j-adamczyk

from detectron2 import model_zoo
from detectron2.config import get_cfg, CfgNode
from detectron2.engine import DefaultPredictor


if __name__ == "__main__":
    args: argparse.Namespace = _get_parsed_args()

    cfg: CfgNode = get_cfg()
    cfg.merge_from_file(model_zoo.get_config_file(args.base_model))

j-adamczyk

import cv2
import numpy as np
import re

from detectron2.data import MetadataCatalog
from detectron2.structures import Instances
from detectron2.utils.visualizer import Visualizer, VisImage


if __name__ == "__main__":

j-adamczyk

import argparse

import cv2
import numpy as np
import re

from detectron2 import model_zoo
from detectron2.config import get_cfg, CfgNode
from detectron2.data import MetadataCatalog
from detectron2.engine import DefaultPredictor

j-adamczyk

import argparse


def _get_parsed_args() -> argparse.Namespace:
    """
    Create an argument parser and parse arguments.
    :return: parsed arguments as a Namespace object
    """

    parser = argparse.ArgumentParser(description="Detectron2 demo")

j-adamczyk

import numpy as np
import faiss


class FaissKNeighbors:
    def __init__(self, k=5):
        self.index = None
        self.y = None
        self.k = k

j-adamczyk

import faiss
import numpy as np


class FaissKMeans:
    def __init__(self, n_clusters=8, n_init=10, max_iter=300):
        self.n_clusters = n_clusters
        self.n_init = n_init
        self.max_iter = max_iter
        self.kmeans = None

j-adamczyk

from typing import List

import cv2
import graphviz
import numpy as np
from sklearn.tree import DecisionTreeClassifier, export_graphviz


def plot_in_memory(clf: DecisionTreeClassifier,
                   feature_names: List[str],

j-adamczyk

import os
from typing import List

import graphviz
import matplotlib.image as plt_img
import numpy as np
from sklearn.tree import DecisionTreeClassifier, export_graphviz


def plot_disk_operations(clf: DecisionTreeClassifier,

j-adamczyk

import cv2
import numpy as np

face_cascade = cv2.CascadeClassifier()
face_cascade.load("haarcascade_frontalface_alt.xml")


def viola_jones_detect(img: np.ndarray) -> np.ndarray:
    frame_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    frame_gray = cv2.equalizeHist(frame_gray)

j-adamczyk

import cv2
from facenet_pytorch.models.mtcnn import MTCNN
import numpy as np
import torch

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
mtcnn = MTCNN(keep_all=True, device=device)


def mtcnn_detect(img: np.ndarray) -> np.ndarray: