import cv2 import numpy as np import Face_Swap 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": "", "FileName": "" } self.focus_id = None self.frame_count_remove_idx = 0 sa = gspread.service_account("key.json") sh = sa.open("TestData") wks = sh.worksheet("Sheet2") 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 = "" self.source_file = "sid/source_file.jpg" self.ready_success = False self.show_success = False self.check_save, self.check_generate = False, False self.forward_face = FaceDetection() self.face_swap = Face_Swap.FaceSwap() self.focus_face_bbox = [] def convertFrame(self, frame) -> str: encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 90] frame = imutils.resize(frame, width=400) 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) # Get the current time current_time = datetime.now() # Format the current time formatted_time = current_time.strftime("%d_%m_%Y_%H_%M_%S") self.des_file = f"sid/{formatted_time}.jpg" self.send_data_unity["FileName"] = f"{formatted_time}.jpg" 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 generate_with_rapapi(self): image_url, des = self.get_image() image_path = self.face_swap.save_image_result(image_url) # Get the current time current_time = datetime.now() # Format the current time formatted_time = current_time.strftime("%d_%m_%Y_%H_%M_%S") self.des_file = f"sid/{formatted_time}.jpg" self.send_data_unity["FileName"] = f"{formatted_time}.jpg" 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.jpg" 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 cropped_image(self, frame, x1, y1, x2, y2): return frame[y1: y2, x1: x2] 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): 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 self.send_data_unity["Gender"] = None self.send_data_unity["AgeMin"] = None self.send_data_unity["AgeMax"] = None self.send_data_unity["GenerateImageSuccess"] = False self.send_data_unity["Description"] = "" if self.ready_success: # Perform person detection self.send_data_unity["PassBy"], _, _ = self.check_ready(frame) self.send_data_unity["Engage"] = self.send_data_unity["PassBy"] if not self.ready_success: self.send_data_unity["PassBy"], self.send_data_unity["Ready"], self.focus_face_bbox = self.check_ready(frame) self.send_data_unity["Engage"] = self.send_data_unity["PassBy"] elif not self.check_save: cv2.imwrite("./image/output.jpg", self.cropped_image(frame, self.focus_face_bbox[0], self.focus_face_bbox[1], self.focus_face_bbox[2], self.focus_face_bbox[3])) self.client_minio.fput_object( self.bucket_name, self.source_file, "./image/output.jpg", ) 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] - 120, self.center_point[0] + 120) self.face_zone_height = (self.center_point[1] - 120, self.center_point[1] + 120) 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()