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3efff6d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | import cv2
import numpy as np
import mediapipe as mp
from aura.gaze_tracking import GazeTracking
import time
import threading
class ImageEnhancer:
@staticmethod
def enhance_image(frame):
lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)
clahe = cv2.createCLAHE(clipLimit=4, tileGridSize=(8,8))
l_clahe = clahe.apply(l)
lab_clahe = cv2.merge((l_clahe, a, b))
enhanced_image = cv2.cvtColor(lab_clahe, cv2.COLOR_LAB2BGR)
gamma = 1.8
look_up_table = np.array([((i / 255.0) ** gamma) * 255 for i in range(256)]).astype("uint8")
enhanced_image2 = cv2.LUT(enhanced_image, look_up_table)
gray = cv2.cvtColor(enhanced_image2, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 250, 500)
edges_colored = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
enhanced_image3 = cv2.addWeighted(enhanced_image2, 0.8, edges_colored, 0.2, 0)
return enhanced_image3
class KalmanFilter:
def __init__(self):
self.kalman = cv2.KalmanFilter(4, 2)
self.kalman.measurementMatrix = np.array([[1, 0, 0, 0],
[0, 1, 0, 0]], np.float32)
self.kalman.transitionMatrix = np.array([[1, 0, 1, 0],
[0, 1, 0, 1],
[0, 0, 1, 0],
[0, 0, 0, 1]], np.float32)
self.kalman.processNoiseCov = np.eye(4, dtype=np.float32) * 0.5
def correct(self, coord):
return self.kalman.correct(np.array([coord[0], coord[1]], np.float32))
def predict(self):
return self.kalman.predict()
class GazeProcessor:
def __init__(self, webcam):
self.webcam = webcam
self.webcam_lock = threading.Lock()
self.gaze_flip = GazeTracking()
self.gaze_orig = GazeTracking()
self.kalman_filter = KalmanFilter()
self.total_distractions = 0
self.focused = 0
self.right_distractions = 0
self.left_distractions = 0
def _get_gaze_info(self, gaze):
if gaze.is_right():
return "Yes1", (0, 0, 255)
elif gaze.is_left():
return "Yes", (0, 0, 255)
elif gaze.is_center():
return "No", (0, 255, 0)
return "", (255, 0, 0)
def _get_eye_position(self, gaze):
left_pupil = gaze.pupil_left_coords()
right_pupil = gaze.pupil_right_coords()
if left_pupil and right_pupil:
avg_pupil = np.mean([left_pupil, right_pupil], axis=0)
corrected_pupil = self.kalman_filter.correct(avg_pupil)
predicted_pupil = self.kalman_filter.predict()
return predicted_pupil
return None
def process_combined(self):
mp_face_mesh = mp.solutions.face_mesh
face_mesh = mp_face_mesh.FaceMesh(min_detection_confidence=0.5, min_tracking_confidence=0.5)
mp_drawing = mp.solutions.drawing_utils
drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
while self.webcam.isOpened():
start_time = time.time()
with self.webcam_lock:
ret, frame = self.webcam.read()
if not ret:
break
enhanced_frame = ImageEnhancer.enhance_image(frame)
flipped_frame = cv2.flip(enhanced_frame, 1)
self.gaze_flip.refresh(flipped_frame)
self.gaze_orig.refresh(enhanced_frame)
new_frame_orig = self.gaze_orig.annotated_frame()
text_flip, _ = self._get_gaze_info(self.gaze_flip)
text_orig, _ = self._get_gaze_info(self.gaze_orig)
text_pose = ''
image_rgb = cv2.cvtColor(enhanced_frame, cv2.COLOR_BGR2RGB)
results = face_mesh.process(image_rgb)
img_h, img_w, img_c = enhanced_frame.shape
face_3d = []
face_2d = []
if results.multi_face_landmarks:
for face_landmarks in results.multi_face_landmarks:
for idx, lm in enumerate(face_landmarks.landmark):
if idx in [33, 263, 1, 61, 291, 199]:
if idx == 1:
nose_2d = (lm.x * img_w, lm.y * img_h)
nose_3d = (lm.x * img_w, lm.y * img_h, lm.z * 3000)
x, y = int(lm.x * img_w), int(lm.y * img_h)
face_2d.append([x, y])
face_3d.append([x, y, lm.z])
face_2d = np.array(face_2d, dtype=np.float64)
face_3d = np.array(face_3d, dtype=np.float64)
focal_length = 1 * img_w
cam_matrix = np.array([[focal_length, 0, img_w / 2],
[0, focal_length, img_h / 2],
[0, 0, 1]])
dist_matrix = np.zeros((4, 1), dtype=np.float64)
success, rot_vec, trans_vec = cv2.solvePnP(face_3d, face_2d, cam_matrix, dist_matrix)
rmat, jac = cv2.Rodrigues(rot_vec)
angles, mtxR, mtxQ, Qx, Qy, Qz = cv2.RQDecomp3x3(rmat)
x = angles[0] * 360
y = angles[1] * 360
z = angles[2] * 360
if y < -13:
text_pose = "Looking Right"
elif y > 13:
text_pose = "Looking Left"
elif x < -13:
text_pose = "Looking Down"
elif x > 13:
text_pose = "Looking Up"
else:
text_pose = "Forward"
if text_pose != "Forward" or text_flip == "Yes1" or text_orig == "Yes1" or text_flip == 'Yes' or text_orig == 'Yes':
self.total_distractions += 1
if text_pose == "Forward" and text_orig == "No" and text_flip == "No":
self.focused += 1
if text_flip == "Yes1" or text_orig == "Yes" or text_pose == "Looking Left":
self.left_distractions += 1
if text_flip == "Yes" or text_orig == "Yes1" or text_pose == "Looking Right":
self.right_distractions += 1
cv2.putText(new_frame_orig, text_pose, (10, 140), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
mp_drawing.draw_landmarks(
image=new_frame_orig,
landmark_list=face_landmarks,
connections=mp_face_mesh.FACEMESH_TESSELATION,
landmark_drawing_spec=drawing_spec,
connection_drawing_spec=drawing_spec)
frame_height, frame_width = new_frame_orig.shape[:2]
cv2.putText(new_frame_orig, f"Distractions: {self.total_distractions}", (10, frame_height - 450),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.putText(new_frame_orig, f"Focused: {self.focused}", (10, frame_height - 400),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
_, buffer = cv2.imencode('.jpg', new_frame_orig)
frame = buffer.tobytes()
yield frame
def get_focus_and_distractions(self):
return {
'distractions': self.total_distractions,
'focus': self.focused,
'left_distractions': self.left_distractions,
'right_distractions': self.right_distractions
} |