fisheye support
This commit is contained in:
Ruben van de Ven
parent
ef24bb56f5
commit
7eaab23668
9 changed files with 428 additions and 143 deletions
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@ -54,7 +54,7 @@ torch = [{ url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp
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torchvision = [{ url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp310-cp310-linux_x86_64.whl", marker = "python_version ~= '3.10' and sys_platform == 'linux'" }]
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pandas-helper-calc = { git = "https://github.com/scls19fr/pandas-helper-calc" }
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bytetracker = { git = "https://github.com/rubenvandeven/bytetrack-pip" }
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baumer-neoapi = { path = "../../Downloads/Baumer_neoAPI_1.4.1_lin_x86_64_python/wheel/baumer_neoapi-1.4.1-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl" }
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baumer-neoapi = { path = "../../Downloads/Baumer_neoAPI_1.5.0_lin_x86_64_python/wheel/baumer_neoapi-1.5.0-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl" }
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foucault = { git = "https://git.rubenvandeven.com/r/conductofconduct" }
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opencv-python = {path="./opencv_python-4.10.0.84-cp310-cp310-linux_x86_64.whl"}
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File diff suppressed because one or more lines are too long
189
trap/base.py
189
trap/base.py
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@ -1,14 +1,14 @@
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from __future__ import annotations
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from abc import ABC, abstractmethod
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import argparse
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from ast import List
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from enum import IntFlag
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from itertools import cycle
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import json
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import logging
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from pathlib import Path
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import time
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from typing import Iterable, Optional
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from typing import Iterable, Optional, Tuple, Union, List
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import cv2
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from dataclasses import dataclass, field
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import dataclasses
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@ -19,11 +19,12 @@ from deep_sort_realtime.deep_sort.track import TrackState as DeepsortTrackState
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from bytetracker.byte_tracker import STrack as ByteTrackTrack
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from bytetracker.basetrack import TrackState as ByteTrackTrackState
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import pandas as pd
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from shapely import Point
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from trap.utils import get_bins, inv_lerp, lerp
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from trajectron.environment import Environment, Node, Scene
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from urllib.parse import urlparse
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from cv2.typing import MatLike
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logger = logging.getLogger('trap.base')
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class UrlOrPath():
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@ -100,31 +101,134 @@ def H_from_path(path: Path):
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H = np.loadtxt(path, delimiter=',')
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return H
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@dataclass
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class Camera:
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mtx: cv2.Mat
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dist: cv2.Mat
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w: float
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h: float
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H: cv2.Mat # homography
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newcameramtx: cv2.Mat = field(init=False)
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roi: cv2.typing.Rect = field(init=False)
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fps: float
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PointList = List[Tuple[float, float]] | np.ndarray | cv2.typing.MatLike
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def scale_homography(H: cv2.Mat, scale: float):
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"""Transform the given matrix so that it immediately converts
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the points to img space"""
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new_H = H.copy()
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new_H[:2] = H[:2] * scale
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return new_H
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class DistortedCamera(ABC):
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@abstractmethod
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def undistort_img(self, img: MatLike):
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return cv2.remap(img, self.map1, self.map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
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def project_img(self, undistorted_img: MatLike, scale: float = 1.0):
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w, h = undistorted_img.shape[1], undistorted_img.shape[0]
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if scale != 1:
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H = scale_homography(self.H, scale)
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else:
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H = self.H
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return cv2.warpPerspective(undistorted_img, H,(w, h))
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def img_to_world(self, img: MatLike, scale = 1.):
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img = self.undistort_img(img)
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return self.project_img(img, scale)
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@abstractmethod
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def undistort_points(self, distorted_points: PointList):
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pass
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def project_point(self, point):
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return self.project_points([point])[0]
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def project_points(self, points: PointList, scale: float = 1.0):
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if scale != 1:
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H = scale_homography(self.H, scale)
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else:
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H = self.H
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coords = cv2.perspectiveTransform(np.array([points]),H)
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if coords.shape[1:] == (1,2):
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coords = np.reshape(coords, (coords.shape[0], 2))
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return coords
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def points_img_to_world(self, points: PointList, scale = 1.):
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# undistort & project
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coords = self.undistort_points(points)
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coords: np.ndarray[argparse.Any, np.dtype[np.integer[argparse.Any] | np.floating[argparse.Any]]] = self.project_points(coords, scale)
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return coords
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class FisheyeCamera(DistortedCamera):
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def __init__(self, dim1, dim2, dim3, K, D, new_K, scaled_K, balance, H, fps):
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# dimensions as per: https://medium.com/@kennethjiang/calibrate-fisheye-lens-using-opencv-part-2-13990f1b157f
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self.dim1 = dim1 # original image
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self.dim2 = dim2 # dimension of the box you want to keep after un-distorting the image. influced by balance
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self.dim3 = dim3 # Dimension of the final box where OpenCV will put the undistorted image.
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self.K = K
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self.D = D
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self.new_K = new_K
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self.scaled_K = scaled_K
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self.balance = balance
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self.H = H # Homography
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self._R = np.eye(3)
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self.fps = fps
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self.map1, self.map2 = cv2.fisheye.initUndistortRectifyMap(self.scaled_K, self.D, self._R, self.new_K, self.dim3, cv2.CV_16SC2)
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def undistort_img(self, img: MatLike):
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return cv2.remap(img, self.map1, self.map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
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def undistort_points(self, distorted_points: PointList):
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points = cv2.fisheye.undistortPoints (np.array([distorted_points]).astype(np.float32), K=self.scaled_K, D=self.D, R=self._R, P=self.new_K)
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return points[0]
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@property
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def projected_w(self):
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return self.dim3[0]
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@property
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def projected_h(self):
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return self.dim3[1]
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@classmethod
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def from_calibfile(cls, calibration_path, H, fps):
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with calibration_path.open('r') as fp:
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data = json.load(fp)
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return cls(
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data['dim1'],
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data['dim2'],
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data['dim3'],
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np.array(data['K']),
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np.array(data['D']),
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np.array(data['new_K']),
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np.array(data['scaled_K']),
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data['balance'],
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H, fps)
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@classmethod
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def from_paths(cls, calibration_path, h_path, fps):
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H = H_from_path(h_path)
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return cls.from_calibfile(calibration_path, H, fps)
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class Camera(DistortedCamera):
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def __init__(self, mtx: cv2.Mat, dist: cv2.Mat, w: float, h: float, H: cv2.Mat, fps: float):
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self.mtx = mtx
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self.dist = dist
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self.w = w
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self.h = h
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self.H = H
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self.fps = fps
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def __post_init__(self):
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self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(self.mtx, self.dist, (self.w,self.h), 1, (self.w,self.h))
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@classmethod
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def from_calibfile(cls, calibration_path, H, fps):
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with calibration_path.open('r') as fp:
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data = json.load(fp)
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# print(data)
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# print(data['camera_matrix'])
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# camera = {
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# 'camera_matrix': np.array(data['camera_matrix']),
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# 'dist_coeff': np.array(data['dist_coeff']),
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# }
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return cls(
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np.array(data['camera_matrix']),
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np.array(data['dist_coeff']),
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@ -132,18 +236,26 @@ class Camera:
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data['dim']['height'],
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H, fps)
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@property
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def projected_w(self):
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return self.w
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@property
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def projected_h(self):
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return self.h
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@classmethod
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def from_paths(cls, calibration_path, h_path, fps):
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H = H_from_path(h_path)
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return cls.from_calibfile(calibration_path, H, fps)
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# def __init__(self, mtx, dist, w, h, H):
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# self.mtx = mtx
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# self.dist = dist
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# self.w = w
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# self.h = h
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# self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
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# self.H = H # homography
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def undistort_img(self, img: MatLike):
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return cv2.undistort(img, self.mtx, self.dist, None, self.newcameramtx)
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def undistort_points(self, distorted_points: PointList):
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points = cv2.undistortPoints(np.array([distorted_points]).astype('float32'), self.mtx, self.dist, None, self.newcameramtx)
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return points[0]
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@dataclass
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@ -212,20 +324,22 @@ class Track:
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if not self.created_at:
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self.created_at = time.time()
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def get_projected_history(self, H: Optional[cv2.Mat] = None, camera: Optional[Camera]= None) -> np.array:
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def get_projected_history(self, H: Optional[cv2.Mat] = None, camera: Optional[DistortedCamera]= None) -> np.array:
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foot_coordinates = [d.get_foot_coords() for d in self.history]
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# TODO)) Undistort points before perspective transform
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if len(foot_coordinates):
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if camera:
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coords = cv2.undistortPoints(np.array([foot_coordinates]).astype('float32'), camera.mtx, camera.dist, None, camera.newcameramtx)
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coords = cv2.perspectiveTransform(np.array(coords),camera.H)
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return coords.reshape((coords.shape[0],2))
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coords = camera.points_img_to_world(foot_coordinates)
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return coords
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# coords = cv2.undistortPoints(np.array([foot_coordinates]).astype('float32'), camera.mtx, camera.dist, None, camera.newcameramtx)
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# coords = cv2.perspectiveTransform(np.array(coords),camera.H)
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# return coords.reshape((coords.shape[0],2))
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else:
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coords = cv2.perspectiveTransform(np.array([foot_coordinates]),H)
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return coords[0]
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return np.array([])
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def get_projected_history_as_dict(self, H, camera: Optional[Camera]= None) -> dict:
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def get_projected_history_as_dict(self, H, camera: Optional[DistortedCamera]= None) -> dict:
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coords = self.get_projected_history(H, camera)
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return [{"x":c[0], "y":c[1]} for c in coords]
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@ -551,10 +665,13 @@ class CameraAction(argparse.Action):
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if values is None:
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setattr(namespace, self.dest, None)
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else:
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camera = Camera.from_calibfile(Path(values), namespace.H, namespace.camera_fps)
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# values = Path(values)
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# with values.open('r') as fp:
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# data = json.load(fp)
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values = Path(values)
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with values.open('r') as fp:
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data = json.load(fp)
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if 'type' in data and data['type'] == 'fisheye':
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camera = FisheyeCamera.from_calibfile(Path(values), namespace.H, namespace.camera_fps)
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else:
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camera = Camera.from_calibfile(Path(values), namespace.H, namespace.camera_fps)
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# # print(data)
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# # print(data['camera_matrix'])
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# # camera = {
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@ -67,7 +67,7 @@ class CvRenderer:
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# TODO: get FPS from frame_emitter
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# self.out = cv2.VideoWriter(str(filename), fourcc, 23.97, (1280,720))
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self.fps = 60
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self.frame_size = (self.config.camera.w,self.config.camera.h)
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self.frame_size = (self.config.camera.projected_w,self.config.camera.projected_h)
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self.hide_stats = False
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self.out_writer = self.start_writer() if self.config.render_file else None
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self.streaming_process = self.start_streaming() if self.config.render_url else None
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@ -459,16 +459,16 @@ def get_animation_position(track: Track, current_frame: Frame):
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# Deprecated
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def decorate_frame(frame: Frame, tracker_frame: Frame, prediction_frame: Frame, first_time: float, config: Namespace, tracks: Dict[str, Track], predictions: Dict[str, Track], as_clusters = True, counter_listener: CounterListerner|None = None) -> np.array:
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scale = 100
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# TODO: replace opencv with QPainter to support alpha? https://doc.qt.io/qtforpython-5/PySide2/QtGui/QPainter.html#PySide2.QtGui.PySide2.QtGui.QPainter.drawImage
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# or https://github.com/pygobject/pycairo?tab=readme-ov-file
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# or https://pyglet.readthedocs.io/en/latest/programming_guide/shapes.html
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# and use http://code.astraw.com/projects/motmot/pygarrayimage.html or https://gist.github.com/nkymut/1cb40ea6ae4de0cf9ded7332f1ca0d55
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# or https://api.arcade.academy/en/stable/index.html (supports gradient color in line -- "Arcade is built on top of Pyglet and OpenGL.")
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undistorted_img = cv2.undistort(frame.img, config.camera.mtx, config.camera.dist, None, config.camera.newcameramtx)
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dst_img = cv2.warpPerspective(undistorted_img,convert_world_space_to_img_space(config.camera.H),(config.camera.w,config.camera.h))
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dst_img = frame.camera.img_to_world(frame.img, scale)
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# undistorted_img = cv2.undistort(frame.img, config.camera.mtx, config.camera.dist, None, config.camera.newcameramtx)
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# dst_img = cv2.warpPerspective(undistorted_img,convert_world_space_to_img_space(config.camera.H),(config.camera.w,config.camera.h))
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# dst_img2 = cv2.warpPerspective(undistorted_img,convert_world_space_to_img_space(config.camera.H), None)
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# cv2.imwrite('/home/ruben/suspicion/DATASETS/hof3/camera2.png', dst_img2)
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@ -489,12 +489,15 @@ def decorate_frame(frame: Frame, tracker_frame: Frame, prediction_frame: Frame,
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# cv2.imwrite(str(self.config.output_dir / "orig.png"), warpedFrame)
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cv2.rectangle(img, (0,0), (img.shape[1],25), (0,0,0), -1)
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def conversion(points):
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convert_world_points_to_img_points(points, scale)
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if not tracker_frame:
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cv2.putText(img, f"and track", (650,17), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
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else:
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for track_id, track in tracks.items():
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inv_H = np.linalg.pinv(tracker_frame.H)
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draw_track_projected(img, track, int(track_id), config.camera, convert_world_points_to_img_points)
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draw_track_projected(img, track, int(track_id), config.camera, conversion)
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if not prediction_frame:
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cv2.putText(img, f"Waiting for prediction...", (500,17), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
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@ -503,9 +506,9 @@ def decorate_frame(frame: Frame, tracker_frame: Frame, prediction_frame: Frame,
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for track_id, track in predictions.items():
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inv_H = np.linalg.pinv(prediction_frame.H)
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# For debugging:
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# draw_trackjectron_history(img, track, int(track.track_id), convert_world_points_to_img_points)
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# draw_trackjectron_history(img, track, int(track.track_id), conversion)
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anim_position = get_animation_position(track, frame)
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draw_track_predictions(img, track, int(track.track_id)+1, config.camera, convert_world_points_to_img_points, anim_position=anim_position, as_clusters=as_clusters)
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draw_track_predictions(img, track, int(track.track_id)+1, config.camera, conversion, anim_position=anim_position, as_clusters=as_clusters)
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cv2.putText(img, f"{len(track.predictor_history) if track.predictor_history else 'none'}", to_point(track.history[0].get_foot_coords()), cv2.FONT_HERSHEY_COMPLEX, 1, (255,255,255), 1)
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if prediction_frame.maps:
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for i, m in enumerate(prediction_frame.maps):
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@ -34,7 +34,7 @@ class RenderablePoint():
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def from_list(cls, l: List[float, float], color: SrgbaColor) -> RenderablePoint:
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return cls([float(l[0]), float(l[1])], color)
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SIMPLIFY_FACTOR_RDP = .05 # smaller is more detailed
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SIMPLIFY_FACTOR_RDP = .001 # smaller is more detailed
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SIMPLIFY_FACTOR_VW = 10
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class SimplifyMethod(Enum):
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@ -309,7 +309,7 @@ class DrawnScenario(TrackScenario):
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# if self.current_state is self.first_prediction or self.current_state is self.corrected_prediction:
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# shape = np.array(shapes.YOUR if time.time() % 2 > 1 else shapes.FUTURE)
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# text = "your" if time.time() % 2 > 1 else "future"
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# color = SrgbaColor(0.5,0.5,0.,1.-self.lost_factor())
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# color = SrgbaColor(0.5,0.5,0.5,1.-self.lost_factor())
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# line = self.get_text_lines(text, shape, color)
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# if not line:
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@ -79,19 +79,19 @@ def get_bins(bin_size: float):
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return [[bin_size, 0], [bin_size, bin_size], [0, bin_size], [-bin_size, bin_size], [-bin_size, 0], [-bin_size, -bin_size], [0, -bin_size], [bin_size, -bin_size]]
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||||
|
||||
def convert_world_space_to_img_space(H: cv2.Mat):
|
||||
def convert_world_space_to_img_space(H: cv2.Mat, scale=100):
|
||||
"""Transform the given matrix so that it immediately converts
|
||||
the points to img space"""
|
||||
new_H = H.copy()
|
||||
new_H[:2] = H[:2] * 100
|
||||
new_H[:2] = H[:2] * scale
|
||||
return new_H
|
||||
|
||||
def convert_world_points_to_img_points(points: Iterable):
|
||||
def convert_world_points_to_img_points(points: Iterable, scale=100):
|
||||
"""Transform the given matrix so that it immediately converts
|
||||
the points to img space"""
|
||||
if isinstance(points, np.ndarray):
|
||||
return np.array(points) * 100
|
||||
return [[p[0]*100, p[1]*100] for p in points]
|
||||
return np.array(points) * scale
|
||||
return [[p[0]*scale, p[1]*scale] for p in points]
|
||||
|
||||
def display_top(snapshot: tracemalloc.Snapshot, key_type='lineno', limit=5):
|
||||
snapshot = snapshot.filter_traces((
|
||||
|
|
|
|||
|
|
@ -1,9 +1,12 @@
|
|||
from dataclasses import dataclass
|
||||
from itertools import cycle
|
||||
import json
|
||||
import logging
|
||||
import math
|
||||
from os import PathLike
|
||||
from pathlib import Path
|
||||
import time
|
||||
from typing import Any, Generator, Iterable, List, Optional
|
||||
from typing import Any, Generator, Iterable, List, Literal, Optional, Tuple
|
||||
import neoapi
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
|
@ -21,23 +24,54 @@ class VideoSource:
|
|||
def __iter__(self):
|
||||
for i in self.recv():
|
||||
yield i
|
||||
|
||||
|
||||
BinningValue = Literal[1, 2]
|
||||
Coordinate = Tuple[int, int]
|
||||
|
||||
@dataclass
|
||||
class GigEConfig:
|
||||
identifier: Optional[str] = None
|
||||
binning_h: BinningValue = 1
|
||||
binning_v: BinningValue = 1
|
||||
pixel_format: int = neoapi.PixelFormat_BayerRG8
|
||||
|
||||
post_crop_tl: Optional[Coordinate] = None
|
||||
post_crop_br: Optional[Coordinate] = None
|
||||
|
||||
@classmethod
|
||||
def from_file(cls, file: PathLike):
|
||||
with open(file, 'r') as fp:
|
||||
return cls(**json.load(fp))
|
||||
|
||||
|
||||
class GigE(VideoSource):
|
||||
def __init__(self, identifier=None):
|
||||
def __init__(self, config=GigEConfig):
|
||||
|
||||
self.config = config
|
||||
|
||||
self.camera = neoapi.Cam()
|
||||
# self.camera.Connect('-B127')
|
||||
self.camera.Connect(identifier)
|
||||
self.camera.Connect(self.config.identifier)
|
||||
# Default buffer mode, streaming, always returns latest frame
|
||||
self.camera.SetImageBufferCount(10)
|
||||
# neoAPI docs: Setting the neoapi.Cam.SetImageBufferCycleCount()to one ensures that all buffers but one are given back to the neoAPI to be re-cycled and never given to the user by the neoapi.Cam.GetImage() method.
|
||||
self.camera.SetImageBufferCycleCount(1)
|
||||
self.setPixelFormat(self.config.pixel_format)
|
||||
|
||||
if self.camera.IsConnected():
|
||||
self.camera.f.PixelFormat.Set(neoapi.PixelFormat_RGB8)
|
||||
self.camera.f.BinningHorizontal.Set(2)
|
||||
self.camera.f.BinningVertical.Set(2)
|
||||
self.pixfmt = self.camera.f.PixelFormat.Get()
|
||||
# self.camera.f.PixelFormat.Set(neoapi.PixelFormat_RGB8)
|
||||
self.camera.f.BinningHorizontal.Set(self.config.binning_h)
|
||||
self.camera.f.BinningVertical.Set(self.config.binning_v)
|
||||
print('exposure time', self.camera.f.ExposureAutoMaxValue.Set(20000))
|
||||
print('exposure time', self.camera.f.ExposureTime.Get())
|
||||
# print('exposure time max', self.camera.f.ExposureTimeGapMax.Get())
|
||||
# print('exposure time min', self.camera.f.ExposureTimeGapMin.Get())
|
||||
# self.pixfmt = self.camera.f.PixelFormat.Get()
|
||||
|
||||
def setPixelFormat(self, pixfmt):
|
||||
self.pixfmt = pixfmt
|
||||
self.camera.f.PixelFormat.Set(pixfmt)
|
||||
# self.pixfmt = self.camera.f.PixelFormat.Get()
|
||||
|
||||
def recv(self):
|
||||
while True:
|
||||
|
|
@ -59,8 +93,16 @@ class GigE(VideoSource):
|
|||
|
||||
if img.dtype == np.uint16:
|
||||
img = cv2.convertScaleAbs(img, alpha=(255.0/65535.0))
|
||||
img = self._crop(img)
|
||||
yield img
|
||||
|
||||
def _crop(self, img):
|
||||
tl = self.config.post_crop_tl or (0,0)
|
||||
br = self.config.post_crop_br or (img.shape[1], img.shape[0])
|
||||
|
||||
return img[tl[1]:br[1],tl[0]:br[0],:]
|
||||
|
||||
|
||||
|
||||
class SingleCvVideoSource(VideoSource):
|
||||
def recv(self):
|
||||
|
|
@ -178,7 +220,8 @@ def get_video_source(video_sources: List[UrlOrPath], camera: Camera, frame_offse
|
|||
elif video_sources[0].url.scheme == 'gige':
|
||||
if frame_offset:
|
||||
logger.info("video-offset ignored for gige source")
|
||||
return GigE(video_sources[0].url.hostname)
|
||||
config = GigEConfig.from_file(Path(video_sources[0].url.netloc + video_sources[0].url.path))
|
||||
return GigE(config)
|
||||
else:
|
||||
return FilelistSource(video_sources, offset = frame_offset, end=frame_end, loop=loop)
|
||||
# os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "fflags;nobuffer|flags;low_delay|avioflags;direct|rtsp_transport;udp"
|
||||
|
|
|
|||
8
uv.lock
8
uv.lock
|
|
@ -211,10 +211,10 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "baumer-neoapi"
|
||||
version = "1.4.1"
|
||||
source = { path = "../../Downloads/Baumer_neoAPI_1.4.1_lin_x86_64_python/wheel/baumer_neoapi-1.4.1-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl" }
|
||||
version = "1.5.0"
|
||||
source = { path = "../../Downloads/Baumer_neoAPI_1.5.0_lin_x86_64_python/wheel/baumer_neoapi-1.5.0-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl" }
|
||||
wheels = [
|
||||
{ filename = "baumer_neoapi-1.4.1-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl", hash = "sha256:22e378f98cc4112f942db88fca9de74ba34fdc91a05512312aa57adf8e2cf84e" },
|
||||
{ filename = "baumer_neoapi-1.5.0-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl", hash = "sha256:ed16595f087694014ea16358b8d2ad7fe608db7608b22dc320db7dc01ae9deeb" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2508,7 +2508,7 @@ dependencies = [
|
|||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "baumer-neoapi", path = "../../Downloads/Baumer_neoAPI_1.4.1_lin_x86_64_python/wheel/baumer_neoapi-1.4.1-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl" },
|
||||
{ name = "baumer-neoapi", path = "../../Downloads/Baumer_neoAPI_1.5.0_lin_x86_64_python/wheel/baumer_neoapi-1.5.0-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl" },
|
||||
{ name = "bytetracker", git = "https://github.com/rubenvandeven/bytetrack-pip" },
|
||||
{ name = "deep-sort-realtime", specifier = ">=1.3.2,<2" },
|
||||
{ name = "facenet-pytorch", specifier = ">=2.5.3" },
|
||||
|
|
|
|||
Loading…
Reference in a new issue