| import os |
| import cv2 |
| import imghdr |
| import shutil |
| import warnings |
| import numpy as np |
| import gradio as gr |
| from dataclasses import dataclass |
| from mivolo.predictor import Predictor |
| from utils import is_url, download_file, get_jpg_files, MODEL_DIR |
|
|
| TMP_DIR = "./__pycache__" |
|
|
| @dataclass |
| class Cfg: |
| detector_weights: str |
| checkpoint: str |
| device: str = "cpu" |
| with_persons: bool = True |
| disable_faces: bool = False |
| draw: bool = True |
|
|
| class ValidImgDetector: |
| predictor = None |
|
|
| def __init__(self): |
| detector_path = f"{MODEL_DIR}/yolov8x_person_face.pt" |
| age_gender_path = f"{MODEL_DIR}/model_imdb_cross_person_4.22_99.46.pth.tar" |
| predictor_cfg = Cfg(detector_path, age_gender_path) |
| self.predictor = Predictor(predictor_cfg) |
|
|
| def _detect( |
| self, |
| image: np.ndarray, |
| score_threshold: float, |
| iou_threshold: float, |
| mode: str, |
| predictor: Predictor, |
| ) -> np.ndarray: |
| predictor.detector.detector_kwargs["conf"] = score_threshold |
| predictor.detector.detector_kwargs["iou"] = iou_threshold |
|
|
| if mode == "Use persons and faces": |
| use_persons = True |
| disable_faces = False |
| elif mode == "Use persons only": |
| use_persons = True |
| disable_faces = True |
| elif mode == "Use faces only": |
| use_persons = False |
| disable_faces = False |
|
|
| predictor.age_gender_model.meta.use_persons = use_persons |
| predictor.age_gender_model.meta.disable_faces = disable_faces |
| detected_objects, out_im = predictor.recognize(image) |
| has_child, has_female, has_male = False, False, False |
| if len(detected_objects.ages) > 0: |
| has_child = min(detected_objects.ages) < 18 |
| has_female = "female" in detected_objects.genders |
| has_male = "male" in detected_objects.genders |
|
|
| return out_im[:, :, ::-1], has_child, has_female, has_male |
|
|
| def valid_video(self, video_path): |
| cap = cv2.VideoCapture(video_path) |
| results = [] |
|
|
| while cap.isOpened(): |
| ret, frame = cap.read() |
| if not ret: |
| break |
|
|
| |
| out_frame, has_child, has_female, has_male = self._detect(frame, 0.4, 0.7, "Use persons and faces", self.predictor) |
| results.append((out_frame, has_child, has_female, has_male)) |
| |
| cap.release() |
| return results |
|
|
| def infer(video_path: str): |
| if is_url(video_path): |
| if os.path.exists(TMP_DIR): |
| shutil.rmtree(TMP_DIR) |
|
|
| video_path = download_file(video_path, f"{TMP_DIR}/download.mp4") |
|
|
| detector = ValidImgDetector() |
| if not video_path or not os.path.exists(video_path): |
| return None, None, None, "Please input the video correctly" |
|
|
| results = detector.valid_video(video_path) |
| |
| |
| if results: |
| first_frame_result = results[0] |
| return first_frame_result |
| else: |
| return None, None, None, "No frames detected in video." |
|
|
| if __name__ == "__main__": |
| with gr.Blocks() as iface: |
| warnings.filterwarnings("ignore") |
| with gr.Tab("Upload Mode"): |
| gr.Interface( |
| fn=infer, |
| inputs=gr.Video(label="Upload Video"), |
| outputs=[ |
| gr.Image(label="Detection Result", type="numpy"), |
| gr.Textbox(label="Has Child"), |
| gr.Textbox(label="Has Female"), |
| gr.Textbox(label="Has Male"), |
| ], |
| allow_flagging="never", |
| ) |
|
|
| iface.launch() |
|
|
