.shape[0])).astype(np.uint8)
joint_c_map = cv2.addWeighted(c_map_big_grads, w_grads, c_map_big_iso,
w_iso, 1.0)
max_val_index = np.argmax(joint_c_map)
pupil_y0, pupil_x0 = max_val_index // joint_c_map.shape[
1], max_val_index % joint_c_map.shape[1]
max_val_index_2 = np.argmax(c_map_big_grads)
pupil_y0_2, pupil_x0_2 = max_val_index_2 // joint_c_map.shape[
1], max_val_index_2 % joint_c_map.shape[1]
max_val_index_3 = np.argmax(c_map_big_iso)
pupil_y0_3, pupil_x0_3 = max_val_index_3 // joint_c_map.shape[
1], max_val_index_3 % joint_c_map.shape[1]
if debug_index:
debug_img = eye_img_bgr.copy()
joint_c_map = cv2.cvtColor(joint_c_map, cv2.COLOR_GRAY2BGR)
c_map_big_iso = cv2.cvtColor(c_map_big_iso, cv2.COLOR_GRAY2BGR)
c_map_big_grads = cv2.cvtColor(c_map_big_grads, cv2.COLOR_GRAY2BGR)
draw_utils.draw_cross(debug_img, (pupil_x0, pupil_y0), (0, 255, 255),
16, 2)
draw_utils.draw_cross(joint_c_map, (pupil_x0_3, pupil_y0_3),
(255, 0, 255), 8, 2)
draw_utils.draw_cross(joint_c_map, (pupil_x0_2, pupil_y0_2),
(255, 0, 255), 8, 2)
draw_utils.draw_cross(joint_c_map, (pupil_x0, pupil_y0), (255, 0, 0),
16, 2)
draw_utils.draw_cross(c_map_big_iso, (pupil_x0_3, pupil_y0_3),
(255, 0, 0), 16, 2)
draw_utils.draw_cross(c_map_big_grads, (pupil_x0_2, pupil_y0_2),
(255, 0, 0), 16, 2)
stacked_imgs = image_utils.stack_imgs_horizontal(
[debug_img, c_map_big_grads, c_map_big_iso, joint_c_map])
__debug_imgs[debug_index] = stacked_imgs
if debug_index == 2:
full_debug_img = image_utils.stack_imgs_vertical(
[__debug_imgs[1], __debug_imgs[2]])
cv2.imshow(__winname, full_debug_img)
elif debug_index > 2:
cv2.imshow(__winname, stacked_imgs)
return pupil_x0, pupil_y0
def find_lower_eyelid(eye_img, debug_index):
line_y_offset = 0 # Amount to shift eye-lid by after detection
img_blue = cv2.split(eye_img)[2]
img_w, img_h = eye_img.shape[:2]
# Indexes to extract window sub-images
w_y1, w_y2 = int(img_h * __l_win_rats_h[0]), int(img_h * sum(__l_win_rats_h[:2]))
wl_x1, wl_x2 = int(img_w * __l_win_rats_w_l[0]), int(img_w * sum(__l_win_rats_w_l[:2]))
wr_x1, wr_x2 = int(img_w * __l_win_rats_w_r[0]), int(img_w * sum(__l_win_rats_w_r[:2]))
# Split image into two halves
window_img_l = img_blue[w_y1:w_y2, wl_x1:wl_x2]
window_img_r = img_blue[w_y1:w_y2, wr_x1:wr_x2]
window_img_l = cv2.GaussianBlur(window_img_l, (5, 5), 20)
window_img_r = cv2.GaussianBlur(window_img_r, (5, 5), 20)
filter_img_l = cv2.filter2D(window_img_l, -1, __gabor_kern_diag)
filter_img_r = cv2.filter2D(window_img_r, -1, cv2.flip(__gabor_kern_diag, 1))
filter_img = np.concatenate([filter_img_l, filter_img_r], axis=1)
# In polar image, x <-> theta, y <-> magnitude
max_vals = np.max(filter_img, axis=0)
ys = np.argmax(filter_img, axis=0) # Take highest filter response as limbus points
xs = (np.arange(filter_img.shape[1]) + wl_x1)[max_vals > __min_thresh]
ys = (ys + w_y1)[max_vals > __min_thresh]
l_lid_pts = np.squeeze(np.dstack([xs, ys]), axis=0)
# Only RANSAC fit eyelid if there are enough points
if l_lid_pts.size < __min_num_pts_u * 2:
eyelid_lower_line = None
else:
eyelid_lower_line = ransac_line(l_lid_pts)
if eyelid_lower_line is not None:
a, b = eyelid_lower_line
b = b + line_y_offset
eyelid_lower_line = a, b
if debug_index:
debug_img = eye_img.copy()
filter_img = cv2.cvtColor(filter_img, cv2.COLOR_GRAY2BGR)
if l_lid_pts.size > 2:
draw_points(debug_img, l_lid_pts, (0, 0, 255), 1, 2)
if eyelid_lower_line is not None:
cv2.line(debug_img, (0, int(b)), (img_w, int(a * img_w + b)), (0, 255, 0))
window_img = np.concatenate([window_img_l, window_img_r], axis=1)
stacked_windows = stack_imgs_vertical([window_img, filter_img])
stacked_imgs = stack_imgs_horizontal([stacked_windows, debug_img])
__debug_imgs_lower[debug_index] = stacked_imgs
if debug_index > 2:
cv2.imshow(__winname + repr(debug_index) + "l", stacked_imgs)
return eyelid_lower_line
def find_upper_eyelid(eye_img, debug_index):
u_2_win_rats_w = [0.0, 1.0, 0.0] # Margins around ROI windows
u_2_win_rats_h = [0.0, 0.5, 0.5]
# FIXME - using r channel?
img_blue = cv2.split(eye_img)[2]
img_w, img_h = eye_img.shape[:2]
# Indexes to extract window sub-images
w_y1, w_y2 = int(img_h * u_2_win_rats_h[0]), int(img_h * sum(u_2_win_rats_h[:2]))
w_x1, w_x2 = int(img_w * u_2_win_rats_w[0]), int(img_w * sum(u_2_win_rats_w[:2]))
# Split image into two halves
window_img = img_blue[w_y1:w_y2, w_x1:w_x2]
# Supress eyelashes
morph_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
window_img = cv2.morphologyEx(window_img, cv2.MORPH_CLOSE, morph_kernel)
# Filter right half with inverse kernel of left half to ignore iris/sclera boundary
filter_img_win = cv2.filter2D(window_img, -1, __gabor_kern_horiz)
# Copy windows back into correct places in full filter image
filter_img = np.zeros(eye_img.shape[:2], dtype=np.uint8)
filter_img[w_y1:w_y2, w_x1:w_x2] = filter_img_win
# Mask with circles
cv2.circle(filter_img, (3 * filter_img.shape[1] / 7, filter_img.shape[0] / 2), filter_img.shape[1] / 4, 0, -1)
cv2.circle(filter_img, (4 * filter_img.shape[1] / 7, filter_img.shape[0] / 2), filter_img.shape[1] / 4, 0, -1)
ys = np.argmax(filter_img, axis=0)
xs = np.arange(filter_img.shape[1])[ys > 0]
ys = (ys)[ys > 0]
u_lid_pts = []
for i, x in enumerate(xs):
col = filter_img.T[x]
start_ind, end_ind = ys[i] + 5, min(ys[i] + 100, len(col) - 2)
col_window = col[start_ind:end_ind]
max_col = np.max(col)
max_win = np.max(col_window)
if max_col - max_win < 50:
new_y = np.argmax(col_window) + ys[i] + 5
u_lid_pts.append((x, new_y))
else:
u_lid_pts.append((x, ys[i]))
# Only RANSAC fit eyelid if there are enough points
if len(u_lid_pts) < __min_num_pts_u * 2:
eyelid_upper_parabola = None
u_lid_pts = []
else:
u_lid_pts_l = [(x, y) for (x, y) in u_lid_pts if x < filter_img.shape[1] / 2]
u_lid_pts_r = [(x, y) for (x, y) in u_lid_pts if x > filter_img.shape[1] / 2]
# Fit eye_img coord points of sclera-segs to degree 2 polynomial
# a(x^2) + b(x) + c
eyelid_upper_parabola = ransac_parabola(
u_lid_pts_l, u_lid_pts_r, ransac_iters_max=5, refine_iters_max=2, max_err=4
)
if eyelid_upper_parabola is not None:
a, b, c = eyelid_upper_parabola
c = c - __parabola_y_offset
eyelid_upper_parabola = a, b, c
# --------------------- Debug Drawing ---------------------
if debug_index:
debug_img = eye_img.copy()
if eyelid_upper_parabola is not None:
lid_xs = np.arange(21) * img_w / 20
lid_ys = a * lid_xs ** 2 + b * lid_xs + c
lid_pts = np.dstack([lid_xs, lid_ys]).astype(int)
cv2.polylines(debug_img, lid_pts, False, (0, 255, 0), 1)
draw_points(debug_img, u_lid_pts, (0, 0, 255), 1, 2)
filter_img = cv2.cvtColor(filter_img, cv2.COLOR_GRAY2BGR)
draw_points(filter_img, u_lid_pts, (0, 0, 255), 1, 2)
stacked_windows = stack_imgs_vertical([window_img, filter_img])
stacked_imgs = stack_imgs_horizontal([stacked_windows, debug_img])
__debug_imgs_upper[debug_index] = stacked_imgs
if debug_index > 2:
cv2.imshow(__winname + repr(debug_index) + "u", stacked_imgs)
# --------------------- Debug Drawing ---------------------
return eyelid_upper_parabola
