269 lines
9.3 KiB
Python
269 lines
9.3 KiB
Python
import cv2
|
|
import numpy as np
|
|
|
|
from lib import *
|
|
|
|
|
|
class MainProgram:
|
|
def __init__(self, face_model_path, model_path, reid_weights, tracker_type="deepocsort"):
|
|
self.face_model_path = face_model_path
|
|
self.model_path = model_path
|
|
|
|
self.face_model = YOLO(face_model_path)
|
|
self.person_model = YOLO(model_path)
|
|
|
|
self.reid_weights = reid_weights
|
|
|
|
self.tracker_conf = get_tracker_config(tracker_type)
|
|
|
|
self.sock = U.UdpComms(udpIP="192.168.1.122", portTX=8000, portRX=8001, enableRX=True, suppressWarnings=True)
|
|
|
|
self.tracker = create_tracker(
|
|
tracker_type=tracker_type,
|
|
tracker_config=self.tracker_conf,
|
|
reid_weights=reid_weights,
|
|
device='0',
|
|
half=False,
|
|
per_class=False
|
|
)
|
|
|
|
self.send_data_unity: dict = {
|
|
"PassBy": False,
|
|
"Engage": False,
|
|
"Ready": False,
|
|
"Gender": None,
|
|
"AgeMin": None,
|
|
"AgeMax": None,
|
|
"GenerateImageSuccess": False,
|
|
"Description": ""
|
|
}
|
|
|
|
self.focus_id = None
|
|
|
|
self.frame_count_remove_idx = 0
|
|
|
|
sa = gspread.service_account("key.json")
|
|
sh = sa.open("TestData")
|
|
|
|
wks = sh.worksheet("Sheet1")
|
|
|
|
self.all_record = wks.get_all_records()
|
|
|
|
self.client = NovitaClient("48cc2b16-286f-49c8-9581-f409b68359c4")
|
|
|
|
self.client_minio = Minio("192.168.1.186:50047",
|
|
access_key="play4promo_user",
|
|
secret_key="12345678",
|
|
secure=False
|
|
)
|
|
|
|
self.bucket_name = "play4promo"
|
|
self.des_file = "sid/final_image.jpg"
|
|
|
|
self.ready_success = False
|
|
|
|
self.show_success = False
|
|
|
|
self.check_save, self.check_generate = False, False
|
|
|
|
self.forward_face = FaceDetection()
|
|
|
|
def convertFrame(self, frame) -> str:
|
|
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 90]
|
|
frame = imutils.resize(frame, width=512)
|
|
result, encoded_frame = cv2.imencode('.jpg', frame, encode_param)
|
|
jpg_as_text = base64.b64encode(encoded_frame.tobytes())
|
|
|
|
return jpg_as_text.decode('utf-8')
|
|
|
|
def get_image(self):
|
|
ran_num = random.randint(0, len(self.all_record) - 1)
|
|
|
|
image_url = self.all_record[ran_num]["Image link"]
|
|
des = self.all_record[ran_num]["Note"]
|
|
|
|
return image_url, des
|
|
|
|
def generate_image(self):
|
|
image_url, des = self.get_image()
|
|
image_path = "./image/merge_face.png"
|
|
|
|
res = self.client.merge_face(
|
|
image=image_url,
|
|
face_image="./image/output.jpg",
|
|
)
|
|
|
|
base64_to_image(res.image_file).save(image_path)
|
|
|
|
self.client_minio.fput_object(
|
|
self.bucket_name, self.des_file, image_path,
|
|
)
|
|
|
|
self.send_data_unity["Description"] = des
|
|
self.send_data_unity["GenerateImageSuccess"] = True
|
|
self.send_data_unity["StreamingData"] = "./Assets/StreamingAssets/MergeFace/image/merge_face.png"
|
|
|
|
def predict_age_and_gender(self):
|
|
image_predict = cv2.imread("./image/output.jpg")
|
|
if AgeGenderPrediction.Prediction(image_predict):
|
|
|
|
self.send_data_unity["Gender"] = AgeGenderPrediction.Prediction(image_predict)[0]
|
|
self.send_data_unity["AgeMin"] = int(
|
|
AgeGenderPrediction.Prediction(image_predict)[1].split("-")[0])
|
|
self.send_data_unity["AgeMax"] = int(
|
|
AgeGenderPrediction.Prediction(image_predict)[1].split("-")[1])
|
|
|
|
else:
|
|
self.send_data_unity["Gender"] = None
|
|
self.send_data_unity["AgeMin"] = None
|
|
self.send_data_unity["AgeMax"] = None
|
|
|
|
def get_face_bbox(self, frame):
|
|
outs = self.face_model(frame)
|
|
results = sv.Detections.from_ultralytics(outs[0])
|
|
bbox = results.xyxy.astype(np.int_)
|
|
conf = results.confidence.astype(np.float32)
|
|
|
|
return np.concatenate((bbox, conf[:, np.newaxis]), axis=1)
|
|
|
|
def check_engage(self, frame):
|
|
# Perform object detection with YOLOv8 class = 0 indicate person class
|
|
outs = self.person_model(frame, classes=[0], conf=0.7)
|
|
if not outs[0].boxes.xyxy.tolist():
|
|
return False
|
|
else:
|
|
return True
|
|
|
|
def cropped_image(self, frame, x1, y1, x2, y2):
|
|
return frame[y1: y2, x1: x2]
|
|
|
|
def calculate_dis_to_cp(self, cx, cy, face_cx, face_cy) -> float:
|
|
return math.sqrt((face_cx - cx) ** 2 + (face_cy - cy) ** 2)
|
|
|
|
def check_ready(self, frame):
|
|
return self.forward_face.detect_face(frame, self.face_zone_center_point[0],
|
|
self.face_zone_center_point[1])
|
|
|
|
def person_process(self, frame):
|
|
# Perform person detection
|
|
self.send_data_unity["PassBy"] = self.check_engage(frame)
|
|
self.send_data_unity["Engage"] = self.send_data_unity["PassBy"]
|
|
|
|
received_data = self.sock.ReadReceivedData()
|
|
|
|
if received_data == "Begin":
|
|
self.ready_success = True
|
|
|
|
elif received_data == "End":
|
|
self.ready_success = False
|
|
self.check_save = False
|
|
self.check_generate = False
|
|
os.remove("./image/output.jpg")
|
|
os.remove("./image/merge_face.png")
|
|
self.send_data_unity: dict = {
|
|
"PassBy": False,
|
|
"Engage": False,
|
|
"Ready": False,
|
|
"Gender": None,
|
|
"AgeMin": None,
|
|
"AgeMax": None,
|
|
"GenerateImageSuccess": False,
|
|
"Description": ""
|
|
}
|
|
|
|
if not self.ready_success:
|
|
self.send_data_unity["Ready"] = True if self.check_ready(frame) else False
|
|
|
|
elif not self.check_save:
|
|
cv2.imwrite("./image/output.jpg", self.cropped_image(frame, self.face_zone_width[0],
|
|
self.face_zone_height[0],
|
|
self.face_zone_width[1],
|
|
self.face_zone_height[1]))
|
|
self.check_save = True
|
|
|
|
elif not self.check_generate:
|
|
if str(self.send_data_unity["Gender"]) == "None":
|
|
self.predict_age_and_gender()
|
|
|
|
else:
|
|
self.generate_image()
|
|
self.check_generate = True
|
|
|
|
elif self.show_success:
|
|
self.check_save = False
|
|
self.check_generate = False
|
|
|
|
def __call__(self):
|
|
cap = cv2.VideoCapture(0)
|
|
|
|
while cap.isOpened():
|
|
|
|
self.frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
|
self.frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
|
|
|
self.center_point = (int(int(self.frame_width) / 2), int(int(self.frame_height) / 2))
|
|
|
|
self.red_zone_width = (self.center_point[0] - 250, self.center_point[0] + 250)
|
|
self.red_zone_height = (self.center_point[1] - 50, self.frame_height)
|
|
|
|
self.face_zone_width = (self.center_point[0] - 100, self.center_point[0] + 100)
|
|
self.face_zone_height = (self.center_point[1] - 200, self.center_point[1])
|
|
|
|
self.face_zone_center_point = (
|
|
int((self.face_zone_width[1] - self.face_zone_width[0]) / 2) + self.face_zone_width[0],
|
|
int((self.face_zone_height[1] - self.face_zone_height[0]) / 2) + self.face_zone_height[0])
|
|
|
|
ret, frame = cap.read()
|
|
|
|
frame = cv2.flip(frame, 1)
|
|
|
|
if not ret:
|
|
continue
|
|
|
|
frame_to_handle = frame.copy()
|
|
self.frame_to_show = frame.copy()
|
|
|
|
# self.person_process(frame_to_handle)
|
|
|
|
try:
|
|
self.person_process(frame_to_handle)
|
|
|
|
except Exception as e:
|
|
print(e)
|
|
|
|
if not self.send_data_unity["GenerateImageSuccess"]:
|
|
self.send_data_unity["StreamingData"] = self.convertFrame(self.cropped_image(frame,
|
|
self.face_zone_width[0],
|
|
self.face_zone_height[0],
|
|
self.face_zone_width[1],
|
|
self.face_zone_height[1]))
|
|
|
|
self.sock.SendData(self.send_data_unity)
|
|
|
|
cv2.rectangle(self.frame_to_show, (self.face_zone_width[0], self.face_zone_height[0]),
|
|
(self.face_zone_width[1], self.face_zone_height[1]),
|
|
(0, 255, 255), 2)
|
|
|
|
cv2.circle(self.frame_to_show, self.face_zone_center_point, 5, (255, 255, 0), -1)
|
|
|
|
cv2.imshow("Output", self.frame_to_show)
|
|
|
|
if cv2.waitKey(1) & 0xFF == ord("q"):
|
|
break
|
|
|
|
cap.release()
|
|
cv2.destroyAllWindows()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
print("Starting python...")
|
|
face_model_path = "face_detect.pt"
|
|
model_path = "yolov8n.pt"
|
|
|
|
tracker_type = "deepocsort"
|
|
reid_weights = Path('osnet_x0_25_msmt17.pt')
|
|
|
|
run_main_program = MainProgram(face_model_path, model_path, reid_weights, tracker_type)
|
|
|
|
run_main_program()
|