car-detection-bayes/utils/datasets.py

import glob
import math
import os
import random
import shutil
import time
from pathlib import Path
from threading import Thread

import cv2
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import Dataset
from tqdm import tqdm

from utils.utils import xyxy2xywh, xywh2xyxy

img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif']
vid_formats = ['.mov', '.avi', '.mp4']

# Get orientation exif tag
for orientation in ExifTags.TAGS.keys():
    if ExifTags.TAGS[orientation] == 'Orientation':
        break


def exif_size(img):
    # Returns exif-corrected PIL size
    s = img.size  # (width, height)
    try:
        rotation = dict(img._getexif().items())[orientation]
        if rotation == 6:  # rotation 270
            s = (s[1], s[0])
        elif rotation == 8:  # rotation 90
            s = (s[1], s[0])
    except:
        pass

    return s


class LoadImages:  # for inference
    def __init__(self, path, img_size=416, half=False):
        path = str(Path(path))  # os-agnostic
        files = []
        if os.path.isdir(path):
            files = sorted(glob.glob(os.path.join(path, '*.*')))
        elif os.path.isfile(path):
            files = [path]

        images = [x for x in files if os.path.splitext(x)[-1].lower() in img_formats]
        videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats]
        nI, nV = len(images), len(videos)

        self.img_size = img_size
        self.files = images + videos
        self.nF = nI + nV  # number of files
        self.video_flag = [False] * nI + [True] * nV
        self.mode = 'images'
        self.half = half  # half precision fp16 images
        if any(videos):
            self.new_video(videos[0])  # new video
        else:
            self.cap = None
        assert self.nF > 0, 'No images or videos found in ' + path

    def __iter__(self):
        self.count = 0
        return self

    def __next__(self):
        if self.count == self.nF:
            raise StopIteration
        path = self.files[self.count]

        if self.video_flag[self.count]:
            # Read video
            self.mode = 'video'
            ret_val, img0 = self.cap.read()
            if not ret_val:
                self.count += 1
                self.cap.release()
                if self.count == self.nF:  # last video
                    raise StopIteration
                else:
                    path = self.files[self.count]
                    self.new_video(path)
                    ret_val, img0 = self.cap.read()

            self.frame += 1
            print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nF, self.frame, self.nframes, path), end='')

        else:
            # Read image
            self.count += 1
            img0 = cv2.imread(path)  # BGR
            assert img0 is not None, 'Image Not Found ' + path
            print('image %g/%g %s: ' % (self.count, self.nF, path), end='')

        # Padded resize
        img = letterbox(img0, new_shape=self.img_size)[0]

        # Normalize RGB
        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB
        img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32)  # uint8 to fp16/fp32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0

        # cv2.imwrite(path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1])  # save letterbox image
        return path, img, img0, self.cap

    def new_video(self, path):
        self.frame = 0
        self.cap = cv2.VideoCapture(path)
        self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))

    def __len__(self):
        return self.nF  # number of files


class LoadWebcam:  # for inference
    def __init__(self, pipe=0, img_size=416, half=False):
        self.img_size = img_size
        self.half = half  # half precision fp16 images

        if pipe == '0':
            pipe = 0  # local camera
        # pipe = 'rtsp://192.168.1.64/1'  # IP camera
        # pipe = 'rtsp://username:password@192.168.1.64/1'  # IP camera with login
        # pipe = 'rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa'  # IP traffic camera
        # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg'  # IP golf camera

        # https://answers.opencv.org/question/215996/changing-gstreamer-pipeline-to-opencv-in-pythonsolved/
        # pipe = '"rtspsrc location="rtsp://username:password@192.168.1.64/1" latency=10 ! appsink'  # GStreamer

        # https://answers.opencv.org/question/200787/video-acceleration-gstremer-pipeline-in-videocapture/
        # https://stackoverflow.com/questions/54095699/install-gstreamer-support-for-opencv-python-package  # install help
        # pipe = "rtspsrc location=rtsp://root:root@192.168.0.91:554/axis-media/media.amp?videocodec=h264&resolution=3840x2160 protocols=GST_RTSP_LOWER_TRANS_TCP ! rtph264depay ! queue ! vaapih264dec ! videoconvert ! appsink"  # GStreamer

        self.pipe = pipe
        self.cap = cv2.VideoCapture(pipe)  # video capture object
        self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3)  # set buffer size

    def __iter__(self):
        self.count = -1
        return self

    def __next__(self):
        self.count += 1
        if cv2.waitKey(1) == ord('q'):  # q to quit
            self.cap.release()
            cv2.destroyAllWindows()
            raise StopIteration

        # Read frame
        if self.pipe == 0:  # local camera
            ret_val, img0 = self.cap.read()
            img0 = cv2.flip(img0, 1)  # flip left-right
        else:  # IP camera
            n = 0
            while True:
                n += 1
                self.cap.grab()
                if n % 30 == 0:  # skip frames
                    ret_val, img0 = self.cap.retrieve()
                    if ret_val:
                        break

        # Print
        assert ret_val, 'Camera Error %s' % self.pipe
        img_path = 'webcam.jpg'
        print('webcam %g: ' % self.count, end='')

        # Padded resize
        img = letterbox(img0, new_shape=self.img_size)[0]

        # Normalize RGB
        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB
        img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32)  # uint8 to fp16/fp32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0

        return img_path, img, img0, None

    def __len__(self):
        return 0


class LoadStreams:  # multiple IP or RTSP cameras
    def __init__(self, sources='streams.txt', img_size=416, half=False):
        self.mode = 'images'
        self.img_size = img_size
        self.half = half  # half precision fp16 images

        if os.path.isfile(sources):
            with open(sources, 'r') as f:
                sources = [x.strip() for x in f.read().splitlines() if len(x.strip())]
        else:
            sources = [sources]

        n = len(sources)
        self.imgs = [None] * n
        self.sources = sources
        for i, s in enumerate(sources):
            # Start the thread to read frames from the video stream
            print('%g/%g: %s... ' % (i + 1, n, s), end='')
            cap = cv2.VideoCapture(0 if s == '0' else s)
            assert cap.isOpened(), 'Failed to open %s' % s
            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
            fps = cap.get(cv2.CAP_PROP_FPS) % 100
            _, self.imgs[i] = cap.read()  # guarantee first frame
            thread = Thread(target=self.update, args=([i, cap]), daemon=True)
            print(' success (%gx%g at %.2f FPS).' % (w, h, fps))
            thread.start()
        print('')  # newline

    def update(self, index, cap):
        # Read next stream frame in a daemon thread
        n = 0
        while cap.isOpened():
            n += 1
            # _, self.imgs[index] = cap.read()
            cap.grab()
            if n == 4:  # read every 4th frame
                _, self.imgs[index] = cap.retrieve()
                n = 0
            time.sleep(0.01)  # wait time

    def __iter__(self):
        self.count = -1
        return self

    def __next__(self):
        self.count += 1
        img0 = self.imgs.copy()
        if cv2.waitKey(1) == ord('q'):  # q to quit
            cv2.destroyAllWindows()
            raise StopIteration

        # Letterbox
        img = [letterbox(x, new_shape=self.img_size, interp=cv2.INTER_LINEAR)[0] for x in img0]

        # Stack
        img = np.stack(img, 0)

        # Normalize RGB
        img = img[:, :, :, ::-1].transpose(0, 3, 1, 2)  # BGR to RGB
        img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32)  # uint8 to fp16/fp32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0

        return self.sources, img, img0, None

    def __len__(self):
        return 0  # 1E12 frames = 32 streams at 30 FPS for 30 years


class LoadImagesAndLabels(Dataset):  # for training/testing
    def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=True, image_weights=False,
                 cache_labels=False, cache_images=False):
        path = str(Path(path))  # os-agnostic
        with open(path, 'r') as f:
            self.img_files = [x.replace('/', os.sep) for x in f.read().splitlines()  # os-agnostic
                              if os.path.splitext(x)[-1].lower() in img_formats]

        n = len(self.img_files)
        bi = np.floor(np.arange(n) / batch_size).astype(np.int)  # batch index
        nb = bi[-1] + 1  # number of batches
        assert n > 0, 'No images found in %s' % path

        self.n = n
        self.batch = bi  # batch index of image
        self.img_size = img_size
        self.augment = augment
        self.hyp = hyp
        self.image_weights = image_weights
        self.rect = False if image_weights else rect

        # Define labels
        self.label_files = [x.replace('images', 'labels').replace(os.path.splitext(x)[-1], '.txt')
                            for x in self.img_files]

        # Rectangular Training  https://github.com/ultralytics/yolov3/issues/232
        if self.rect:
            # Read image shapes
            sp = 'data' + os.sep + path.replace('.txt', '.shapes').split(os.sep)[-1]  # shapefile path
            try:
                with open(sp, 'r') as f:  # read existing shapefile
                    s = [x.split() for x in f.read().splitlines()]
                    assert len(s) == n, 'Shapefile out of sync'
            except:
                s = [exif_size(Image.open(f)) for f in tqdm(self.img_files, desc='Reading image shapes')]
                np.savetxt(sp, s, fmt='%g')  # overwrites existing (if any)

            # Sort by aspect ratio
            s = np.array(s, dtype=np.float64)
            ar = s[:, 1] / s[:, 0]  # aspect ratio
            i = ar.argsort()
            self.img_files = [self.img_files[i] for i in i]
            self.label_files = [self.label_files[i] for i in i]
            self.shapes = s[i]
            ar = ar[i]

            # Set training image shapes
            shapes = [[1, 1]] * nb
            for i in range(nb):
                ari = ar[bi == i]
                mini, maxi = ari.min(), ari.max()
                if maxi < 1:
                    shapes[i] = [maxi, 1]
                elif mini > 1:
                    shapes[i] = [1, 1 / mini]

            self.batch_shapes = np.ceil(np.array(shapes) * img_size / 32.).astype(np.int) * 32

        # Preload labels (required for weighted CE training)
        self.imgs = [None] * n
        self.labels = [None] * n
        if cache_labels or image_weights:  # cache labels for faster training
            self.labels = [np.zeros((0, 5))] * n
            extract_bounding_boxes = False
            create_datasubset = False
            pbar = tqdm(self.label_files, desc='Reading labels')
            nm, nf, ne, ns = 0, 0, 0, 0  # number missing, number found, number empty, number datasubset
            for i, file in enumerate(pbar):
                try:
                    with open(file, 'r') as f:
                        l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
                except:
                    nm += 1  # print('missing labels for image %s' % self.img_files[i])  # file missing
                    continue

                if l.shape[0]:
                    assert l.shape[1] == 5, '> 5 label columns: %s' % file
                    assert (l >= 0).all(), 'negative labels: %s' % file
                    assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file
                    self.labels[i] = l
                    nf += 1  # file found

                    # Create subdataset (a smaller dataset)
                    if create_datasubset and ns < 1E4:
                        if ns == 0:
                            create_folder(path='./datasubset')
                            os.makedirs('./datasubset/images')
                        exclude_classes = 43
                        if exclude_classes not in l[:, 0]:
                            ns += 1
                            # shutil.copy(src=self.img_files[i], dst='./datasubset/images/')  # copy image
                            with open('./datasubset/images.txt', 'a') as f:
                                f.write(self.img_files[i] + '\n')

                    # Extract object detection boxes for a second stage classifier
                    if extract_bounding_boxes:
                        p = Path(self.img_files[i])
                        img = cv2.imread(str(p))
                        h, w, _ = img.shape
                        for j, x in enumerate(l):
                            f = '%s%sclassifier%s%g_%g_%s' % (p.parent.parent, os.sep, os.sep, x[0], j, p.name)
                            if not os.path.exists(Path(f).parent):
                                os.makedirs(Path(f).parent)  # make new output folder

                            b = x[1:] * np.array([w, h, w, h])  # box
                            b[2:] = b[2:].max()  # rectangle to square
                            b[2:] = b[2:] * 1.3 + 30  # pad
                            b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)

                            b[[0, 2]] = np.clip(b[[0, 2]], 0, w)  # clip boxes outside of image
                            b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
                            assert cv2.imwrite(f, img[b[1]:b[3], b[0]:b[2]]), 'Failure extracting classifier boxes'
                else:
                    ne += 1  # file empty

                pbar.desc = 'Reading labels (%g found, %g missing, %g empty for %g images)' % (nf, nm, ne, n)
            assert nf > 0, 'No labels found. Recommend correcting image and label paths.'

        # Cache images into memory for faster training (~5GB)
        if cache_images and augment:  # if training
            for i in tqdm(range(min(len(self.img_files), 10000)), desc='Reading images'):  # max 10k images
                img_path = self.img_files[i]
                img = cv2.imread(img_path)  # BGR
                assert img is not None, 'Image Not Found ' + img_path
                r = self.img_size / max(img.shape)  # size ratio
                if self.augment and r < 1:  # if training (NOT testing), downsize to inference shape
                    h, w, _ = img.shape
                    img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR)  # or INTER_AREA
                self.imgs[i] = img

        # Detect corrupted images https://medium.com/joelthchao/programmatically-detect-corrupted-image-8c1b2006c3d3
        detect_corrupted_images = False
        if detect_corrupted_images:
            from skimage import io  # conda install -c conda-forge scikit-image
            for file in tqdm(self.img_files, desc='Detecting corrupted images'):
                try:
                    _ = io.imread(file)
                except:
                    print('Corrupted image detected: %s' % file)

    def __len__(self):
        return len(self.img_files)

    # def __iter__(self):
    #     self.count = -1
    #     print('ran dataset iter')
    #     #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
    #     return self

    def __getitem__(self, index):
        if self.image_weights:
            index = self.indices[index]

        img_path = self.img_files[index]
        label_path = self.label_files[index]

        mosaic = True and self.augment  # load 4 images at a time into a mosaic (only during training)
        if mosaic:
            # Load mosaic
            img, labels = load_mosaic(self, index)
            h, w, _ = img.shape

        else:
            # Load image
            img = load_image(self, index)

            # Letterbox
            h, w, _ = img.shape
            if self.rect:
                img, ratio, padw, padh = letterbox(img, self.batch_shapes[self.batch[index]], mode='rect')
            else:
                img, ratio, padw, padh = letterbox(img, self.img_size, mode='square')

            # Load labels
            labels = []
            if os.path.isfile(label_path):
                x = self.labels[index]
                if x is None:  # labels not preloaded
                    with open(label_path, 'r') as f:
                        x = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)

                if x.size > 0:
                    # Normalized xywh to pixel xyxy format
                    labels = x.copy()
                    labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + padw
                    labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + padh
                    labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + padw
                    labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + padh

        if self.augment:
            # Augment colorspace
            augment_hsv(img, hgain=self.hyp['hsv_h'], sgain=self.hyp['hsv_s'], vgain=self.hyp['hsv_v'])

            # Augment imagespace
            g = 0.0 if mosaic else 1.0  # do not augment mosaics
            hyp = self.hyp
            img, labels = random_affine(img, labels,
                                        degrees=hyp['degrees'] * g,
                                        translate=hyp['translate'] * g,
                                        scale=hyp['scale'] * g,
                                        shear=hyp['shear'] * g)

            # Apply cutouts
            # if random.random() < 0.9:
            #     labels = cutout(img, labels)

        nL = len(labels)  # number of labels
        if nL:
            # convert xyxy to xywh
            labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])

            # Normalize coordinates 0 - 1
            labels[:, [2, 4]] /= img.shape[0]  # height
            labels[:, [1, 3]] /= img.shape[1]  # width

        if self.augment:
            # random left-right flip
            lr_flip = True
            if lr_flip and random.random() < 0.5:
                img = np.fliplr(img)
                if nL:
                    labels[:, 1] = 1 - labels[:, 1]

            # random up-down flip
            ud_flip = False
            if ud_flip and random.random() < 0.5:
                img = np.flipud(img)
                if nL:
                    labels[:, 2] = 1 - labels[:, 2]

        labels_out = torch.zeros((nL, 6))
        if nL:
            labels_out[:, 1:] = torch.from_numpy(labels)

        # Normalize
        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
        img = np.ascontiguousarray(img, dtype=np.float32)  # uint8 to float32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0

        return torch.from_numpy(img), labels_out, img_path, (h, w)

    @staticmethod
    def collate_fn(batch):
        img, label, path, hw = list(zip(*batch))  # transposed
        for i, l in enumerate(label):
            l[:, 0] = i  # add target image index for build_targets()
        return torch.stack(img, 0), torch.cat(label, 0), path, hw


def load_image(self, index):
    # loads 1 image from dataset
    img = self.imgs[index]
    if img is None:
        img_path = self.img_files[index]
        img = cv2.imread(img_path)  # BGR
        assert img is not None, 'Image Not Found ' + img_path
        r = self.img_size / max(img.shape)  # size ratio
        if self.augment and r < 1.0:  # if training (NOT testing), downsize to inference shape
            h, w, _ = img.shape
            img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR)  # _LINEAR fastest
    return img


def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
    x = (np.random.uniform(-1, 1, 3) * np.array([hgain, sgain, vgain]) + 1).astype(np.float32)  # random gains
    img_hsv = (cv2.cvtColor(img, cv2.COLOR_BGR2HSV) * x.reshape((1, 1, 3))).clip(None, 255).astype(np.uint8)
    cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)  # no return needed


# def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):  # original version
#     # SV augmentation by 50%
#     img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)  # hue, sat, val
#
#     S = img_hsv[:, :, 1].astype(np.float32)  # saturation
#     V = img_hsv[:, :, 2].astype(np.float32)  # value
#
#     a = random.uniform(-1, 1) * sgain + 1
#     b = random.uniform(-1, 1) * vgain + 1
#     S *= a
#     V *= b
#
#     img_hsv[:, :, 1] = S if a < 1 else S.clip(None, 255)
#     img_hsv[:, :, 2] = V if b < 1 else V.clip(None, 255)
#     cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)  # no return needed


def load_mosaic(self, index):
    # loads images in a mosaic

    labels4 = []
    s = self.img_size
    xc, yc = [int(random.uniform(s * 0.5, s * 1.5)) for _ in range(2)]  # mosaic center x, y
    img4 = np.zeros((s * 2, s * 2, 3), dtype=np.uint8) + 128  # base image with 4 tiles
    indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)]  # 3 additional image indices
    for i, index in enumerate(indices):
        # Load image
        img = load_image(self, index)
        h, w, _ = img.shape

        # place img in img4
        if i == 0:  # top left
            x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
            x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)
        elif i == 1:  # top right
            x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
            x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
        elif i == 2:  # bottom left
            x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
            x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, max(xc, w), min(y2a - y1a, h)
        elif i == 3:  # bottom right
            x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
            x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)

        img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
        padw = x1a - x1b
        padh = y1a - y1b

        # Load labels
        label_path = self.label_files[index]
        if os.path.isfile(label_path):
            x = self.labels[index]
            if x is None:  # labels not preloaded
                with open(label_path, 'r') as f:
                    x = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)

            if x.size > 0:
                # Normalized xywh to pixel xyxy format
                labels = x.copy()
                labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padw
                labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + padh
                labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padw
                labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + padh

            labels4.append(labels)
    if len(labels4):
        labels4 = np.concatenate(labels4, 0)

    # hyp = self.hyp
    # img4, labels4 = random_affine(img4, labels4,
    #                               degrees=hyp['degrees'],
    #                               translate=hyp['translate'],
    #                               scale=hyp['scale'],
    #                               shear=hyp['shear'])

    # Center crop
    a = s // 2
    img4 = img4[a:a + s, a:a + s]
    if len(labels4):
        labels4[:, 1:] -= a

    return img4, labels4


def letterbox(img, new_shape=416, color=(128, 128, 128), mode='auto', interp=cv2.INTER_AREA):
    # Resize a rectangular image to a 32 pixel multiple rectangle
    # https://github.com/ultralytics/yolov3/issues/232
    shape = img.shape[:2]  # current shape [height, width]

    if isinstance(new_shape, int):
        r = float(new_shape) / max(shape)  # ratio  = new / old
    else:
        r = max(new_shape) / max(shape)
    ratio = r, r  # width, height ratios
    new_unpad = (int(round(shape[1] * r)), int(round(shape[0] * r)))

    # Compute padding https://github.com/ultralytics/yolov3/issues/232
    if mode is 'auto':  # minimum rectangle
        dw = np.mod(new_shape - new_unpad[0], 32) / 2  # width padding
        dh = np.mod(new_shape - new_unpad[1], 32) / 2  # height padding
    elif mode is 'square':  # square
        dw = (new_shape - new_unpad[0]) / 2  # width padding
        dh = (new_shape - new_unpad[1]) / 2  # height padding
    elif mode is 'rect':  # square
        dw = (new_shape[1] - new_unpad[0]) / 2  # width padding
        dh = (new_shape[0] - new_unpad[1]) / 2  # height padding
    elif mode is 'scaleFill':
        dw, dh = 0.0, 0.0
        new_unpad = (new_shape, new_shape)
        ratio = new_shape / shape[1], new_shape / shape[0]  # width, height ratios

    if shape[::-1] != new_unpad:  # resize
        img = cv2.resize(img, new_unpad, interpolation=interp)  # INTER_AREA is better, INTER_LINEAR is faster
    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    return img, ratio, dw, dh


def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10):
    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
    # https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4

    if targets is None:  # targets = [cls, xyxy]
        targets = []
    border = 0  # width of added border (optional)
    height = img.shape[0] + border * 2
    width = img.shape[1] + border * 2

    # Rotation and Scale
    R = np.eye(3)
    a = random.uniform(-degrees, degrees)
    # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
    s = random.uniform(1 - scale, 1 + scale)
    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)

    # Translation
    T = np.eye(3)
    T[0, 2] = random.uniform(-translate, translate) * img.shape[0] + border  # x translation (pixels)
    T[1, 2] = random.uniform(-translate, translate) * img.shape[1] + border  # y translation (pixels)

    # Shear
    S = np.eye(3)
    S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # x shear (deg)
    S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # y shear (deg)

    # Combined rotation matrix
    M = S @ T @ R  # ORDER IS IMPORTANT HERE!!
    changed = (border != 0) or (M != np.eye(3)).any()
    if changed:
        img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_AREA, borderValue=(128, 128, 128))

    # Transform label coordinates
    n = len(targets)
    if n:
        # warp points
        xy = np.ones((n * 4, 3))
        xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
        xy = (xy @ M.T)[:, :2].reshape(n, 8)

        # create new boxes
        x = xy[:, [0, 2, 4, 6]]
        y = xy[:, [1, 3, 5, 7]]
        xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T

        # # apply angle-based reduction of bounding boxes
        # radians = a * math.pi / 180
        # reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
        # x = (xy[:, 2] + xy[:, 0]) / 2
        # y = (xy[:, 3] + xy[:, 1]) / 2
        # w = (xy[:, 2] - xy[:, 0]) * reduction
        # h = (xy[:, 3] - xy[:, 1]) * reduction
        # xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T

        # reject warped points outside of image
        xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
        xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
        w = xy[:, 2] - xy[:, 0]
        h = xy[:, 3] - xy[:, 1]
        area = w * h
        area0 = (targets[:, 3] - targets[:, 1]) * (targets[:, 4] - targets[:, 2])
        ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
        i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10)

        targets = targets[i]
        targets[:, 1:5] = xy[i]

    return img, targets


def cutout(image, labels):
    # https://arxiv.org/abs/1708.04552
    # https://github.com/hysts/pytorch_cutout/blob/master/dataloader.py
    # https://towardsdatascience.com/when-conventional-wisdom-fails-revisiting-data-augmentation-for-self-driving-cars-4831998c5509
    h, w = image.shape[:2]

    def bbox_ioa(box1, box2, x1y1x2y2=True):
        # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2
        box2 = box2.transpose()

        # Get the coordinates of bounding boxes
        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]

        # Intersection area
        inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
                     (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)

        # box2 area
        box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16

        # Intersection over box2 area
        return inter_area / box2_area

    # create random masks
    scales = [0.5] * 1  # + [0.25] * 4 + [0.125] * 16 + [0.0625] * 64 + [0.03125] * 256  # image size fraction
    for s in scales:
        mask_h = random.randint(1, int(h * s))
        mask_w = random.randint(1, int(w * s))

        # box
        xmin = max(0, random.randint(0, w) - mask_w // 2)
        ymin = max(0, random.randint(0, h) - mask_h // 2)
        xmax = min(w, xmin + mask_w)
        ymax = min(h, ymin + mask_h)

        # apply random color mask
        mask_color = [random.randint(0, 255) for _ in range(3)]
        image[ymin:ymax, xmin:xmax] = mask_color

        # return unobscured labels
        if len(labels) and s > 0.03:
            box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
            ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area
            labels = labels[ioa < 0.90]  # remove >90% obscured labels

    return labels


def convert_images2bmp():
    # cv2.imread() jpg at 230 img/s, *.bmp at 400 img/s
    for path in ['../coco/images/val2014/', '../coco/images/train2014/']:
        folder = os.sep + Path(path).name
        output = path.replace(folder, folder + 'bmp')
        if os.path.exists(output):
            shutil.rmtree(output)  # delete output folder
        os.makedirs(output)  # make new output folder

        for f in tqdm(glob.glob('%s*.jpg' % path)):
            save_name = f.replace('.jpg', '.bmp').replace(folder, folder + 'bmp')
            cv2.imwrite(save_name, cv2.imread(f))

    for label_path in ['../coco/trainvalno5k.txt', '../coco/5k.txt']:
        with open(label_path, 'r') as file:
            lines = file.read()
        lines = lines.replace('2014/', '2014bmp/').replace('.jpg', '.bmp').replace(
            '/Users/glennjocher/PycharmProjects/', '../')
        with open(label_path.replace('5k', '5k_bmp'), 'w') as file:
            file.write(lines)


def create_folder(path='./new_folder'):
    # Create folder
    if os.path.exists(path):
        shutil.rmtree(path)  # delete output folder
    os.makedirs(path)  # make new output folder