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Added some object-oriented-ness to the EyeTracker class

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Cowwan 2024-02-06 12:14:04 -05:00
parent 517d859ca1
commit f793bb270a
2 changed files with 50 additions and 37 deletions
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66
eyes.py
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@ -42,6 +42,12 @@ class EyeTracker():
self.eye_tracking_frames = 0
self.mean = np.float32(np.array([-2.1179, -2.0357, -1.8044]))
self.std = np.float32(np.array([0.0171, 0.0175, 0.0174]))
self.faceFrame = None
self.eyeState = None
self.offset = None
self.results = None
self.faceCenter = None
self.faceRadius = None
model_base_path = os.path.join(os.path.dirname(__file__), os.path.join("models"))
providersList = onnxruntime.capi._pybind_state.get_available_providers()
@ -54,27 +60,31 @@ class EyeTracker():
self.gaze_model = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_gaze32_split_opt.onnx"), sess_options=options, providers=providersList)
def get_eye_state(self,face_frame, lms, offset):
eye_state = [[1.,0.,0.,0.],[1.,0.,0.,0.]] #Array constructed early so it can be used for an early exit
def get_eye_state(self,frame, lms):
self.eyeState = [[1.,0.,0.,0.],[1.,0.,0.,0.]] #Array constructed early so it can be used for an early exit
lms = self.extract_face(frame, np.array(lms)[:,0:2][:,::-1])
self.eye_tracking_frames+=1
#I've tried to thread this because they don't need to happen in sequence
#but it doesn't do any good because python threads kinda suck
#I'd need a persistent right eye and left eye process and that feels excessive
right_eye, REyeInfo = self.prepare_eye(face_frame, lms[36,0:2], lms[39,0:2], False)
left_eye, LEyeInfo = self.prepare_eye(face_frame,lms[42,0:2], lms[45,0:2], True)
right_eye, REyeInfo = self.prepare_eye(lms[36,0:2], lms[39,0:2], False)
left_eye, LEyeInfo = self.prepare_eye(lms[42,0:2], lms[45,0:2], True)
if right_eye is None or left_eye is None:
return eye_state #Early exit if one of the eyes doesn't have data
both_eyes = np.concatenate((right_eye, left_eye))
results = self.gaze_model.run([], {"input": both_eyes})[0]
eye_state[0] = self.calculateEye(results[0], REyeInfo, offset,0)
eye_state[1] = self.calculateEye(results[1], LEyeInfo, offset,1)
return eye_state
self.results = self.gaze_model.run([], {"input": both_eyes})[0]
self.calculateEye( REyeInfo, 0)
self.calculateEye( LEyeInfo, 1)
return self.eyeState
def calculateStandardDeviation(self, conf, eye):
avgRatio = 1/self.eye_tracking_frames
self.average_eye_confidence[eye] = (self.average_eye_confidence[eye]*(1-avgRatio))+ (conf * avgRatio)
@ -83,31 +93,30 @@ class EyeTracker():
self.eye_std_dev[eye] = math.sqrt(self.average_eye_confidence_variance[eye])
def calculateEye(self, results, eye_info , offset, eye):
def calculateEye(self, eye_info, eye):
e_x, e_y, scale, reference, angles = eye_info
confidenceThreshold = self.average_eye_confidence[eye] - 2 * self.eye_std_dev[eye]
m = results[0].argmax()
m = self.results[eye][0].argmax()
x = m // 8
y = m % 8
p=results[1][x, y]
p=self.results[eye][1][x, y]
p = clamp(p, 0.00001, 0.9999)
off_x = math.log(p/(1-p))
eye_x = 4.0 *(x + off_x)
p=results[2][x, y]
p=self.results[eye][2][x, y]
p = clamp(p, 0.00001, 0.9999)
off_y = math.log(p/(1-p))
eye_y = 4.0 * (y + off_y)
#I'm proud of this
#it's my "anti-Cali" functionality
#if eye movements are below 2 standard deviations of the average the movement is severely limited
#the eyes are handled independenly because my testing showed that one eye tended to have a higher condifence than the other
limit = max((results[0][x,y]- 0.5)*(2/confidenceThreshold),0)
if results[0][x,y] < confidenceThreshold:
limit = max((self.results[eye][0][x,y]- 0.5)*(2/confidenceThreshold),0)
if self.results[eye][0][x,y] < confidenceThreshold:
Delta = eye_y - self.last_eye_state[eye][0]
eye_y = self.last_eye_state[eye][0] + clamp(Delta, -limit, limit)
Delta = eye_x - self.last_eye_state[eye][1]
@ -121,11 +130,15 @@ class EyeTracker():
eye_y = e_y + scale[1] * eye_y
eye_x, eye_y = rotate(reference, (eye_x, eye_y), -angles)
eye_x, eye_y = (eye_x, eye_y) + offset
self.calculateStandardDeviation(results[0][x,y], eye)
return [1.0, eye_y, eye_x, results[0][x,y]]
eye_x, eye_y = (eye_x, eye_y) + self.offset
def prepare_eye(self,im, outer_pt, inner_pt, flip):
conf = self.results[eye][0][x,y]
self.calculateStandardDeviation(conf, eye)
self.eyeState[eye] = [1.0, eye_y, eye_x, conf]
return
def prepare_eye(self, outer_pt, inner_pt, flip):
im = self.faceFrame
(x1, y1), (x2, y2), center, reference, a = self.corners_to_eye((outer_pt, inner_pt), im.shape[1], im.shape[0])
#rotating an image is expensive and reduces clarity
#so I just don't if it's a relatively small angle
@ -154,3 +167,18 @@ class EyeTracker():
upper_left = clamp_to_im(center - radius, w, h)
lower_right = clamp_to_im(center + radius, w, h)
return upper_left, lower_right, center, c1, a
def extract_face(self, frame, lms):
x1, y1 = lms.min(0)
x2, y2 = lms.max(0)
self.faceRadius = np.array([(x2 - x1), (y2 - y1)])*0.6
self.faceCenter = (np.array((x1, y1)) + np.array((x2, y2))) / 2.0
w, h, _ = frame.shape
x1, y1 = clamp_to_im(self.faceCenter - self.faceRadius , h, w)
x2, y2 = clamp_to_im(self.faceCenter + self.faceRadius + 1, h, w)
self.offset = np.array((x1, y1))
lms = (lms[:, 0:2] - self.offset).astype(int)
frame = frame[y1:y2, x1:x2]
self.faceFrame = frame
return lms

21
tracker.py
View file

@ -11,18 +11,6 @@ import emilianaFeatureExtractor
#this file is so much smaller than it used to be, lol
#I moved most of the functionality into separate files so they're easier to work with
def extract_face( frame, lms):
x1, y1 = lms.min(0)
x2, y2 = lms.max(0)
radius = np.array([(x2 - x1), (y2 - y1)])*0.6
center = (np.array((x1, y1)) + np.array((x2, y2))) / 2.0
w, h, _ = frame.shape
x1, y1 = clamp_to_im(center - radius, h, w)
x2, y2 = clamp_to_im(center + radius + 1, h, w)
offset = np.array((x1, y1))
lms = (lms[:, 0:2] - offset).astype(int)
frame = frame[y1:y2, x1:x2]
return frame, lms, offset, center, radius
def clamp_to_im(pt, w, h): #8 times per frame, but that only accounts for 0.005ms
x=max(pt[0],0)
@ -49,8 +37,6 @@ class Tracker():
self.mean = np.float32(np.array([-2.1179, -2.0357, -1.8044]))
self.std = np.float32(np.array([0.0171, 0.0175, 0.0174]))
self.camera = np.array([[width, 0, width/2], [0, width, height/2], [0, 0, 1]], np.float32)
self.inverse_camera = np.linalg.inv(self.camera)
self.width = width
self.height = height
@ -133,9 +119,8 @@ class Tracker():
if conf < self.threshold:
return self.early_exit("Confidence below threshold", start)
#lms is short for landmarks
face_frame, face_lms, face_offset,face_center, face_radius = extract_face(frame, np.array(lms)[:,0:2][:,::-1])
eye_state = self.EyeTracker.get_eye_state(face_frame, face_lms, face_offset)
eye_state = self.EyeTracker.get_eye_state(frame, lms)
self.face_info[0].update((conf, (lms, eye_state)), np.array(lms)[:, 0:2].mean(0))
@ -155,7 +140,7 @@ class Tracker():
duration_pnp += 1000 * (time.perf_counter() - start_pnp)
duration = (time.perf_counter() - start) * 1000
self.messageQueue.put(f"Took {duration:.2f}ms (detect: {duration_fd:.2f}ms, crop: {duration_pp:.2f}ms, track: {duration_model:.2f}ms, 3D points: {duration_pnp:.2f}ms)")
return face_info, face_center, face_radius
return face_info, self.EyeTracker.faceCenter, self.EyeTracker.faceRadius
#Combined multiple failures into one catch all exit
return self.early_exit("Face info not valid", start)