1120 lines
49 KiB
Python
1120 lines
49 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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import queue
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import threading
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import copy
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from retinaface import RetinaFaceDetector
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from remedian import remedian
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def resolve(name):
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f = os.path.join(os.path.dirname(__file__), name)
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return f
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def clamp_to_im(pt, w, h):
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x = pt[0]
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y = pt[1]
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if x < 0:
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x = 0
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if y < 0:
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y = 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):
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a = -a
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ox, oy = origin
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px, py = point
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qx = ox + math.cos(a) * (px - ox) - math.sin(a) * (py - oy)
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qy = oy + math.sin(a) * (px - ox) + math.cos(a) * (py - oy)
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return qx, qy
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def angle(p1, p2):
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p1 = np.array(p1)
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p2 = np.array(p2)
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a = np.arctan2(*(p2 - p1)[::-1])
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return (a % (2 * np.pi))
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def compensate(p1, p2):
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a = angle(p1, p2)
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return rotate(p1, p2, a), a
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def rotate_image(image, a, center):
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(h, w) = image.shape[:2]
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a = np.rad2deg(a)
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M = cv2.getRotationMatrix2D((center[0], center[1]), a, 1.0)
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rotated = cv2.warpAffine(image, M, (w, h))
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return rotated
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def intersects(r1, r2, amount=0.3):
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area1 = r1[2] * r1[3]
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area2 = r2[2] * r2[3]
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inter = 0.0
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total = area1 + area2
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r1_x1, r1_y1, w, h = r1
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r1_x2 = r1_x1 + w
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r1_y2 = r1_y1 + h
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r2_x1, r2_y1, w, h = r2
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r2_x2 = r2_x1 + w
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r2_y2 = r2_y1 + h
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left = max(r1_x1, r2_x1)
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right = min(r1_x2, r2_x2)
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top = max(r1_y1, r2_y1)
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bottom = min(r1_y2, r2_y2)
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if left < right and top < bottom:
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inter = (right - left) * (bottom - top)
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total -= inter
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if inter / total >= amount:
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return True
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return False
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#return not (r1_x1 > r2_x2 or r1_x2 < r2_x1 or r1_y1 > r2_y2 or r1_y2 < r2_y1)
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def group_rects(rects):
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rect_groups = {}
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for rect in rects:
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rect_groups[str(rect)] = [-1, -1, []]
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group_id = 0
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for i, rect in enumerate(rects):
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name = str(rect)
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group = group_id
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group_id += 1
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if rect_groups[name][0] < 0:
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rect_groups[name] = [group, -1, []]
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else:
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group = rect_groups[name][0]
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for j, other_rect in enumerate(rects):
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if i == j:
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continue;
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inter = intersects(rect, other_rect)
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if intersects(rect, other_rect):
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rect_groups[str(other_rect)] = [group, -1, []]
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return rect_groups
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def logit(p, factor=16.0):
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if p >= 1.0:
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p = 0.9999999
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if p <= 0.0:
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p = 0.0000001
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p = p/(1-p)
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return float(np.log(p)) / float(factor)
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def logit_arr(p, factor=16.0):
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p = np.clip(p, 0.0000001, 0.9999999)
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return np.log(p / (1 - p)) / float(factor)
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def matrix_to_quaternion(m):
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t = 0.0
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q = [0.0, 0.0, 0, 0.0]
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if m[2,2] < 0:
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if m[0,0] > m[1,1]:
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t = 1 + m[0,0] - m[1,1] - m[2,2]
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q = [t, m[0,1]+m[1,0], m[2,0]+m[0,2], m[1,2]-m[2,1]]
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else:
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t = 1 - m[0,0] + m[1,1] - m[2,2]
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q = [m[0,1]+m[1,0], t, m[1,2]+m[2,1], m[2,0]-m[0,2]]
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else:
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if m[0,0] < -m[1,1]:
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t = 1 - m[0,0] - m[1,1] + m[2,2]
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q = [m[2,0]+m[0,2], m[1,2]+m[2,1], t, m[0,1]-m[1,0]]
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else:
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t = 1 + m[0,0] + m[1,1] + m[2,2]
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q = [m[1,2]-m[2,1], m[2,0]-m[0,2], m[0,1]-m[1,0], t]
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q = np.array(q, np.float32) * 0.5 / np.sqrt(t)
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return q
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def worker_thread(session, frame, input, crop_info, queue, input_name, idx, tracker):
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output = session.run([], {input_name: input})[0]
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conf, lms = tracker.landmarks(output[0], crop_info)
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if conf > tracker.threshold:
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eye_state = tracker.get_eye_state(frame, lms, single=True)
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queue.put((session, conf, (lms, eye_state), crop_info, idx))
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else:
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queue.put((session,))
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class Feature():
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def __init__(self, threshold=0.15, alpha=0.2, hard_factor=0.15, decay=0.001, max_feature_updates=0):
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self.median = remedian()
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self.min = None
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self.max = None
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self.hard_min = None
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self.hard_max = None
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self.threshold = threshold
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self.alpha = alpha
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self.hard_factor = hard_factor
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self.decay = decay
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self.last = 0
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self.current_median = 0
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self.update_count = 0
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self.max_feature_updates = max_feature_updates
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self.first_seen = -1
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self.updating = True
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def update(self, x, now=0):
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if self.max_feature_updates > 0:
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if self.first_seen == -1:
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self.first_seen = now;
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new = self.update_state(x, now=now)
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filtered = self.last * self.alpha + new * (1 - self.alpha)
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self.last = filtered
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return filtered
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def update_state(self, x, now=0):
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updating = self.updating and (self.max_feature_updates == 0 or now - self.first_seen < self.max_feature_updates)
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if updating:
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self.median + x
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self.current_median = self.median.median()
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else:
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self.updating = False
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median = self.current_median
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if self.min is None:
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if x < median and (median - x) / median > self.threshold:
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if updating:
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self.min = x
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self.hard_min = self.min + self.hard_factor * (median - self.min)
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return -1
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return 0
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else:
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if x < self.min:
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if updating:
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self.min = x
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self.hard_min = self.min + self.hard_factor * (median - self.min)
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return -1
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if self.max is None:
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if x > median and (x - median) / median > self.threshold:
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if updating:
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self.max = x
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self.hard_max = self.max - self.hard_factor * (self.max - median)
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return 1
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return 0
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else:
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if x > self.max:
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if updating:
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self.max = x
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self.hard_max = self.max - self.hard_factor * (self.max - median)
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return 1
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if updating:
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if self.min < self.hard_min:
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self.min = self.hard_min * self.decay + self.min * (1 - self.decay)
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if self.max > self.hard_max:
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self.max = self.hard_max * self.decay + self.max * (1 - self.decay)
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if x < median:
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return - (1 - (x - self.min) / (median - self.min))
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elif x > median:
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return (x - median) / (self.max - median)
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return 0
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class FeatureExtractor():
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def __init__(self, max_feature_updates=0):
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self.eye_l = Feature(max_feature_updates=max_feature_updates)
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self.eye_r = Feature(max_feature_updates=max_feature_updates)
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self.eyebrow_updown_l = Feature(max_feature_updates=max_feature_updates)
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self.eyebrow_updown_r = Feature(max_feature_updates=max_feature_updates)
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self.eyebrow_quirk_l = Feature(threshold=0.05, max_feature_updates=max_feature_updates)
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self.eyebrow_quirk_r = Feature(threshold=0.05, max_feature_updates=max_feature_updates)
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self.eyebrow_steepness_l = Feature(threshold=0.05, max_feature_updates=max_feature_updates)
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self.eyebrow_steepness_r = Feature(threshold=0.05, max_feature_updates=max_feature_updates)
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self.mouth_corner_updown_l = Feature(max_feature_updates=max_feature_updates)
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self.mouth_corner_updown_r = Feature(max_feature_updates=max_feature_updates)
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self.mouth_corner_inout_l = Feature(threshold=0.02, max_feature_updates=max_feature_updates)
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self.mouth_corner_inout_r = Feature(threshold=0.02, max_feature_updates=max_feature_updates)
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self.mouth_open = Feature(max_feature_updates=max_feature_updates)
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self.mouth_wide = Feature(threshold=0.02, max_feature_updates=max_feature_updates)
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def align_points(self, a, b, pts):
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a = tuple(a)
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b = tuple(b)
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alpha = angle(a, b)
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alpha = np.rad2deg(alpha)
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if alpha >= 90:
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alpha = - (alpha - 180)
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if alpha <= -90:
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alpha = - (alpha + 180)
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alpha = np.deg2rad(alpha)
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aligned_pts = []
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for pt in pts:
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aligned_pts.append(np.array(rotate(a, pt, alpha)))
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return alpha, np.array(aligned_pts)
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def update(self, pts):
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features = {}
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now = time.perf_counter()
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norm_distance_x = np.mean([pts[0, 0] - pts[16, 0], pts[1, 0] - pts[15, 0]])
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norm_distance_y = np.mean([pts[27, 1] - pts[28, 1], pts[28, 1] - pts[29, 1], pts[29, 1] - pts[30, 1]])
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a1, f_pts = self.align_points(pts[42], pts[45], pts[[43, 44, 47, 46]])
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f = np.clip((np.mean([f_pts[0,1], f_pts[1,1]]) - np.mean([f_pts[2,1], f_pts[3,1]])) / norm_distance_y, 0, None)
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features["eye_l"] = self.eye_l.update(f, now)
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a2, f_pts = self.align_points(pts[36], pts[39], pts[[37, 38, 41, 40]])
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f = np.clip((np.mean([f_pts[0,1], f_pts[1,1]]) - np.mean([f_pts[2,1], f_pts[3,1]])) / norm_distance_y, 0, None)
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features["eye_r"] = self.eye_r.update(f, now)
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a3, _ = self.align_points(pts[0], pts[16], [])
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a4, _ = self.align_points(pts[31], pts[35], [])
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norm_angle = np.mean(list(map(np.rad2deg, [a1, a2, a3, a4])))
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a, f_pts = self.align_points(pts[22], pts[26], pts[[22, 23, 24, 25, 26]])
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features["eyebrow_steepness_l"] = self.eyebrow_steepness_l.update(-np.rad2deg(a) - norm_angle, now)
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f = (np.mean([pts[22, 1], pts[26, 1]]) - pts[27, 1]) / norm_distance_y
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features["eyebrow_updown_l"] = self.eyebrow_updown_l.update(f, now)
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f = np.max(np.abs(np.array(f_pts[1:4]) - f_pts[0, 1])) / norm_distance_y
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features["eyebrow_quirk_l"] = self.eyebrow_quirk_l.update(f, now)
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a, f_pts = self.align_points(pts[17], pts[21], pts[[17, 18, 19, 20, 21]])
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features["eyebrow_steepness_r"] = self.eyebrow_steepness_r.update(np.rad2deg(a) - norm_angle, now)
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f = (np.mean([pts[17, 1], pts[21, 1]]) - pts[27, 1]) / norm_distance_y
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features["eyebrow_updown_r"] = self.eyebrow_updown_r.update(f, now)
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f = np.max(np.abs(np.array(f_pts[1:4]) - f_pts[0, 1])) / norm_distance_y
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features["eyebrow_quirk_r"] = self.eyebrow_quirk_r.update(f, now)
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upper_mouth_line = np.mean([pts[49, 1], pts[50, 1], pts[51, 1]])
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center_line = np.mean([pts[50, 0], pts[60, 0], pts[27, 0], pts[30, 0], pts[64, 0], pts[55, 0]])
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f = (upper_mouth_line - pts[62, 1]) / norm_distance_y
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features["mouth_corner_updown_l"] = self.mouth_corner_updown_l.update(f, now)
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f = abs(center_line - pts[62, 0]) / norm_distance_x
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features["mouth_corner_inout_l"] = self.mouth_corner_inout_l.update(f, now)
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f = (upper_mouth_line - pts[58, 1]) / norm_distance_y
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features["mouth_corner_updown_r"] = self.mouth_corner_updown_r.update(f, now)
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f = abs(center_line - pts[58, 0]) / norm_distance_x
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features["mouth_corner_inout_r"] = self.mouth_corner_inout_r.update(f, now)
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f = (np.mean([pts[59, 1], pts[60, 1], pts[61, 1]]) - np.mean([pts[65, 1], pts[64, 1], pts[63, 1]])) / norm_distance_y
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features["mouth_open"] = self.mouth_open.update(f, now)
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f = abs(pts[58, 0] - pts[62, 0]) / norm_distance_x
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features["mouth_wide"] = self.mouth_wide.update(f, now)
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return features
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class FaceInfo():
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def __init__(self, id, tracker):
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self.id = id
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self.frame_count = -1
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self.tracker = tracker
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self.contour_pts = [0,1,8,15,16,27,28,29,30,31,32,33,34,35,36,39,42,45]
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self.face_3d = copy.copy(self.tracker.face_3d)
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self.reset()
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self.alive = False
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self.coord = None
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self.base_scale_v = self.tracker.face_3d[27:30, 1] - self.tracker.face_3d[28:31, 1]
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self.base_scale_h = np.abs(self.tracker.face_3d[[0, 36, 42], 0] - self.tracker.face_3d[[16, 39, 45], 0])
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self.limit_3d_adjustment = True
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self.update_count_delta = 75.
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self.update_count_max = 7500.
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if self.tracker.max_feature_updates > 0:
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self.features = FeatureExtractor(self.tracker.max_feature_updates)
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def reset(self):
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self.alive = False
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self.conf = None
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self.lms = None
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self.eye_state = None
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self.rotation = None
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self.translation = None
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self.success = None
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self.quaternion = None
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self.euler = None
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self.pnp_error = None
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self.pts_3d = None
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self.eye_blink = None
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self.bbox = None
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self.pnp_error = 0
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if self.tracker.max_feature_updates < 1:
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self.features = FeatureExtractor(0)
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self.current_features = {}
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self.contour = np.zeros((21,3))
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self.update_counts = np.zeros((66,2))
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self.update_contour()
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self.fail_count = 0
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def update(self, result, coord, frame_count):
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self.frame_count = frame_count
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if result is None:
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self.reset()
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else:
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self.conf, (self.lms, self.eye_state) = result
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self.coord = coord
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self.alive = True
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def update_contour(self):
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self.contour = np.array(self.face_3d[self.contour_pts])
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def normalize_pts3d(self, pts_3d):
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# Calculate angle using nose
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pts_3d[:, 0:2] -= pts_3d[30, 0:2]
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alpha = angle(pts_3d[30, 0:2], pts_3d[27, 0:2])
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alpha -= np.deg2rad(90)
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R = np.matrix([[np.cos(alpha), -np.sin(alpha)], [np.sin(alpha), np.cos(alpha)]])
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pts_3d[:, 0:2] = (pts_3d - pts_3d[30])[:, 0:2].dot(R) + pts_3d[30, 0:2]
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# Vertical scale
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pts_3d[:, 1] /= np.mean((pts_3d[27:30, 1] - pts_3d[28:31, 1]) / self.base_scale_v)
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# Horizontal scale
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pts_3d[:, 0] /= np.mean(np.abs(pts_3d[[0, 36, 42], 0] - pts_3d[[16, 39, 45], 0]) / self.base_scale_h)
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return pts_3d
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def adjust_3d(self):
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if self.conf < 0.4 or self.pnp_error > 300:
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return
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if not self.tracker.static_model:
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max_runs = 1
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eligible = np.delete(np.arange(0, 66), [30])
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changed_any = False
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update_type = -1
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d_o = np.ones((66,))
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d_c = np.ones((66,))
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for runs in range(max_runs):
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r = 1.0 + np.random.random_sample((66,3)) * 0.02 - 0.01
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r[30, :] = 1.0
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if self.euler[0] > -165 and self.euler[0] < 145:
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continue
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elif self.euler[1] > -10 and self.euler[1] < 20:
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r[:, 2] = 1.0
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update_type = 0
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else:
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r[:, 0:2] = 1.0
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if self.euler[2] > 120 or self.euler[2] < 60:
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continue
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# Enable only one side of the points, depending on direction
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elif self.euler[1] < -10:
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update_type = 1
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r[[0, 1, 2, 3, 4, 5, 6, 7, 17, 18, 19, 20, 21, 31, 32, 36, 37, 38, 39, 40, 41, 48, 49, 56, 57, 58, 59, 65], 2] = 1.0
|
|
eligible = [8, 9, 10, 11, 12, 13, 14, 15, 16, 22, 23, 24, 25, 26, 27, 28, 29, 33, 34, 35, 42, 43, 44, 45, 46, 47, 50, 51, 52, 53, 54, 55, 60, 61, 62, 63, 64]
|
|
else:
|
|
update_type = 1
|
|
r[[9, 10, 11, 12, 13, 14, 15, 16, 22, 23, 24, 25, 26, 34, 35, 42, 43, 44, 45, 46, 47, 51, 52, 53, 54, 61, 62, 63], 2] = 1.0
|
|
eligible = [0, 1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 21, 27, 28, 29, 31, 32, 33, 36, 37, 38, 39, 40, 41, 48, 49, 50, 55, 56, 57, 58, 59, 60, 64, 65]
|
|
|
|
if self.limit_3d_adjustment:
|
|
eligible = np.nonzero(self.update_counts[:, update_type] < self.update_counts[:, abs(update_type - 1)] + self.update_count_delta)[0]
|
|
if eligible.shape[0] <= 0:
|
|
break
|
|
|
|
if runs == 0:
|
|
updated = copy.copy(self.face_3d[0:66])
|
|
o_projected = np.ones((66,2))
|
|
o_projected[eligible] = np.squeeze(np.array(cv2.projectPoints(self.face_3d[eligible], self.rotation, self.translation, self.tracker.camera, self.tracker.dist_coeffs)[0]), 1)
|
|
c = updated * r
|
|
c_projected = np.zeros((66,2))
|
|
c_projected[eligible] = np.squeeze(np.array(cv2.projectPoints(c[eligible], self.rotation, self.translation, self.tracker.camera, self.tracker.dist_coeffs)[0]), 1)
|
|
changed = False
|
|
|
|
d_o[eligible] = np.linalg.norm(o_projected[eligible] - self.lms[eligible, 0:2], axis=1)
|
|
d_c[eligible] = np.linalg.norm(c_projected[eligible] - self.lms[eligible, 0:2], axis=1)
|
|
indices = np.nonzero(d_c < d_o)[0]
|
|
if indices.shape[0] > 0:
|
|
if self.limit_3d_adjustment:
|
|
indices = np.intersect1d(indices, eligible)
|
|
if indices.shape[0] > 0:
|
|
self.update_counts[indices, update_type] += 1
|
|
updated[indices] = c[indices]
|
|
o_projected[indices] = c_projected[indices]
|
|
changed = True
|
|
changed_any = changed_any or changed
|
|
|
|
if not changed:
|
|
break
|
|
|
|
if changed_any:
|
|
# Update weighted by point confidence
|
|
weights = np.zeros((66,3))
|
|
weights[:, :] = self.lms[0:66, 2:3]
|
|
weights[weights > 0.7] = 1.0
|
|
weights = 1.0 - weights
|
|
update_indices = np.arange(0, 66)
|
|
if self.limit_3d_adjustment:
|
|
update_indices = np.nonzero(self.update_counts[:, update_type] <= self.update_count_max)[0]
|
|
self.face_3d[update_indices] = self.face_3d[update_indices] * weights[update_indices] + updated[update_indices] * (1. - weights[update_indices])
|
|
self.update_contour()
|
|
|
|
self.pts_3d = self.normalize_pts3d(self.pts_3d)
|
|
self.current_features = self.features.update(self.pts_3d[:, 0:2])
|
|
self.eye_blink = []
|
|
self.eye_blink.append(1 - min(max(0, -self.current_features["eye_r"]), 1))
|
|
self.eye_blink.append(1 - min(max(0, -self.current_features["eye_l"]), 1))
|
|
|
|
|
|
class Tracker():
|
|
def __init__(self, width, height, model_type=3, threshold=0.6, max_faces=1, discard_after=5, scan_every=3, bbox_growth=0.0, max_threads=4, silent=False, model_dir=None, no_gaze=False, use_retinaface=False, max_feature_updates=0, static_model=False):
|
|
options = onnxruntime.SessionOptions()
|
|
options.inter_op_num_threads = 1
|
|
options.intra_op_num_threads = max(max_threads,4)
|
|
options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
|
|
options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
|
|
options.log_severity_level = 3
|
|
self.model_type = model_type
|
|
self.models = [
|
|
"mnv3_opt_very_fast.onnx",
|
|
"mnv3_opt_fast.onnx",
|
|
"mnv3_opt_medium.onnx",
|
|
"mnv3_opt_b.onnx"
|
|
]
|
|
model = self.models[self.model_type]
|
|
model_base_path = resolve(os.path.join("models"))
|
|
if model_dir is None:
|
|
if not os.path.exists(model_base_path):
|
|
model_base_path = resolve(os.path.join("..", "models"))
|
|
else:
|
|
model_base_path = model_dir
|
|
|
|
self.retinaface = RetinaFaceDetector(model_path=os.path.join(model_base_path, "retinaface_640x640_opt.onnx"), json_path=os.path.join(model_base_path, "priorbox_640x640.json"), threads=max(max_threads,4), top_k=max_faces, res=(640, 640))
|
|
self.retinaface_scan = RetinaFaceDetector(model_path=os.path.join(model_base_path, "retinaface_640x640_opt.onnx"), json_path=os.path.join(model_base_path, "priorbox_640x640.json"), threads=2, top_k=max_faces, res=(640, 640))
|
|
self.use_retinaface = use_retinaface
|
|
|
|
# Single face instance with multiple threads
|
|
self.session = onnxruntime.InferenceSession(os.path.join(model_base_path, model), sess_options=options)
|
|
|
|
# Multiple faces with single threads
|
|
self.sessions = []
|
|
self.max_workers = min(max_threads, max_faces)
|
|
extra_threads = max_threads % self.max_workers
|
|
for i in range(self.max_workers):
|
|
options = onnxruntime.SessionOptions()
|
|
options.inter_op_num_threads = 1
|
|
options.intra_op_num_threads = max_threads // self.max_workers
|
|
if options.intra_op_num_threads < 1:
|
|
options.intra_op_num_threads = 1
|
|
elif i < extra_threads:
|
|
options.intra_op_num_threads += 1
|
|
options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
|
|
options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
|
|
self.sessions.append(onnxruntime.InferenceSession(os.path.join(model_base_path, model), sess_options=options))
|
|
self.input_name = self.session.get_inputs()[0].name
|
|
|
|
options = onnxruntime.SessionOptions()
|
|
options.inter_op_num_threads = 1
|
|
options.intra_op_num_threads = max(max_threads,4)
|
|
options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
|
|
options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
|
|
options.log_severity_level = 3
|
|
self.gaze_model = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_gaze32_split_opt.onnx"), sess_options=options)
|
|
options.intra_op_num_threads = 1
|
|
self.gaze_model_single = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_gaze32_split_opt.onnx"), sess_options=options)
|
|
|
|
self.detection = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_detection_opt.onnx"), sess_options=options)
|
|
self.faces = []
|
|
|
|
# Image normalization constants
|
|
self.mean = np.float32(np.array([0.485, 0.456, 0.406]))
|
|
self.std = np.float32(np.array([0.229, 0.224, 0.225]))
|
|
self.mean = self.mean / self.std
|
|
self.std = self.std * 255.0
|
|
|
|
# PnP solving
|
|
self.face_3d = np.array([
|
|
[ 0.4551769692672 , 0.300895790030204, -0.764429433974752],
|
|
[ 0.448998827123556, 0.166995837790733, -0.765143004071253],
|
|
[ 0.437431554952677, 0.022655479179981, -0.739267175112735],
|
|
[ 0.415033422928434, -0.088941454648772, -0.747947437846473],
|
|
[ 0.389123587370091, -0.232380029794684, -0.704788385327458],
|
|
[ 0.334630113904382, -0.361265387599081, -0.615587579236862],
|
|
[ 0.263725112132858, -0.460009725616771, -0.491479221041573],
|
|
[ 0.16241621322721 , -0.558037146073869, -0.339445180872282],
|
|
[ 0. , -0.621079019321682, -0.287294770748887],
|
|
[-0.16241621322721 , -0.558037146073869, -0.339445180872282],
|
|
[-0.263725112132858, -0.460009725616771, -0.491479221041573],
|
|
[-0.334630113904382, -0.361265387599081, -0.615587579236862],
|
|
[-0.389123587370091, -0.232380029794684, -0.704788385327458],
|
|
[-0.415033422928434, -0.088941454648772, -0.747947437846473],
|
|
[-0.437431554952677, 0.022655479179981, -0.739267175112735],
|
|
[-0.448998827123556, 0.166995837790733, -0.765143004071253],
|
|
[-0.4551769692672 , 0.300895790030204, -0.764429433974752],
|
|
[ 0.385529968662985, 0.402800553948697, -0.310031082540741],
|
|
[ 0.322196658344302, 0.464439136821772, -0.250558059367669],
|
|
[ 0.25409760441282 , 0.46420381416882 , -0.208177722146526],
|
|
[ 0.186875436782135, 0.44706071961879 , -0.145299823706503],
|
|
[ 0.120880983543622, 0.423566314072968, -0.110757158774771],
|
|
[-0.120880983543622, 0.423566314072968, -0.110757158774771],
|
|
[-0.186875436782135, 0.44706071961879 , -0.145299823706503],
|
|
[-0.25409760441282 , 0.46420381416882 , -0.208177722146526],
|
|
[-0.322196658344302, 0.464439136821772, -0.250558059367669],
|
|
[-0.385529968662985, 0.402800553948697, -0.310031082540741],
|
|
[ 0. , 0.293332603215811, -0.137582088779393],
|
|
[ 0. , 0.194828701837823, -0.069158109325951],
|
|
[ 0. , 0.103844017393155, -0.009151819844964],
|
|
[ 0. , 0. , 0. ],
|
|
[ 0.080626352317973, -0.041276068128093, -0.134161035564826],
|
|
[ 0.046439347377934, -0.057675223874769, -0.102990627164664],
|
|
[ 0. , -0.068753126205604, -0.090545348482397],
|
|
[-0.046439347377934, -0.057675223874769, -0.102990627164664],
|
|
[-0.080626352317973, -0.041276068128093, -0.134161035564826],
|
|
[ 0.315905195966084, 0.298337502555443, -0.285107407636464],
|
|
[ 0.275252345439353, 0.312721904921771, -0.244558251170671],
|
|
[ 0.176394511553111, 0.311907184376107, -0.219205360345231],
|
|
[ 0.131229723798772, 0.284447361805627, -0.234239149487417],
|
|
[ 0.184124948330084, 0.260179585304867, -0.226590776513707],
|
|
[ 0.279433549294448, 0.267363071770222, -0.248441437111633],
|
|
[-0.131229723798772, 0.284447361805627, -0.234239149487417],
|
|
[-0.176394511553111, 0.311907184376107, -0.219205360345231],
|
|
[-0.275252345439353, 0.312721904921771, -0.244558251170671],
|
|
[-0.315905195966084, 0.298337502555443, -0.285107407636464],
|
|
[-0.279433549294448, 0.267363071770222, -0.248441437111633],
|
|
[-0.184124948330084, 0.260179585304867, -0.226590776513707],
|
|
[ 0.121155252430729, -0.208988660580347, -0.160606287940521],
|
|
[ 0.041356305910044, -0.194484199722098, -0.096159882202821],
|
|
[ 0. , -0.205180167345702, -0.083299217789729],
|
|
[-0.041356305910044, -0.194484199722098, -0.096159882202821],
|
|
[-0.121155252430729, -0.208988660580347, -0.160606287940521],
|
|
[-0.132325402795928, -0.290857984604968, -0.187067868218105],
|
|
[-0.064137791831655, -0.325377847425684, -0.158924039726607],
|
|
[ 0. , -0.343742581679188, -0.113925986025684],
|
|
[ 0.064137791831655, -0.325377847425684, -0.158924039726607],
|
|
[ 0.132325402795928, -0.290857984604968, -0.187067868218105],
|
|
[ 0.181481567104525, -0.243239316141725, -0.231284988892766],
|
|
[ 0.083999507750469, -0.239717753728704, -0.155256465640701],
|
|
[ 0. , -0.256058040176369, -0.0950619498899 ],
|
|
[-0.083999507750469, -0.239717753728704, -0.155256465640701],
|
|
[-0.181481567104525, -0.243239316141725, -0.231284988892766],
|
|
[-0.074036069749345, -0.250689938345682, -0.177346470406188],
|
|
[ 0. , -0.264945854681568, -0.112349967428413],
|
|
[ 0.074036069749345, -0.250689938345682, -0.177346470406188],
|
|
# Pupils and eyeball centers
|
|
[ 0.257990002632141, 0.276080012321472, -0.219998998939991],
|
|
[-0.257990002632141, 0.276080012321472, -0.219998998939991],
|
|
[ 0.257990002632141, 0.276080012321472, -0.324570998549461],
|
|
[-0.257990002632141, 0.276080012321472, -0.324570998549461]
|
|
], np.float32)
|
|
|
|
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.dist_coeffs = np.zeros((4,1))
|
|
|
|
self.frame_count = 0
|
|
self.width = width
|
|
self.height = height
|
|
self.threshold = threshold
|
|
self.max_faces = max_faces
|
|
self.max_threads = max_threads
|
|
self.discard = 0
|
|
self.discard_after = discard_after
|
|
self.detected = 0
|
|
self.wait_count = 0
|
|
self.scan_every = scan_every
|
|
self.bbox_growth = bbox_growth
|
|
self.silent = silent
|
|
|
|
self.res = 224.
|
|
if model_type < 0:
|
|
self.res = 56.
|
|
self.res_i = int(self.res)
|
|
self.out_res = 27.
|
|
if model_type < 0:
|
|
self.out_res = 13.
|
|
self.out_res_i = int(self.out_res) + 1
|
|
self.logit_factor = 16.
|
|
if model_type < 0:
|
|
self.logit_factor = 8.
|
|
|
|
self.no_gaze = no_gaze
|
|
self.debug_gaze = False
|
|
self.max_feature_updates = max_feature_updates
|
|
self.static_model = static_model
|
|
self.face_info = [FaceInfo(id, self) for id in range(max_faces)]
|
|
self.fail_count = 0
|
|
|
|
def detect_faces(self, frame):
|
|
im = cv2.resize(frame, (224, 224), interpolation=cv2.INTER_LINEAR)[:,:,::-1] / self.std - self.mean
|
|
im = np.expand_dims(im, 0)
|
|
im = np.transpose(im, (0,3,1,2))
|
|
outputs, maxpool = self.detection.run([], {'input': im})
|
|
outputs = np.array(outputs)
|
|
maxpool = np.array(maxpool)
|
|
outputs[0, 0, outputs[0, 0] != maxpool[0, 0]] = 0
|
|
detections = np.flip(np.argsort(outputs[0,0].flatten()))
|
|
results = []
|
|
for det in detections[0:self.max_faces]:
|
|
y, x = det // 56, det % 56
|
|
c = outputs[0, 0, y, x]
|
|
r = outputs[0, 1, y, x] * 112.
|
|
x *= 4
|
|
y *= 4
|
|
r *= 1.0
|
|
if c < self.threshold:
|
|
break
|
|
results.append((x - r, y - r, 2 * r, 2 * r * 1.0))
|
|
results = np.array(results).astype(np.float32)
|
|
if results.shape[0] > 0:
|
|
results[:, [0,2]] *= frame.shape[1] / 224.
|
|
results[:, [1,3]] *= frame.shape[0] / 224.
|
|
return results
|
|
|
|
def landmarks(self, tensor, crop_info):
|
|
crop_x1, crop_y1, scale_x, scale_y, _ = crop_info
|
|
avg_conf = 0
|
|
res = self.res - 1
|
|
t_main = tensor[0:66].reshape((66,self.out_res_i * self.out_res_i))
|
|
t_m = t_main.argmax(1)
|
|
indices = np.expand_dims(t_m, 1)
|
|
t_conf = np.take_along_axis(t_main, indices, 1).reshape((66,))
|
|
t_off_x = np.take_along_axis(tensor[66:132].reshape((66,self.out_res_i * self.out_res_i)), indices, 1).reshape((66,))
|
|
t_off_y = np.take_along_axis(tensor[132:198].reshape((66,self.out_res_i * self.out_res_i)), indices, 1).reshape((66,))
|
|
t_off_x = np.floor(res * logit_arr(t_off_x, self.logit_factor) + 0.5)
|
|
t_off_y = np.floor(res * logit_arr(t_off_y, self.logit_factor) + 0.5)
|
|
t_x = crop_y1 + scale_y * (res * np.floor(t_m / self.out_res_i) / self.out_res + t_off_x)
|
|
t_y = crop_x1 + scale_x * (res * np.floor(np.mod(t_m, self.out_res_i)) / self.out_res + t_off_y)
|
|
avg_conf = np.average(t_conf)
|
|
lms = np.stack([t_x, t_y, t_conf], 1)
|
|
return (avg_conf, np.array(lms))
|
|
|
|
def estimate_depth(self, face_info):
|
|
lms = np.concatenate((face_info.lms, np.array([[face_info.eye_state[0][1], face_info.eye_state[0][2], face_info.eye_state[0][3]], [face_info.eye_state[1][1], face_info.eye_state[1][2], face_info.eye_state[1][3]]], np.float)), 0)
|
|
|
|
image_pts = np.array(lms)[face_info.contour_pts, 0:2]
|
|
|
|
success = False
|
|
if not face_info.rotation is None:
|
|
success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, self.camera, self.dist_coeffs, useExtrinsicGuess=True, rvec=np.transpose(face_info.rotation), tvec=np.transpose(face_info.translation), flags=cv2.SOLVEPNP_ITERATIVE)
|
|
else:
|
|
rvec = np.array([0, 0, 0], np.float32)
|
|
tvec = np.array([0, 0, 0], np.float32)
|
|
success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, self.camera, self.dist_coeffs, useExtrinsicGuess=True, rvec=rvec, tvec=tvec, flags=cv2.SOLVEPNP_ITERATIVE)
|
|
|
|
rotation = face_info.rotation
|
|
translation = face_info.translation
|
|
|
|
pts_3d = np.zeros((70,3), np.float32)
|
|
if not success:
|
|
face_info.rotation = np.array([0.0, 0.0, 0.0], np.float32)
|
|
face_info.translation = np.array([0.0, 0.0, 0.0], np.float32)
|
|
return False, np.zeros(4), np.zeros(3), 99999., pts_3d, lms
|
|
else:
|
|
face_info.rotation = np.transpose(face_info.rotation)
|
|
face_info.translation = np.transpose(face_info.translation)
|
|
|
|
rmat, _ = cv2.Rodrigues(rotation)
|
|
inverse_rotation = np.linalg.inv(rmat)
|
|
t_reference = face_info.face_3d.dot(rmat.transpose())
|
|
t_reference = t_reference + face_info.translation
|
|
t_reference = t_reference.dot(self.camera.transpose())
|
|
t_depth = t_reference[:, 2]
|
|
t_depth[t_depth == 0] = 0.000001
|
|
t_depth_e = np.expand_dims(t_depth[:],1)
|
|
t_reference = t_reference[:] / t_depth_e
|
|
pts_3d[0:66] = np.stack([lms[0:66,0], lms[0:66,1], np.ones((66,))], 1) * t_depth_e[0:66]
|
|
pts_3d[0:66] = (pts_3d[0:66].dot(self.inverse_camera.transpose()) - face_info.translation).dot(inverse_rotation.transpose())
|
|
pnp_error = np.power(lms[0:17,0:2] - t_reference[0:17,0:2], 2).sum()
|
|
pnp_error += np.power(lms[30,0:2] - t_reference[30,0:2], 2).sum()
|
|
for i, pt in enumerate(face_info.face_3d):
|
|
if i == 68:
|
|
# Right eyeball
|
|
# Eyeballs have an average diameter of 12.5mm and and the distance between eye corners is 30-35mm, so a conversion factor of 0.385 can be applied
|
|
eye_center = (pts_3d[36] + pts_3d[39]) / 2.0
|
|
d_corner = np.linalg.norm(pts_3d[36] - pts_3d[39])
|
|
depth = 0.385 * d_corner
|
|
pt_3d = np.array([eye_center[0], eye_center[1], eye_center[2] - depth])
|
|
pts_3d[i] = pt_3d
|
|
continue
|
|
if i == 69:
|
|
# Left eyeball
|
|
eye_center = (pts_3d[42] + pts_3d[45]) / 2.0
|
|
d_corner = np.linalg.norm(pts_3d[42] - pts_3d[45])
|
|
depth = 0.385 * d_corner
|
|
pt_3d = np.array([eye_center[0], eye_center[1], eye_center[2] - depth])
|
|
pts_3d[i] = pt_3d
|
|
continue
|
|
if i == 66:
|
|
d1 = np.linalg.norm(lms[i,0:2] - lms[36,0:2])
|
|
d2 = np.linalg.norm(lms[i,0:2] - lms[39,0:2])
|
|
d = d1 + d2
|
|
pt = (pts_3d[36] * d1 + pts_3d[39] * d2) / d
|
|
if i == 67:
|
|
d1 = np.linalg.norm(lms[i,0:2] - lms[42,0:2])
|
|
d2 = np.linalg.norm(lms[i,0:2] - lms[45,0:2])
|
|
d = d1 + d2
|
|
pt = (pts_3d[42] * d1 + pts_3d[45] * d2) / d
|
|
if i == 66 or i == 67:
|
|
reference = rmat.dot(pt)
|
|
reference = reference + face_info.translation
|
|
reference = self.camera.dot(reference)
|
|
depth = reference[2]
|
|
pt_3d = np.array([lms[i][0] * depth, lms[i][1] * depth, depth], np.float32)
|
|
pt_3d = self.inverse_camera.dot(pt_3d)
|
|
pt_3d = pt_3d - face_info.translation
|
|
pt_3d = inverse_rotation.dot(pt_3d)
|
|
pts_3d[i,:] = pt_3d[:]
|
|
|
|
pnp_error = np.sqrt(pnp_error / (2.0 * image_pts.shape[0]))
|
|
if pnp_error > 300:
|
|
face_info.fail_count += 1
|
|
if face_info.fail_count > 5:
|
|
# Something went wrong with adjusting the 3D model
|
|
if not self.silent:
|
|
print(f"Detected anomaly when 3D fitting face {face_info.id}. Resetting.")
|
|
face_info.face_3d = copy.copy(self.face_3d)
|
|
face_info.rotation = None
|
|
face_info.translation = np.array([0.0, 0.0, 0.0], np.float32)
|
|
face_info.update_counts = np.zeros((66,2))
|
|
face_info.update_contour()
|
|
else:
|
|
face_info.fail_count = 0
|
|
|
|
euler = cv2.RQDecomp3x3(rmat)[0]
|
|
return True, matrix_to_quaternion(rmat), euler, pnp_error, pts_3d, lms
|
|
|
|
def preprocess(self, im, crop):
|
|
x1, y1, x2, y2 = crop
|
|
im = np.float32(im[y1:y2, x1:x2,::-1]) # Crop and BGR to RGB
|
|
im = cv2.resize(im, (self.res_i, self.res_i), interpolation=cv2.INTER_LINEAR) / self.std - self.mean
|
|
#im = cv2.resize(im, (224, 224), interpolation=cv2.INTER_LINEAR) / 255.0
|
|
#im = (im - mean) / std
|
|
im = np.expand_dims(im, 0)
|
|
im = np.transpose(im, (0,3,1,2))
|
|
return im
|
|
|
|
def equalize(self, im):
|
|
im_yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV)
|
|
im_yuv[:,:,0] = cv2.equalizeHist(im_yuv[:,:,0])
|
|
return cv2.cvtColor(im_yuv, cv2.COLOR_YUV2BGR)
|
|
|
|
def corners_to_eye(self, corners, w, h, flip):
|
|
((cx1, cy1), (cx2, cy2)) = corners
|
|
c1 = np.array([cx1, cy1])
|
|
c2 = np.array([cx2, cy2])
|
|
c2, a = compensate(c1, c2)
|
|
center = (c1 + c2) / 2.0
|
|
radius = np.linalg.norm(c1 - c2) / 2.0
|
|
radius = np.array([radius * 1.4, radius * 1.2])
|
|
upper_left = clamp_to_im(center - radius, w, h)
|
|
lower_right = clamp_to_im(center + radius, w, h)
|
|
return upper_left, lower_right, center, radius, c1, a
|
|
|
|
def prepare_eye(self, frame, full_frame, lms, flip):
|
|
outer_pt = tuple(lms[0])
|
|
inner_pt = tuple(lms[1])
|
|
h, w, _ = frame.shape
|
|
(x1, y1), (x2, y2), center, radius, reference, a = self.corners_to_eye((outer_pt, inner_pt), w, h, flip)
|
|
im = rotate_image(frame[:, :, ::], a, reference)
|
|
im = im[int(y1):int(y2), int(x1):int(x2),:]
|
|
if np.prod(im.shape) < 1:
|
|
return None, None, None, None, None, None
|
|
if flip:
|
|
im = cv2.flip(im, 1)
|
|
scale = np.array([(x2 - x1), (y2 - y1)]) / 32.
|
|
im = cv2.resize(im, (32, 32), interpolation=cv2.INTER_LINEAR)
|
|
#im = self.equalize(im)
|
|
if self.debug_gaze:
|
|
if not flip:
|
|
full_frame[0:32, 0:32] = im
|
|
else:
|
|
full_frame[0:32, 32:64] = im
|
|
im = im.astype(np.float32)[:,:,::-1] / self.std - self.mean
|
|
im = np.expand_dims(im, 0)
|
|
im = np.transpose(im, (0,3,2,1))
|
|
return im, x1, y1, scale, reference, a
|
|
|
|
def extract_face(self, frame, lms):
|
|
lms = np.array(lms)[:,0:2][:,::-1]
|
|
x1, y1 = tuple(lms.min(0))
|
|
x2, y2 = tuple(lms.max(0))
|
|
radius_x = 1.2 * (x2 - x1) / 2.0
|
|
radius_y = 1.2 * (y2 - y1) / 2.0
|
|
radius = np.array((radius_x, radius_y))
|
|
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(np.int)
|
|
frame = frame[y1:y2, x1:x2]
|
|
return frame, lms, offset
|
|
|
|
def get_eye_state(self, frame, lms, single=False):
|
|
if self.no_gaze:
|
|
return [(1.0, 0.0, 0.0, 0.0), (1.0, 0.0, 0.0, 0.0)]
|
|
lms = np.array(lms)
|
|
e_x = [0,0]
|
|
e_y = [0,0]
|
|
scale = [0,0]
|
|
reference = [None, None]
|
|
angles = [0, 0]
|
|
face_frame, lms, offset = self.extract_face(frame, lms)
|
|
(right_eye, e_x[0], e_y[0], scale[0], reference[0], angles[0]) = self.prepare_eye(face_frame, frame, np.array([lms[36,0:2], lms[39,0:2]]), False)
|
|
(left_eye, e_x[1], e_y[1], scale[1], reference[1], angles[1]) = self.prepare_eye(face_frame, frame, np.array([lms[42,0:2], lms[45,0:2]]), True)
|
|
if right_eye is None or left_eye is None:
|
|
return [(1.0, 0.0, 0.0, 0.0), (1.0, 0.0, 0.0, 0.0)]
|
|
both_eyes = np.concatenate((right_eye, left_eye))
|
|
results = None
|
|
if single:
|
|
results = self.gaze_model_single.run([], {self.input_name: both_eyes})
|
|
else:
|
|
results = self.gaze_model.run([], {self.input_name: both_eyes})
|
|
open = [0, 0]
|
|
open[0] = 1#results[1][0].argmax()
|
|
open[1] = 1#results[1][1].argmax()
|
|
results = np.array(results[0])
|
|
|
|
eye_state = []
|
|
for i in range(2):
|
|
m = int(results[i][0].argmax())
|
|
x = m // 8
|
|
y = m % 8
|
|
conf = float(results[i][0][x,y])
|
|
|
|
off_x = 32.0 * logit(results[i][1][x, y], 8.0)
|
|
off_y = 32.0 * logit(results[i][2][x, y], 8.0)
|
|
if i == 1:
|
|
eye_x = 32.0 * float(x) / 8.0 + off_x
|
|
else:
|
|
eye_x = 32.0 * float(x) / 8.0 + off_x
|
|
eye_y = 32.0 * float(y) / 8.0 + off_y
|
|
|
|
if self.debug_gaze:
|
|
if i == 0:
|
|
frame[int(eye_y), int(eye_x)] = (0, 0, 255)
|
|
frame[int(eye_y+1), int(eye_x)] = (0, 0, 255)
|
|
frame[int(eye_y+1), int(eye_x+1)] = (0, 0, 255)
|
|
frame[int(eye_y), int(eye_x+1)] = (0, 0, 255)
|
|
else:
|
|
frame[int(eye_y), 32+int(eye_x)] = (0, 0, 255)
|
|
frame[int(eye_y+1), 32+int(eye_x)] = (0, 0, 255)
|
|
frame[int(eye_y+1), 32+int(eye_x+1)] = (0, 0, 255)
|
|
frame[int(eye_y), 32+int(eye_x+1)] = (0, 0, 255)
|
|
|
|
if i == 0:
|
|
eye_x = e_x[i] + scale[i][0] * eye_x
|
|
else:
|
|
eye_x = e_x[i] + scale[i][0] * (32. - eye_x)
|
|
eye_y = e_y[i] + scale[i][1] * eye_y
|
|
eye_x, eye_y = rotate(reference[i], (eye_x, eye_y), -angles[i])
|
|
|
|
eye_x = eye_x + offset[0]
|
|
eye_y = eye_y + offset[1]
|
|
eye_state.append([open[i], eye_y, eye_x, conf])
|
|
|
|
return eye_state
|
|
|
|
def assign_face_info(self, results):
|
|
result_coords = []
|
|
adjusted_results = []
|
|
for conf, (lms, eye_state), conf_adjust in results:
|
|
adjusted_results.append((conf - conf_adjust, (lms, eye_state)))
|
|
result_coords.append(np.array(lms)[:, 0:2].mean(0))
|
|
results = adjusted_results
|
|
candidates = [[]] * self.max_faces
|
|
max_dist = 2 * np.linalg.norm(np.array([self.width, self.height]))
|
|
for i, face_info in enumerate(self.face_info):
|
|
for j, coord in enumerate(result_coords):
|
|
if face_info.coord is None:
|
|
candidates[i].append((max_dist, i, j))
|
|
else:
|
|
candidates[i].append((np.linalg.norm(face_info.coord - coord), i, j))
|
|
for i, candidate in enumerate(candidates):
|
|
candidates[i] = sorted(candidate)
|
|
found = 0
|
|
target = len(results)
|
|
used_results = {}
|
|
used_faces = {}
|
|
while found < target:
|
|
min_list = min(candidates)
|
|
candidate = min_list.pop(0)
|
|
face_idx = candidate[1]
|
|
result_idx = candidate[2]
|
|
if not result_idx in used_results and not face_idx in used_faces:
|
|
self.face_info[face_idx].update(results[result_idx], result_coords[result_idx], self.frame_count)
|
|
min_list.clear()
|
|
used_results[result_idx] = True
|
|
used_faces[face_idx] = True
|
|
found += 1
|
|
if len(min_list) == 0:
|
|
min_list.append((2 * max_dist, face_idx, result_idx))
|
|
for face_info in self.face_info:
|
|
if face_info.frame_count != self.frame_count:
|
|
face_info.update(None, None, self.frame_count)
|
|
|
|
def predict(self, frame, additional_faces=[]):
|
|
self.frame_count += 1
|
|
start = time.perf_counter()
|
|
im = frame
|
|
|
|
duration_fd = 0.0
|
|
duration_pp = 0.0
|
|
duration_model = 0.0
|
|
duration_pnp = 0.0
|
|
|
|
new_faces = []
|
|
new_faces.extend(self.faces)
|
|
bonus_cutoff = len(self.faces)
|
|
new_faces.extend(additional_faces)
|
|
self.wait_count += 1
|
|
if self.detected == 0:
|
|
start_fd = time.perf_counter()
|
|
if self.use_retinaface > 0:
|
|
retinaface_detections = self.retinaface.detect_retina(frame)
|
|
new_faces.extend(retinaface_detections)
|
|
else:
|
|
new_faces.extend(self.detect_faces(frame))
|
|
duration_fd = 1000 * (time.perf_counter() - start_fd)
|
|
self.wait_count = 0
|
|
elif self.detected < self.max_faces:
|
|
if self.use_retinaface > 0:
|
|
new_faces.extend(self.retinaface_scan.get_results())
|
|
if self.wait_count >= self.scan_every:
|
|
if self.use_retinaface > 0:
|
|
self.retinaface_scan.background_detect(frame)
|
|
else:
|
|
start_fd = time.perf_counter()
|
|
new_faces.extend(self.detect_faces(frame))
|
|
duration_fd = 1000 * (time.perf_counter() - start_fd)
|
|
self.wait_count = 0
|
|
else:
|
|
self.wait_count = 0
|
|
|
|
if len(new_faces) < 1:
|
|
duration = (time.perf_counter() - start) * 1000
|
|
if not self.silent:
|
|
print(f"Took {duration:.2f}ms")
|
|
return []
|
|
|
|
crops = []
|
|
crop_info = []
|
|
num_crops = 0
|
|
for j, (x,y,w,h) in enumerate(new_faces):
|
|
crop_x1 = x - int(w * 0.1)
|
|
crop_y1 = y - int(h * 0.125)
|
|
crop_x2 = x + w + int(w * 0.1)
|
|
crop_y2 = y + h + int(h * 0.125)
|
|
|
|
crop_x1, crop_y1 = clamp_to_im((crop_x1, crop_y1), self.width, self.height)
|
|
crop_x2, crop_y2 = clamp_to_im((crop_x2, crop_y2), self.width, self.height)
|
|
|
|
scale_x = float(crop_x2 - crop_x1) / self.res
|
|
scale_y = float(crop_y2 - crop_y1) / self.res
|
|
|
|
if crop_x2 - crop_x1 < 4 or crop_y2 - crop_y1 < 4:
|
|
continue
|
|
|
|
start_pp = time.perf_counter()
|
|
crop = self.preprocess(im, (crop_x1, crop_y1, crop_x2, crop_y2))
|
|
duration_pp += 1000 * (time.perf_counter() - start_pp)
|
|
crops.append(crop)
|
|
crop_info.append((crop_x1, crop_y1, scale_x, scale_y, 0.0 if j >= bonus_cutoff else 0.1))
|
|
num_crops += 1
|
|
|
|
start_model = time.perf_counter()
|
|
outputs = {}
|
|
if num_crops == 1:
|
|
output = self.session.run([], {self.input_name: crops[0]})[0]
|
|
conf, lms = self.landmarks(output[0], crop_info[0])
|
|
if conf > self.threshold:
|
|
eye_state = self.get_eye_state(frame, lms)
|
|
outputs[crop_info[0]] = (conf, (lms, eye_state), 0)
|
|
else:
|
|
started = 0
|
|
results = queue.Queue()
|
|
for i in range(min(num_crops, self.max_workers)):
|
|
thread = threading.Thread(target=worker_thread, args=(self.sessions[started], frame, crops[started], crop_info[started], results, self.input_name, started, self))
|
|
started += 1
|
|
thread.start()
|
|
returned = 0
|
|
while returned < num_crops:
|
|
result = results.get(True)
|
|
if len(result) != 1:
|
|
session, conf, lms, sample_crop_info, idx = result
|
|
outputs[sample_crop_info] = (conf, lms, idx)
|
|
else:
|
|
session = result[0]
|
|
returned += 1
|
|
if started < num_crops:
|
|
thread = threading.Thread(target=worker_thread, args=(session, frame, crops[started], crop_info[started], results, self.input_name, started, self))
|
|
started += 1
|
|
thread.start()
|
|
|
|
actual_faces = []
|
|
good_crops = []
|
|
for crop in crop_info:
|
|
if crop not in outputs:
|
|
continue
|
|
conf, lms, i = outputs[crop]
|
|
x1, y1, _ = lms[0].min(0)
|
|
x2, y2, _ = lms[0].max(0)
|
|
bb = (x1, y1, x2 - x1, y2 - y1)
|
|
outputs[crop] = (conf, lms, i, bb)
|
|
actual_faces.append(bb)
|
|
good_crops.append(crop)
|
|
groups = group_rects(actual_faces)
|
|
|
|
best_results = {}
|
|
for crop in good_crops:
|
|
conf, lms, i, bb = outputs[crop]
|
|
if conf < self.threshold:
|
|
continue;
|
|
group_id = groups[str(bb)][0]
|
|
if not group_id in best_results:
|
|
best_results[group_id] = [-1, [], 0]
|
|
if conf > self.threshold and best_results[group_id][0] < conf + crop[4]:
|
|
best_results[group_id][0] = conf + crop[4]
|
|
best_results[group_id][1] = lms
|
|
best_results[group_id][2] = crop[4]
|
|
|
|
sorted_results = sorted(best_results.values(), key=lambda x: x[0], reverse=True)[:self.max_faces]
|
|
self.assign_face_info(sorted_results)
|
|
duration_model = 1000 * (time.perf_counter() - start_model)
|
|
|
|
results = []
|
|
detected = []
|
|
start_pnp = time.perf_counter()
|
|
for face_info in self.face_info:
|
|
if face_info.alive and face_info.conf > self.threshold:
|
|
face_info.success, face_info.quaternion, face_info.euler, face_info.pnp_error, face_info.pts_3d, face_info.lms = self.estimate_depth(face_info)
|
|
face_info.adjust_3d()
|
|
lms = face_info.lms[:, 0:2]
|
|
x1, y1 = tuple(lms.min(0))
|
|
x2, y2 = tuple(lms.max(0))
|
|
bbox = (y1, x1, y2 - y1, x2 - x1)
|
|
face_info.bbox = bbox
|
|
detected.append(bbox)
|
|
results.append(face_info)
|
|
duration_pnp += 1000 * (time.perf_counter() - start_pnp)
|
|
|
|
if len(detected) > 0:
|
|
self.detected = len(detected)
|
|
self.faces = detected
|
|
self.discard = 0
|
|
else:
|
|
self.detected = 0
|
|
self.discard += 1
|
|
if self.discard > self.discard_after:
|
|
self.faces = []
|
|
else:
|
|
if self.bbox_growth > 0:
|
|
faces = []
|
|
for (x,y,w,h) in self.faces:
|
|
x -= w * self.bbox_growth
|
|
y -= h * self.bbox_growth
|
|
w += 2 * w * self.bbox_growth
|
|
h += 2 * h * self.bbox_growth
|
|
faces.append((x,y,w,h))
|
|
self.faces = faces
|
|
|
|
duration = (time.perf_counter() - start) * 1000
|
|
if not self.silent:
|
|
print(f"Took {duration:.2f}ms (detect: {duration_fd:.2f}ms, crop: {duration_pp:.2f}, track: {duration_model:.2f}ms, 3D points: {duration_pnp:.2f}ms)")
|
|
|
|
results = sorted(results, key=lambda x: x.id)
|
|
|
|
return results
|