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import numpy as np

import albumentations.augmentations.functional as F

ternaus

black .
isort

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import albumentations as A

transform = A.Compose([
    A.HorizontalFlip(p=0.5),
    A.ShiftScaleRotate(border_mode=cv2.BORDER_CONSTANT, 
                          scale_limit=0.3,
                          rotate_limit=(10, 30),
                          p=0.5)
], p=1)

ternaus

import albumentations as A

transform = A.Compose([
    A.HorizontalFlip(p=0.5),
    A.GridDistortion(p=0.5),    
    A.RandomCrop(height=1024, width=1024, p=0.5),    
], p=1)

transformed = transform(image=image, masks=[mask, mask2])

ternaus

import albumentations as A

transform = A.Compose([A.HorizontalFlip(p=0.5),
                       A.ShiftScaleRotate(border_mode=cv2.BORDER_CONSTANT, scale_limit=0.3, p=0.5)],
                         bbox_params=albu.BboxParams(format='pascal_voc', label_fields=['category_ids']),
                         keypoint_params=albu.KeypointParams(format='xy'),
                         additional_targets={
                             "image1": "image",
                             "bboxes1": "bboxes",
                             "mask1": "mask",

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# https://github.com/ternaus/retinaface/blob/master/retinaface/pre_trained_models.py

from collections import namedtuple
from torch.utils import model_zoo
from retinaface.predict_single import Model

model = namedtuple("model", ["url", "model"])

models = {
    "resnet50_2020-07-20": model(

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# https://raw.githubusercontent.com/ternaus/retinaface/master/setup.py

import io
import os
import re
import sys
from shutil import rmtree
from typing import Tuple, List

from setuptools import Command, find_packages, setup

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import albumentations as A
import cv2

bgr_image = cv2.imread("cat1.jpeg")
image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)

transform = A.Compose([A.RandomResizedCrop(size=(512, 512), 
                                     scale=(0.08, 1), 
                                     ratio=(0.75, 1.33), 
                                     interpolation=cv2.INTER_AREA, p=0.5)])