def place_cross(self, source: np.ndarray, point: tuple,
color: tuple) -> None:
try:
source[to_int(point[1] - 3):to_int(point[1] + 4),
to_int(point[0])] = color
source[to_int(point[1]),
to_int(point[0] - 3):to_int(point[0] + 4)] = color
except:
pass
def track(self, last: bool = False):
try:
if config.engine.blink_i == 1:
self.corners = self.standard_corners.copy()
self.walkout_offset = 0
self.refresh_source(self.source)
contours, hierarchy = cv2.findContours(self.area, 1, 2)
if len(contours) > 0:
dists = np.zeros(len(contours))
for i, cnt in enumerate(contours):
M = cv2.moments(cnt)
try:
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
if self.type == 1:
dists[i] = np.mean(self.source[cy - 2:cy + 2,
cx - 2:cx + 2])
else:
dists[i] = np.sqrt((cx - self.center[0])**2 +
(cy - self.center[1])**2)
except:
dists[i] = 255
from operator import itemgetter
#todo: score these based on 1) color and 2) distance to center
M = cv2.moments(contours[min(enumerate(dists),
key=itemgetter(1))[0]])
try:
cx = round(M['m10'] / M['m00'])
cy = round(M['m01'] / M['m00'])
except:
return False
#print(cx,cy)
self.center = (cx, cy)
#self.source[cy,cx]=250
#cv2.imshow("JJ", self.source)
#cv2.waitKey(0)
center = [
self.center[0] - self.corners[0][0],
self.center[1] - self.corners[0][1]
]
walkout = self.walkout
walkout.reset(center)
fit_product = 1
if walkout.walkout():
fit_product = self.fit_model
if fit_product.fit(walkout.rx, walkout.ry):
ellipse = fit_product
else:
ellipse = 0
else:
ellipse = 0
except Exception as e:
print(e)
return False
if ellipse == fit_product:
center, width, height = ellipse.center, ellipse.width, ellipse.height
if width * height > 4:
# if self.type == 2:
# if distance(np.array(center), np.array(self.center)) > 6: #normalize qqqq
# self.center = self.original_center[self.center_index]
# self.corners = self.standard_corners.copy()
# return False
self.ellipse = ellipse
self.center = center
#"walkout_offset" defines the walkout offset for the next frame.
#The multiplication factor, here .4, returns slightly below the average.
self.walkout_offset = int(.4 * (width + height))
self.margin = np.amax(ellipse.dimensions_int) * 2
center_int = tuple_int(center)
self.corners[0] = (max(center_int[0] - self.margin,
0), max(center_int[1] - self.margin, 0))
self.corners[1] = (min(center_int[0] + self.margin,
config.engine.width),
min(center_int[1] + self.margin,
config.engine.height))
self.center_index = 0
margin = to_int(np.amax(ellipse.dimensions_int) * 1)
#self.bbox = (max(center_int[0] - margin, 0), max(center_int[1] - margin, 0), margin * 2, margin * 2)
# try:
# if self.bbox:
# (success, box) = self.tracker.update(self.source)
# if success:
# box=np.array(box, dtype=int)
# x,y,w,h = box
#
# cv2.rectangle(self.source,(x,y),(x+w,y+h),(0,255,0),1)
# #cv2.rectangle(self.source, box, (0,255,0),1)
# cv2.imshow("Kk", self.source)
# #cv2.waitKey(0)
# #print(box)
# except Exception as e:
# print(e)
# pass
return True
if last:
return False
# Shape detection failed, try contour detection
self.corners = self.standard_corners.copy()
self.walkout_offset = 0
self.refresh_source(self.source)
contours, hierarchy = cv2.findContours(self.area, 1, 2)
if len(contours) > 0:
dists = np.zeros(len(contours))
for i, cnt in enumerate(contours):
M = cv2.moments(cnt)
try:
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
if self.type == 1:
dists[i] = np.mean(self.source[cy - 2:cy + 2,
cx - 2:cx + 2])
else:
dists[i] = np.sqrt((cx - self.center[0])**2 +
(cy - self.center[1])**2)
except:
dists[i] = 255
from operator import itemgetter
#todo: score these based on 1) color and 2) distance to center
M = cv2.moments(contours[min(enumerate(dists),
key=itemgetter(1))[0]])
try:
cx = round(M['m10'] / M['m00'])
cy = round(M['m01'] / M['m00'])
except:
return False
#print(cx,cy)
self.center = (cx, cy)
return self.track(True)
return False
def walkout(self, total_n=0):
"""
Points are iteratively translated centrifugally to a limit,
and in an equally distributed manner (θ=360/n) from a center.
Notably, n is offset based on the width and height of the previously detected ellipsoid.
Similar to what was described by Sakatani and Isa (2004).
"""
x = point_source.copy()
y = point_source.copy()
step_list = step_list_source.copy()
offset = self.processor.walkout_offset
b = 0
for i, cos_sin in enumerate(cos_sin_steps):
cos, sin = cos_sin
x[i] = self.center[0] + cos * offset
y[i] = self.center[1] + sin * offset
insidemark = False
for _ in limit:
b += 1
#If walkout coordinate hits out-of-contour area, break out of the loop.
try:
pixel = self.processor.area[to_int(y[i]), to_int(x[i])]
if pixel != 255:
if pixel == 100: #inside mark
if insidemark == False:
lastcoord = x[i], y[i]
insidemark = True
else:
if insidemark:
x[i], y[i] = lastcoord
break
else:
insidemark = False
except:
#Walkout hit outside of Processor area. Go back to previous in-bounds point, then break.
x[i] -= cos
y[i] -= sin
step_list[i] -= 1
break
x[i] += cos
y[i] += sin
step_list[i] += 1
coord_length = len(x)
if b <= len(x) + 2:
return False
if coord_length < 5:
return False
x, y, coord_length = self.filter(x, y, coord_length, step_list)
if coord_length < 4:
return False
self.rx, self.ry = x[(0 != x)], y[(0 != y)]
return True
def cr_artifacts(self, cr_processor, offsetx: int, offsety: int,
pupil_area) -> None:
"""
Computes pupillary overlaps and acts to remove these artifacts.
"""
cr_center, cr_width, cr_height, cr_angle, cr_dimensions_int = cr_processor.ellipse.parameters(
)
cr_center_int = tuple_int(cr_center)
larger_width, larger_height = larger_radius = tuple(
int(1.2 * element) for element in cr_dimensions_int)
cr_width_norm = larger_width * self.norm
cr_height_norm = larger_height * self.norm
dimensional_product = larger_width * larger_height
arc = [
dimensional_product /
np.sqrt((cr_width_norm * anglesteps_cos[i])**2 +
(cr_width_norm * anglesteps_sin[i])**2)
for i in angular_range
]
cos_sin_arc = [(to_int(anglesteps_cos[i] * arc[i]),
to_int(anglesteps_sin[i] * arc[i]))
for i in angular_range]
hit_list = zeros.copy()
for i, arc_element in enumerate(cos_sin_arc):
cos, sin = arc_element
x = cr_center_int[0] + offsetx + cos
y = cr_center_int[1] + offsety + sin
n = 1
strike = 0
while n < self.norm_cr_artefact: #normalize qqqq
n += 1
try:
if pupil_area[y, x] != 0:
hit_list[i] = i + 1
break
else:
x += cos
y += sin
except:
break
if np.any(hit_list):
delta = np.count_nonzero(number_row - hit_list)
if delta < self.norm_cr_artefact:
cv2.ellipse(self.pupil_source, cr_center_int, larger_radius,
cr_angle, 0, 360, 0, -1)
else:
for element in hit_list:
if element != 0:
cos, sin = cos_sin_arc[element - 1]
x = cr_center_int[0] + cos
y = cr_center_int[1] + sin
cv2.ellipse(self.pupil_source, cr_center_int,
larger_radius, cr_angle, element * 40 - 40,
element * 40, 0, 4) #normalize qqqq
def update_track(self, blink: int) -> None:
frame_preview = cv2.cvtColor(config.engine.source, cv2.COLOR_GRAY2BGR)
frame_source = frame_preview.copy()
cr_width = pupil_width = -1
Processor = self.pupil_processor
if blink == 0:
self.rplace_markers(frame_preview)
for index, cr_processor in enumerate(config.engine.cr_processors):
if cr_processor.active:
cr_corners = cr_processor.corners
cr_center, cr_width, cr_height, cr_angle, cr_dimensions_int = cr_processor.ellipse.parameters(
)
if self._state == "adjustment":
if cr_processor == self.current_cr_processor:
color = bluish
else:
color = green
try:
cv2.ellipse(frame_preview, tuple_int(cr_center),
cr_dimensions_int, cr_angle, 0, 360,
color, 1)
self.place_cross(frame_preview, cr_center, color)
cv2.rectangle(frame_preview, cr_corners[0],
cr_corners[1], color)
cv2.putText(
frame_preview, "{}".format(index + 2),
(int((cr_corners[1][0] + cr_corners[0][0]) *
.5 - 3), cr_corners[0][1] - 3), font, .7,
color, 0, cv2.LINE_4)
except Exception as e:
cr_processor.active = False
else:
self.place_cross(frame_preview, cr_center, bluish)
try:
pupil_corners = Processor.corners
pupil_center, pupil_width, pupil_height, pupil_angle, pupil_dimensions_int = Processor.ellipse.parameters(
)
cv2.ellipse(frame_preview, tuple_int(pupil_center),
pupil_dimensions_int, pupil_angle, 0, 360, red, 1)
self.place_cross(frame_preview, pupil_center, red)
cv2.rectangle(frame_preview, pupil_corners[0],
pupil_corners[1], red)
except:
pass
if self._state == "adjustment":
stock_P = self.PStock.copy()
stock_CR = self.CRStock.copy()
if cr_width != -1:
cr_area = self.current_cr_processor.area
offset_y = int((self.binary_height - cr_area.shape[0]) / 2)
offset_x = int((self.binary_width - cr_area.shape[1]) / 2)
stock_CR[offset_y:min(offset_y +
cr_area.shape[0], self.binary_height),
offset_x:min(offset_x + cr_area.shape[1], self.
binary_width)] = cr_area
stock_CR[0:20, 0:self.binary_width] = self.crstock_txt_selected
else:
stock_CR[0:20, 0:self.binary_width] = self.crstock_txt
if pupil_width != -1:
# stock_P[pcorners[0][1]:pcorners[0][1]+Processor.area.shape[0],
# pcorners[0][0]:pcorners[0][0]+Processor.area.shape[1]] = Processor.area
stock_P[0:20, 0:self.binary_width] = self.pstock_txt_selected
pupil_area = Processor.area
offset_y = int((self.binary_height - pupil_area.shape[0]) / 2)
offset_x = int((self.binary_width - pupil_area.shape[1]) / 2)
stock_P[offset_y:min(offset_y +
pupil_area.shape[0], self.binary_height),
offset_x:min(offset_x + pupil_area.shape[1], self.
binary_width)] = pupil_area
else:
stock_P[0:20, 0:self.binary_width] = self.pstock_txt
cv2.putText(
stock_P, "{} || {}".format(round(Processor.binarythreshold, 1),
Processor.blur),
(10, self.binary_height - 10), font, .7, 255, 0, cv2.LINE_4)
cv2.putText(
stock_CR, "{} || {}".format(
round(self.current_cr_processor.binarythreshold, 1),
self.current_cr_processor.blur),
(10, self.binary_height - 10), font, .7, 255, 0, cv2.LINE_4)
frame_source[0:20, 0:self.width] = self.src_txt
frame_preview[0:20, 0:self.width] = self.prev_txt
frame_preview[0:self.height, 0:1] = 0
cv2.putText(frame_source, "#" + str(config.importer.frame),
(to_int(self.width / 2), 12), font, .7,
(255, 255, 255), 0, cv2.LINE_4)
i = 0
while i < 5:
frame_source[to_int(self.height * i / 5) -
1:to_int(self.height * i / 5) + 1,
0:self.width] = (100, 100, 100)
i += 1
cv2.imshow("CONFIGURATION", np.hstack(
(frame_source, frame_preview)))
cv2.imshow("BINARY", np.vstack((stock_P, stock_CR)))
self.out.write(frame_preview)
self.key = cv2.waitKey(50)
if self.key == ord("-"):
cv2.imwrite(
"screen_cap_fr{}.jpg".format(config.importer.frame),
frame_preview)
self.key_listener(self.key)
else:
# real tracking
self.out.write(frame_preview)
cv2.imshow("TRACKING", frame_preview)
key = cv2.waitKey(1)
if key == ord("q"):
config.engine.release()