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import numpy as np
from modelscope.models.cv.cartoon.facelib.config import config as cfg
class GroupTrack():
def __init__(self):
self.old_frame = None
self.previous_landmarks_set = None
self.with_landmark = True
self.thres = cfg.TRACE.pixel_thres
self.alpha = cfg.TRACE.smooth_landmark
self.iou_thres = cfg.TRACE.iou_thres
def calculate(self, img, current_landmarks_set):
if self.previous_landmarks_set is None:
self.previous_landmarks_set = current_landmarks_set
result = current_landmarks_set
else:
previous_lm_num = self.previous_landmarks_set.shape[0]
if previous_lm_num == 0:
self.previous_landmarks_set = current_landmarks_set
result = current_landmarks_set
return result
else:
result = []
for i in range(current_landmarks_set.shape[0]):
not_in_flag = True
for j in range(previous_lm_num):
if self.iou(current_landmarks_set[i],
self.previous_landmarks_set[j]
) > self.iou_thres:
result.append(
self.smooth(current_landmarks_set[i],
self.previous_landmarks_set[j]))
not_in_flag = False
break
if not_in_flag:
result.append(current_landmarks_set[i])
result = np.array(result)
self.previous_landmarks_set = result
return result
def iou(self, p_set0, p_set1):
rec1 = [
np.min(p_set0[:, 0]),
np.min(p_set0[:, 1]),
np.max(p_set0[:, 0]),
np.max(p_set0[:, 1])
]
rec2 = [
np.min(p_set1[:, 0]),
np.min(p_set1[:, 1]),
np.max(p_set1[:, 0]),
np.max(p_set1[:, 1])
]
# computing area of each rectangles
S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
# computing the sum_area
sum_area = S_rec1 + S_rec2
# find the each edge of intersect rectangle
x1 = max(rec1[0], rec2[0])
y1 = max(rec1[1], rec2[1])
x2 = min(rec1[2], rec2[2])
y2 = min(rec1[3], rec2[3])
# judge if there is an intersect
intersect = max(0, x2 - x1) * max(0, y2 - y1)
iou = intersect / (sum_area - intersect)
return iou
def smooth(self, now_landmarks, previous_landmarks):
result = []
for i in range(now_landmarks.shape[0]):
x = now_landmarks[i][0] - previous_landmarks[i][0]
y = now_landmarks[i][1] - previous_landmarks[i][1]
dis = np.sqrt(np.square(x) + np.square(y))
if dis < self.thres:
result.append(previous_landmarks[i])
else:
result.append(
self.do_moving_average(now_landmarks[i],
previous_landmarks[i]))
return np.array(result)
def do_moving_average(self, p_now, p_previous):
p = self.alpha * p_now + (1 - self.alpha) * p_previous
return p
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