290 lines
10 KiB
Python
290 lines
10 KiB
Python
import cv2
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import numpy as np
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import Face_Swap
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from lib import *
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class MainProgram:
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def __init__(self, face_model_path, model_path, reid_weights, tracker_type="deepocsort"):
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self.face_model_path = face_model_path
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self.model_path = model_path
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self.face_model = YOLO(face_model_path)
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self.person_model = YOLO(model_path)
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self.reid_weights = reid_weights
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self.tracker_conf = get_tracker_config(tracker_type)
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self.sock = U.UdpComms(udpIP="192.168.1.122", portTX=8000, portRX=8001, enableRX=True, suppressWarnings=True)
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self.tracker = create_tracker(
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tracker_type=tracker_type,
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tracker_config=self.tracker_conf,
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reid_weights=reid_weights,
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device='0',
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half=False,
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per_class=False
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)
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self.face_detect = FaceAnalysis(name='buffalo_l')
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self.face_detect.prepare(ctx_id=0, det_size=(640, 640))
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self.swapper = insightface.model_zoo.get_model("inswapper_128.onnx")
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self.send_data_unity: dict = {
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"PassBy": False,
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"Engage": False,
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"Ready": False,
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"Gender": None,
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"AgeMin": None,
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"AgeMax": None,
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"GenerateImageSuccess": False,
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"Description": "",
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"FileName": ""
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}
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self.focus_id = None
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self.frame_count_remove_idx = 0
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sa = gspread.service_account("key.json")
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sh = sa.open("TestData")
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wks = sh.worksheet("Sheet2")
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self.all_record = wks.get_all_records()
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self.client = NovitaClient("48cc2b16-286f-49c8-9581-f409b68359c4")
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self.client_minio = Minio("192.168.1.186:50047",
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access_key="play4promo_user",
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secret_key="12345678",
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secure=False
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)
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self.bucket_name = "play4promo"
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self.des_file = ""
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self.source_file = "sid/source_file.jpg"
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self.ready_success = False
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self.show_success = False
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self.check_save, self.check_generate = False, False
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self.forward_face = FaceDetection()
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# self.face_swap = Face_Swap.FaceSwap()
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self.gender_model = Gender_Prediction.GenderPrediction()
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self.focus_face_bbox = []
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def convertFrame(self, frame) -> str:
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encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 90]
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frame = imutils.resize(frame, width=400)
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result, encoded_frame = cv2.imencode('.jpg', frame, encode_param)
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jpg_as_text = base64.b64encode(encoded_frame.tobytes())
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return jpg_as_text.decode('utf-8')
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def get_image(self):
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ran_num = random.randint(0, len(self.all_record) - 1)
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image_url = self.all_record[ran_num]["Image link"]
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des = self.all_record[ran_num]["Note"]
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return image_url, des
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def face_swap(self, image, source_face, paste_back=True):
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"""
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Args:
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image : original content image
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target: face in the original image [{....}] need embedding
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source_face: face of the user [{....}] need embedding
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Return:
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image with swapped face
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"""
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target = self.face_detect.get(image)[0]
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source_face = self.face_detect.get(source_face)[0]
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res = image.copy()
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res = self.swapper.get(res, target, source_face, paste_back)
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return res
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def generate_local_image(self, source_face):
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# image_url, des = self.get_image()
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# Path to the folder containing images
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folder_path = "./origin_image/"
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# Get a list of all files in the folder
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files = os.listdir(folder_path)
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# Filter out only image files (assuming common image formats)
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image_files = [f for f in files if f.endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif'))]
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# Check if there are any image files in the folder
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if image_files:
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# Select a random image file
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random_image_file = random.choice(image_files)
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# Read the image using OpenCV
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image_path = os.path.join(folder_path, random_image_file)
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image = cv2.imread(image_path)
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origin_image = image.copy()
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image_path = "./image/merge_face.png"
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result = self.face_swap(origin_image, source_face)
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cv2.imwrite(image_path, result)
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def predict_age_and_gender(self):
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image_predict = cv2.imread("./image/output.jpg")
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gender = self.gender_model.predict_gender(image_predict)
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self.send_data_unity["Gender"] = gender
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def get_face_bbox(self, frame):
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outs = self.face_model(frame)
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results = sv.Detections.from_ultralytics(outs[0])
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bbox = results.xyxy.astype(np.int_)
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conf = results.confidence.astype(np.float32)
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return np.concatenate((bbox, conf[:, np.newaxis]), axis=1)
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def cropped_image(self, frame, x1, y1, x2, y2):
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return frame[y1: y2, x1: x2]
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def check_ready(self, frame):
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return self.forward_face.detect_face(frame, self.face_zone_center_point[0],
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self.face_zone_center_point[1])
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def person_process(self, frame):
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received_data = self.sock.ReadReceivedData()
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if received_data == "Begin":
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self.ready_success = True
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elif received_data == "End":
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self.ready_success = False
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self.check_save = False
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self.check_generate = False
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self.send_data_unity["Gender"] = None
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self.send_data_unity["AgeMin"] = None
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self.send_data_unity["AgeMax"] = None
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self.send_data_unity["GenerateImageSuccess"] = False
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self.send_data_unity["Description"] = ""
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if self.ready_success and (not self.check_save):
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# Perform person detection
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self.send_data_unity["PassBy"], _, _ = self.check_ready(frame)
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self.send_data_unity["Engage"] = self.send_data_unity["PassBy"]
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if not self.ready_success:
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self.send_data_unity["PassBy"], self.send_data_unity["Ready"], self.focus_face_bbox = self.check_ready(frame)
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self.send_data_unity["Engage"] = self.send_data_unity["PassBy"]
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elif not self.check_save and self.focus_face_bbox:
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cv2.imwrite("./image/output.jpg", self.cropped_image(frame, self.focus_face_bbox[0],
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self.focus_face_bbox[1],
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self.focus_face_bbox[2],
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self.focus_face_bbox[3]))
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self.client_minio.fput_object(
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self.bucket_name, self.source_file, "./image/output.jpg",
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)
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self.check_save = True
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elif not self.check_generate:
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if str(self.send_data_unity["Gender"]) == "None":
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self.predict_age_and_gender()
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else:
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source_face = cv2.imread("./image/output.jpg")
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self.generate_local_image(source_face)
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self.check_generate = True
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elif self.show_success:
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self.check_save = False
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self.check_generate = False
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def __call__(self):
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cap = cv2.VideoCapture(0)
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while cap.isOpened():
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self.frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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self.frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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self.center_point = (int(int(self.frame_width) / 2), int(int(self.frame_height) / 2))
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self.red_zone_width = (self.center_point[0] - 250, self.center_point[0] + 250)
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self.red_zone_height = (self.center_point[1] - 50, self.frame_height)
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self.face_zone_width = (self.center_point[0] - 120, self.center_point[0] + 120)
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self.face_zone_height = (self.center_point[1] - 120, self.center_point[1] + 120)
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self.face_zone_center_point = (
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int((self.face_zone_width[1] - self.face_zone_width[0]) / 2) + self.face_zone_width[0],
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int((self.face_zone_height[1] - self.face_zone_height[0]) / 2) + self.face_zone_height[0])
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ret, frame = cap.read()
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frame = cv2.flip(frame, 1)
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if not ret:
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continue
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frame_to_handle = frame.copy()
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self.frame_to_show = frame.copy()
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# self.person_process(frame_to_handle)
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try:
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self.person_process(frame_to_handle)
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except Exception as e:
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print(e)
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if not self.send_data_unity["GenerateImageSuccess"]:
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self.send_data_unity["StreamingData"] = self.convertFrame(self.cropped_image(frame,
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self.face_zone_width[0],
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self.face_zone_height[0],
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self.face_zone_width[1],
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self.face_zone_height[1]))
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self.sock.SendData(self.send_data_unity)
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cv2.rectangle(self.frame_to_show, (self.face_zone_width[0], self.face_zone_height[0]),
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(self.face_zone_width[1], self.face_zone_height[1]),
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(0, 255, 255), 2)
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cv2.circle(self.frame_to_show, self.face_zone_center_point, 5, (255, 255, 0), -1)
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cv2.imshow("Output", self.frame_to_show)
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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cap.release()
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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print("Starting python...")
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face_model_path = "face_detect.pt"
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model_path = "yolov8n.pt"
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tracker_type = "deepocsort"
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reid_weights = Path('osnet_x0_25_msmt17.pt')
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run_main_program = MainProgram(face_model_path, model_path, reid_weights, tracker_type)
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run_main_program()
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