def video_inference(video, model_path, color=(125, 255, 0)):
fd = UltraLightFaceDetecion(model_path, conf_threshold=0.88)
cap = cv2.VideoCapture(video)
while True:
ret, frame = cap.read()
if not ret:
break
start_time = time.perf_counter()
boxes, scores = fd.inference(frame)
print(time.perf_counter() - start_time)
for result in boxes.astype(int):
cv2.rectangle(frame, (result[0], result[1]),
(result[2], result[3]), color, 2)
cv2.imshow('res', frame)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def main(filename):
from TFLiteFaceDetector import UltraLightFaceDetecion
from TFLiteFaceAlignment import CoordinateAlignmentModel
cap = cv2.VideoCapture(filename)
fd = UltraLightFaceDetecion("weights/RFB-320.tflite", conf_threshold=0.95)
fa = CoordinateAlignmentModel("weights/coor_2d106.tflite")
hp = HeadPoseEstimator("weights/head_pose_object_points.npy", cap.get(3),
cap.get(4))
color = (125, 255, 125)
while True:
ret, frame = cap.read()
if not ret:
break
bboxes, _ = fd.inference(frame)
for pred in fa.get_landmarks(frame, bboxes):
for p in np.round(pred).astype(np.int):
cv2.circle(frame, tuple(p), 1, color, 1, cv2.LINE_AA)
face_center = np.mean(pred, axis=0)
euler_angle = hp.get_head_pose(pred).flatten()
print(*euler_angle)
hp.draw_axis(frame, euler_angle, face_center)
cv2.imshow("result", frame)
if cv2.waitKey(0) == ord('q'):
break
def image_inference(image_path, model_path, color=(125, 255, 0)):
fd = UltraLightFaceDetecion(model_path, conf_threshold=0.6)
img = cv2.imread(image_path)
boxes, scores = fd.inference(img)
for result in boxes.astype(int):
cv2.rectangle(img, (result[0], result[1]), (result[2], result[3]),
color, 2)
cv2.imshow('res', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
out = self._inference(inp)
pred = self._postprocessing(out, M)
# self._calibrate(pred, 1, skip=6)
# yield self.pre_landmarks
yield pred
if __name__ == '__main__':
from TFLiteFaceDetector import UltraLightFaceDetecion
import sys
fd = UltraLightFaceDetecion(
"pretrained/version-RFB-320_without_postprocessing.tflite",
conf_threshold=0.88)
fa = CoordinateAlignmentModel(
"pretrained/coor_2d106_face_alignment.tflite")
cap = cv2.VideoCapture(sys.argv[1])
color = (125, 255, 125)
while True:
ret, frame = cap.read()
if not ret:
break
start_time = time.perf_counter()
# coding: utf-8
from TFLiteFaceDetector import UltraLightFaceDetecion
from TFLiteFaceAlignment import CoordinateAlignmentModel
from TFLiteIrisLocalization import IrisLocalizationModel
from SolvePnPHeadPoseEstimation import HeadPoseEstimator
from threading import Thread
import cv2
import sys
import numpy as np
from queue import Queue
import socketio
cap = cv2.VideoCapture(sys.argv[1])
fd = UltraLightFaceDetecion(
"pretrained/version-RFB-320_without_postprocessing.tflite",
conf_threshold=0.98)
fa = CoordinateAlignmentModel("pretrained/coor_2d106_face_alignment.tflite")
hp = HeadPoseEstimator("pretrained/head_pose_object_points.npy", cap.get(3),
cap.get(4))
gs = IrisLocalizationModel("pretrained/iris_localization.tflite")
QUEUE_BUFFER_SIZE = 18
box_queue = Queue(maxsize=QUEUE_BUFFER_SIZE)
landmark_queue = Queue(maxsize=QUEUE_BUFFER_SIZE)
iris_queue = Queue(maxsize=QUEUE_BUFFER_SIZE)
upstream_queue = Queue(maxsize=QUEUE_BUFFER_SIZE)
# ======================================================
return theta, pha, delta.T
if __name__ == "__main__":
import sys
from SolvePnPHeadPoseEstimation import HeadPoseEstimator
from TFLiteFaceAlignment import CoordinateAlignmentModel
from TFLiteFaceDetector import UltraLightFaceDetecion
gpu_ctx = -1
video = sys.argv[1]
YAW_THD = 45
cap = cv2.VideoCapture(video)
fd = UltraLightFaceDetecion("weights/RFB-320.tflite", conf_threshold=0.9)
fa = CoordinateAlignmentModel("weights/coor_2d106.tflite")
hp = HeadPoseEstimator("weights/head_pose_object_points.npy", cap.get(3),
cap.get(4))
gs = IrisLocalizationModel("weights/iris_localization.tflite")
counter = 0
while True:
ret, frame = cap.read()
if not ret:
break
# frame = frame[:480, 380:920, :] # dress
# frame = cv2.resize(frame, (960, 1080))