Module 7_yolov3.lib.utils.datasets

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

help_url = 'https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data'
img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.dng']
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]

        # Convert
        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
        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]

        # Convert
        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
        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

        # check for common shapes
        s = np.stack([letterbox(x, new_shape=self.img_size)[0].shape for x in self.imgs], 0)  # inference shapes
        self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
        if not self.rect:
            print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')

    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, auto=self.rect, interp=cv2.INTER_LINEAR)[0] for x in img0]

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

        # Convert
        img = img[:, :, :, ::-1].transpose(0, 3, 1, 2)  # BGR to RGB, to 3x416x416, uint8 to float32
        img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32)
        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, img_dir, label_dir, img_size=416, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
                 cache_labels=False, cache_images=False, single_cls=False):
        img_dir = str(Path(img_dir))  # os-agnostic
        label_dir = str(Path(label_dir))  # os-agnostic

        assert os.path.isdir(img_dir), 'Img Dir not found %s. See %s' % (img_dir, help_url)
        assert os.path.isdir(label_dir), 'Img Dir not found %s. See %s' % (label_dir, help_url)

        img_files = sorted(os.listdir(img_dir))
        string = img_dir + "/"
        self.img_files = [string+x for x in img_files]


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

        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
        label_files = sorted(os.listdir(label_dir))
        string = label_dir + "/"
        self.label_files = [string+x for x in label_files]

        # Rectangular Training  https://github.com/ultralytics/yolov3/issues/232
        if self.rect:
            # Read image shapes (wh)
            sp = path.replace('.txt', '.shapes')  # 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]  # wh
            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='Caching labels')
            nm, nf, ne, ns, nd = 0, 0, 0, 0, 0  # number missing, found, empty, datasubset, duplicate
            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
                    if np.unique(l, axis=0).shape[0] < l.shape[0]:  # duplicate rows
                        nd += 1  # print('WARNING: duplicate rows in %s' % self.label_files[i])  # duplicate rows
                    if single_cls:
                        l[:, 0] = 0  # force dataset into single-class mode
                    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[:2]
                        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  # print('empty labels for image %s' % self.img_files[i])  # file empty
                    # os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i]))  # remove

                pbar.desc = 'Caching labels (%g found, %g missing, %g empty, %g duplicate, for %g images)' % (
                    nf, nm, ne, nd, n)
            assert nf > 0, 'No labels found. See %s' % help_url

        # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
        if cache_images:  # if training
            gb = 0  # Gigabytes of cached images
            pbar = tqdm(range(len(self.img_files)), desc='Caching images')
            self.img_hw0, self.img_hw = [None] * n, [None] * n
            for i in pbar:  # max 10k images
                self.imgs[i], self.img_hw0[i], self.img_hw[i] = load_image(self, i)  # img, hw_original, hw_resized
                gb += self.imgs[i].nbytes
                pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9)

        # 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.label_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]

        hyp = self.hyp
        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)
            shapes = None

        else:
            # Load image
            img, (h0, w0), (h, w) = load_image(self, index)

            # Letterbox
            shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size  # final letterboxed shape
            img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
            shapes = (h0, w0), ((h / h0, w / w0), pad)  # for COCO mAP rescaling

            # 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) + pad[0]  # pad width
                    labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + pad[1]  # pad height
                    labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0]
                    labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1]

        if self.augment:
            # Augment imagespace
            if not mosaic:
                img, labels = random_affine(img, labels,
                                            degrees=hyp['degrees'],
                                            translate=hyp['translate'],
                                            scale=hyp['scale'],
                                            shear=hyp['shear'])

            # Augment colorspace
            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])

            # 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)

        # Convert
        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
        img = np.ascontiguousarray(img)

        return torch.from_numpy(img), labels_out, img_path, shapes

    @staticmethod
    def collate_fn(batch):
        img, label, path, shapes = 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, shapes


def load_image(self, index):
    # loads 1 image from dataset, returns img, original hw, resized hw
    img = self.imgs[index]
    if img is None:  # not cached
        img_path = self.img_files[index]
        img = cv2.imread(img_path)  # BGR
        assert img is not None, 'Image Not Found ' + img_path
        h0, w0 = img.shape[:2]  # orig hw
        r = self.img_size / max(h0, w0)  # resize image to img_size
        if r < 1 or (self.augment and (r != 1)):  # always resize down, only resize up if training with augmentation
            interp = cv2.INTER_LINEAR if self.augment else cv2.INTER_AREA  # LINEAR for training, AREA for testing
            img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)
        return img, (h0, w0), img.shape[:2]  # img, hw_original, hw_resized
    else:
        return self.imgs[index], self.img_hw0[index], self.img_hw[index]  # img, hw_original, hw_resized


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 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, _, (h, w) = load_image(self, index)

        # 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
            else:
                labels = np.zeros((0, 5), dtype=np.float32)
            labels4.append(labels)

    # Concat/clip labels
    if len(labels4):
        labels4 = np.concatenate(labels4, 0)
        # np.clip(labels4[:, 1:] - s / 2, 0, s, out=labels4[:, 1:])  # use with center crop
        np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:])  # use with random_affine

    # Augment
    # img4 = img4[s // 2: int(s * 1.5), s // 2:int(s * 1.5)]  # center crop (WARNING, requires box pruning)
    img4, labels4 = random_affine(img4, labels4,
                                  degrees=self.hyp['degrees'] * 1,
                                  translate=self.hyp['translate'] * 1,
                                  scale=self.hyp['scale'] * 1,
                                  shear=self.hyp['shear'] * 1,
                                  border=-s // 2)  # border to remove

    return img4, labels4


def letterbox(img, new_shape=(416, 416), color=(128, 128, 128),
              auto=True, scaleFill=False, scaleup=True, interp=cv2.INTER_AREA):
    # Resize 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):
        new_shape = (new_shape, new_shape)

    # Scale ratio (new / old)
    r = max(new_shape) / max(shape)
    if not scaleup:  # only scale down, do not scale up (for better test mAP)
        r = min(r, 1.0)

    # Compute padding
    ratio = r, r  # width, height ratios
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
    if auto:  # minimum rectangle
        dw, dh = np.mod(dw, 32), np.mod(dh, 32)  # wh padding
    elif scaleFill:  # stretch
        dw, dh = 0.0, 0.0
        new_unpad = new_shape
        ratio = new_shape[0] / shape[1], new_shape[1] / shape[0]  # width, height ratios

    dw /= 2  # divide padding into 2 sides
    dh /= 2

    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, border=0):
    # 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 = []
    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))  # aspect ratio
        i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.2) & (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):
        # 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 reduce_img_size(path='../data/sm4/images', img_size=1024):  # from utils.datasets import *; reduce_img_size()
    # creates a new ./images_reduced folder with reduced size images of maximum size img_size
    path_new = path + '_reduced'  # reduced images path
    create_folder(path_new)
    for f in tqdm(glob.glob('%s/*.*' % path)):
        try:
            img = cv2.imread(f)
            h, w = img.shape[:2]
            r = img_size / max(h, w)  # size ratio
            if r < 1.0:
                img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_AREA)  # _LINEAR fastest
            fnew = f.replace(path, path_new)  # .replace(Path(f).suffix, '.jpg')
            cv2.imwrite(fnew, img)
        except:
            print('WARNING: image failure %s' % f)


def convert_images2bmp():  # from utils.datasets import *; convert_images2bmp()
    # Save images
    formats = [x.lower() for x in img_formats] + [x.upper() for x in img_formats]
    # for path in ['../coco/images/val2014', '../coco/images/train2014']:
    for path in ['../data/sm4/images', '../data/sm4/background']:
        create_folder(path + 'bmp')
        for ext in formats:  # ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.dng']
            for f in tqdm(glob.glob('%s/*%s' % (path, ext)), desc='Converting %s' % ext):
                cv2.imwrite(f.replace(ext.lower(), '.bmp').replace(path, path + 'bmp'), cv2.imread(f))

    # Save labels
    # for path in ['../coco/trainvalno5k.txt', '../coco/5k.txt']:
    for file in ['../data/sm4/out_train.txt', '../data/sm4/out_test.txt']:
        with open(file, 'r') as f:
            lines = f.read()
            # lines = f.read().replace('2014/', '2014bmp/')  # coco
            lines = lines.replace('/images', '/imagesbmp')
            lines = lines.replace('/background', '/backgroundbmp')
        for ext in formats:
            lines = lines.replace(ext, '.bmp')
        with open(file.replace('.txt', 'bmp.txt'), 'w') as f:
            f.write(lines)


def recursive_dataset2bmp(dataset='../data/sm4_bmp'):  # from utils.datasets import *; recursive_dataset2bmp()
    # Converts dataset to bmp (for faster training)
    formats = [x.lower() for x in img_formats] + [x.upper() for x in img_formats]
    for a, b, files in os.walk(dataset):
        for file in tqdm(files, desc=a):
            p = a + '/' + file
            s = Path(file).suffix
            if s == '.txt':  # replace text
                with open(p, 'r') as f:
                    lines = f.read()
                for f in formats:
                    lines = lines.replace(f, '.bmp')
                with open(p, 'w') as f:
                    f.write(lines)
            elif s in formats:  # replace image
                cv2.imwrite(p.replace(s, '.bmp'), cv2.imread(p))
                if s != '.bmp':
                    os.system("rm '%s'" % p)


def imagelist2folder(path='data/coco_64img.txt'):  # from utils.datasets import *; imagelist2folder()
    # Copies all the images in a text file (list of images) into a folder
    create_folder(path[:-4])
    with open(path, 'r') as f:
        for line in f.read().splitlines():
            os.system('cp "%s" %s' % (line, path[:-4]))
            print(line)


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