156 lines
6.4 KiB
Python
156 lines
6.4 KiB
Python
import os
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import numpy as np
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import math
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import cv2
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import onnxruntime
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import time
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def clamp_to_im(pt, w, h): #8 times per frame, but that only accounts for 0.005ms
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x=max(pt[0],0)
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y=max(pt[1],0)
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if x >= w:
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x = w-1
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if y >= h:
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y = h-1
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return (int(x),int(y+1))
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def rotate(origin, point, a): #runs 22 times per frame
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x, y = point - origin
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cosa = math.cos(-a)
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sina = math.sin(-a)
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qx = origin[0] + cosa * x - sina * y
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qy = origin[1] + sina * x + cosa * y
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return qx, qy
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def clamp (value, minimum, maxium):
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return max(min(value,maxium),minimum)
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def rotate_image(image, a, center): #twice per frame, 0.2ms - 0.25ms each, improved to 0.15ms - 0.25ms
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M = cv2.getRotationMatrix2D(center, math.degrees(a), 1.0)
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(h, w) = image.shape[:2]
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image = cv2.warpAffine(image, M, (w, h), flags = cv2.INTER_CUBIC)
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return image
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class EyeTracker():
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def __init__(self):
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self.last_eye_state = [[0.,0.],[0.,0.]]
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self.average_eye_confidence = [1.0,1.0]
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self.average_eye_confidence_variance = [0.0,0.0]
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self.eye_std_dev = [0.0,0.0]
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self.eye_tracking_frames = 0
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self.mean = np.float32(np.array([-2.1179, -2.0357, -1.8044]))
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self.std = np.float32(np.array([0.0171, 0.0175, 0.0174]))
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model_base_path = os.path.join(os.path.dirname(__file__), os.path.join("models"))
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providersList = onnxruntime.capi._pybind_state.get_available_providers()
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options = onnxruntime.SessionOptions()
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options.inter_op_num_threads = 1
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options.intra_op_num_threads = 1
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options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
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options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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options.log_severity_level = 3
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self.gaze_model = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_gaze32_split_opt.onnx"), sess_options=options, providers=providersList)
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def get_eye_state(self,face_frame, lms, offset):
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eye_state = [[1.,0.,0.,0.],[1.,0.,0.,0.]] #Array constructed early so it can be used for an early exit
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self.eye_tracking_frames+=1
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#I've tried to thread this because they don't need to happen in sequence
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#but it doesn't do any good because python threads kinda suck
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#I'd need a persistent right eye and left eye process and that feels excessive
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right_eye, REyeInfo = self.prepare_eye(face_frame, lms[36,0:2], lms[39,0:2], False)
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left_eye, LEyeInfo = self.prepare_eye(face_frame,lms[42,0:2], lms[45,0:2], True)
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if right_eye is None or left_eye is None:
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return eye_state #Early exit if one of the eyes doesn't have data
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both_eyes = np.concatenate((right_eye, left_eye))
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results = self.gaze_model.run([], {"input": both_eyes})[0]
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eye_state[0] = self.calculateEye(results[0], REyeInfo, offset,0)
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eye_state[1] = self.calculateEye(results[1], LEyeInfo, offset,1)
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return eye_state
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def calculateStandardDeviation(self, conf, eye):
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avgRatio = 1/self.eye_tracking_frames
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self.average_eye_confidence[eye] = (self.average_eye_confidence[eye]*(1-avgRatio))+ (conf * avgRatio)
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avgRatio = 1/(self.eye_tracking_frames + 1)
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self.average_eye_confidence_variance[eye] = pow(((conf - self.average_eye_confidence[eye]) * avgRatio) + (self.average_eye_confidence_variance[eye] * (1-avgRatio)), 2)
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self.eye_std_dev[eye] = math.sqrt(self.average_eye_confidence_variance[eye])
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def calculateEye(self, results, eye_info , offset, eye):
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e_x, e_y, scale, reference, angles = eye_info
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confidenceThreshold = self.average_eye_confidence[eye] - 2 * self.eye_std_dev[eye]
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m = results[0].argmax()
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x = m // 8
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y = m % 8
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p=results[1][x, y]
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p = clamp(p, 0.00001, 0.9999)
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off_x = math.log(p/(1-p))
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eye_x = 4.0 *(x + off_x)
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p=results[2][x, y]
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p = clamp(p, 0.00001, 0.9999)
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off_y = math.log(p/(1-p))
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eye_y = 4.0 * (y + off_y)
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#I'm proud of this
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#it's my "anti-Cali" functionality
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#if eye movements are below 2 standard deviations of the average the movement is severely limited
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#the eyes are handled independenly because my testing showed that one eye tended to have a higher condifence than the other
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limit = max((results[0][x,y]- 0.5)*(2/confidenceThreshold),0)
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if results[0][x,y] < confidenceThreshold:
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Delta = eye_y - self.last_eye_state[eye][0]
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eye_y = self.last_eye_state[eye][0] + clamp(Delta, -limit, limit)
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Delta = eye_x - self.last_eye_state[eye][1]
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eye_x = self.last_eye_state[eye][1] + clamp(Delta, -limit, limit)
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self.last_eye_state[eye] = [eye_y, eye_x]
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if eye == 1:
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eye_x = (32. - eye_x)
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eye_x = e_x + scale[0] * eye_x
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eye_y = e_y + scale[1] * eye_y
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eye_x, eye_y = rotate(reference, (eye_x, eye_y), -angles)
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eye_x, eye_y = (eye_x, eye_y) + offset
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self.calculateStandardDeviation(results[0][x,y], eye)
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return [1.0, eye_y, eye_x, results[0][x,y]]
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def prepare_eye(self,im, outer_pt, inner_pt, flip):
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(x1, y1), (x2, y2), center, reference, a = self.corners_to_eye((outer_pt, inner_pt), im.shape[1], im.shape[0])
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#rotating an image is expensive and reduces clarity
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#so I just don't if it's a relatively small angle
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if math.degrees(a) > 7.5 and math.degrees(a) < 352.5:
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im = rotate_image(im, a, reference)
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im = im[int(y1):int(y2), int(x1):int(x2)]
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if np.prod(im.shape) < 1:
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return None, None, None, None, None, None
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if flip:
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im = cv2.flip(im, 1)
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scale = [(x2 - x1)/ 32., (y2 - y1)/ 32.]
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im = cv2.resize(im, (32, 32), interpolation=cv2.INTER_CUBIC)
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im = im.astype(np.float32) * self.std + self.mean
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im = np.expand_dims(im, 0)
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im = np.transpose(im, (0,3,2,1))
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return [im, [x1, y1, scale, reference, a]]
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def corners_to_eye(self, corners, w, h):
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c1, c2 = np.array(corners, dtype=float)
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a = math.atan2(*(c2 - c1)[::-1]) % (2*math.pi)
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c2 = rotate(c1, c2, a)
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center = (c1 + c2) / 2.0
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radius = np.linalg.norm(c1 - c2)
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radius = [radius * 0.7, radius * 0.6]
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upper_left = clamp_to_im(center - radius, w, h)
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lower_right = clamp_to_im(center + radius, w, h)
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return upper_left, lower_right, center, c1, a
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