fed-yolo/utils/dataset.py

import math
import os
import random

import cv2
import numpy
import torch
from PIL import Image
from torch.utils import data

FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp"


class Dataset(data.Dataset):
    def __init__(self, filenames, input_size, params, augment):
        self.params = params
        self.mosaic = augment
        self.augment = augment
        self.input_size = input_size

        # Read labels
        labels = self.load_label(filenames)
        self.labels = list(labels.values())
        self.filenames = list(labels.keys())  # update
        self.n = len(self.filenames)  # number of samples
        self.indices = range(self.n)
        # Albumentations (optional, only used if package is installed)
        self.albumentations = Albumentations()

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

        if self.mosaic and random.random() < self.params["mosaic"]:
            # Load MOSAIC
            image, label = self.load_mosaic(index, self.params)
            # MixUp augmentation
            if random.random() < self.params["mix_up"]:
                index = random.choice(self.indices)
                mix_image1, mix_label1 = image, label
                mix_image2, mix_label2 = self.load_mosaic(index, self.params)

                image, label = mix_up(mix_image1, mix_label1, mix_image2, mix_label2)
        else:
            # Load image
            image, shape = self.load_image(index)
            h, w = image.shape[:2]

            # Resize
            image, ratio, pad = resize(image, self.input_size, self.augment)

            label = self.labels[index].copy()
            if label.size:
                label[:, 1:] = wh2xy(label[:, 1:], ratio[0] * w, ratio[1] * h, pad[0], pad[1])
            if self.augment:
                image, label = random_perspective(image, label, self.params)

        nl = len(label)  # number of labels
        h, w = image.shape[:2]
        cls = label[:, 0:1]
        box = label[:, 1:5]
        box = xy2wh(box, w, h)

        if self.augment:
            # Albumentations
            image, box, cls = self.albumentations(image, box, cls)
            nl = len(box)  # update after albumentations
            # HSV color-space
            augment_hsv(image, self.params)
            # Flip up-down
            if random.random() < self.params["flip_ud"]:
                image = numpy.flipud(image)
                if nl:
                    box[:, 1] = 1 - box[:, 1]
            # Flip left-right
            if random.random() < self.params["flip_lr"]:
                image = numpy.fliplr(image)
                if nl:
                    box[:, 0] = 1 - box[:, 0]

        # XXX: when nl=0, torch.from_numpy(box) will error
        if nl:
            target_cls = torch.from_numpy(cls).view(-1, 1).float()  # always (N,1)
            target_box = torch.from_numpy(box).reshape(-1, 4).float()  # always (N,4)
        else:
            target_cls = torch.zeros((0, 1), dtype=torch.float32)
            target_box = torch.zeros((0, 4), dtype=torch.float32)
        # target_cls = torch.zeros((nl, 1))
        # target_box = torch.zeros((nl, 4))
        # if nl:
        #     target_cls = torch.from_numpy(cls)
        #     target_box = torch.from_numpy(box)

        # Convert HWC to CHW, BGR to RGB
        sample = image.transpose((2, 0, 1))[::-1]
        sample = numpy.ascontiguousarray(sample)

        # return torch.from_numpy(sample), target_cls, target_box, torch.zeros(nl)
        return torch.from_numpy(sample), target_cls, target_box, torch.zeros((nl, 1), dtype=torch.long)

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

    def load_image(self, i):
        image = cv2.imread(self.filenames[i])
        if image is None:
            raise FileNotFoundError(f"Image Not Found {self.filenames[i]}")
        h, w = image.shape[:2]
        r = self.input_size / max(h, w)
        if r != 1:
            image = cv2.resize(
                image, dsize=(int(w * r), int(h * r)), interpolation=resample() if self.augment else cv2.INTER_LINEAR
            )
        return image, (h, w)

    def load_mosaic(self, index, params):
        label4 = []
        border = [-self.input_size // 2, -self.input_size // 2]
        image4 = numpy.full((self.input_size * 2, self.input_size * 2, 3), 0, dtype=numpy.uint8)
        y1a, y2a, x1a, x2a, y1b, y2b, x1b, x2b = (None, None, None, None, None, None, None, None)

        xc = int(random.uniform(-border[0], 2 * self.input_size + border[1]))
        yc = int(random.uniform(-border[0], 2 * self.input_size + border[1]))

        indices = [index] + random.choices(self.indices, k=3)
        random.shuffle(indices)

        for i, index in enumerate(indices):
            # Load image
            image, _ = self.load_image(index)
            shape = image.shape
            if i == 0:  # top left
                x1a = max(xc - shape[1], 0)
                y1a = max(yc - shape[0], 0)
                x2a = xc
                y2a = yc
                x1b = shape[1] - (x2a - x1a)
                y1b = shape[0] - (y2a - y1a)
                x2b = shape[1]
                y2b = shape[0]
            if i == 1:  # top right
                x1a = xc
                y1a = max(yc - shape[0], 0)
                x2a = min(xc + shape[1], self.input_size * 2)
                y2a = yc
                x1b = 0
                y1b = shape[0] - (y2a - y1a)
                x2b = min(shape[1], x2a - x1a)
                y2b = shape[0]
            if i == 2:  # bottom left
                x1a = max(xc - shape[1], 0)
                y1a = yc
                x2a = xc
                y2a = min(self.input_size * 2, yc + shape[0])
                x1b = shape[1] - (x2a - x1a)
                y1b = 0
                x2b = shape[1]
                y2b = min(y2a - y1a, shape[0])
            if i == 3:  # bottom right
                x1a = xc
                y1a = yc
                x2a = min(xc + shape[1], self.input_size * 2)
                y2a = min(self.input_size * 2, yc + shape[0])
                x1b = 0
                y1b = 0
                x2b = min(shape[1], x2a - x1a)
                y2b = min(y2a - y1a, shape[0])

            pad_w = x1a - x1b
            pad_h = y1a - y1b
            image4[y1a:y2a, x1a:x2a] = image[y1b:y2b, x1b:x2b]

            # Labels
            label = self.labels[index].copy()
            if len(label):
                label[:, 1:] = wh2xy(label[:, 1:], shape[1], shape[0], pad_w, pad_h)
            label4.append(label)

        # Concat/clip labels
        label4 = numpy.concatenate(label4, 0)
        for x in label4[:, 1:]:
            numpy.clip(x, 0, 2 * self.input_size, out=x)

        # Augment
        image4, label4 = random_perspective(image4, label4, params, border)

        return image4, label4

    @staticmethod
    def collate_fn(batch):
        samples, cls, box, indices = zip(*batch)

        cls = torch.cat(cls, dim=0)
        box = torch.cat(box, dim=0)

        new_indices = list(indices)
        for i in range(len(indices)):
            new_indices[i] += i
        indices = torch.cat(new_indices, dim=0)

        targets = {"cls": cls, "box": box, "idx": indices}
        return torch.stack(samples, dim=0), targets

    @staticmethod
    def load_label(filenames):
        path = f"{os.path.dirname(filenames[0])}.cache"
        if os.path.exists(path):
            # XXX: temporarily disable cache
            os.remove(path)
            pass
            # return torch.load(path, weights_only=False)
        x = {}
        for filename in filenames:
            try:
                # verify images
                with open(filename, "rb") as f:
                    image = Image.open(f)
                    image.verify()  # PIL verify
                shape = image.size  # image size
                assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels"
                assert image.format.lower() in FORMATS, f"invalid image format {image.format}"

                # verify labels
                a = f"{os.sep}images{os.sep}"
                b = f"{os.sep}labels{os.sep}"
                if os.path.isfile(b.join(filename.rsplit(a, 1)).rsplit(".", 1)[0] + ".txt"):
                    with open(b.join(filename.rsplit(a, 1)).rsplit(".", 1)[0] + ".txt") as f:
                        label = [x.split() for x in f.read().strip().splitlines() if len(x)]
                        label = numpy.array(label, dtype=numpy.float32)
                    nl = len(label)
                    if nl:
                        assert (label >= 0).all()
                        assert label.shape[1] == 5
                        assert (label[:, 1:] <= 1).all()
                        _, i = numpy.unique(label, axis=0, return_index=True)
                        if len(i) < nl:  # duplicate row check
                            label = label[i]  # remove duplicates
                    else:
                        label = numpy.zeros((0, 5), dtype=numpy.float32)
                else:
                    label = numpy.zeros((0, 5), dtype=numpy.float32)
            except FileNotFoundError:
                label = numpy.zeros((0, 5), dtype=numpy.float32)
            except AssertionError:
                continue
            x[filename] = label
        torch.save(x, path)
        return x


def wh2xy(x, w=640, h=640, pad_w=0, pad_h=0):
    # Convert nx4 boxes
    # from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
    y = numpy.copy(x)
    y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + pad_w  # top left x
    y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + pad_h  # top left y
    y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + pad_w  # bottom right x
    y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + pad_h  # bottom right y
    return y


def xy2wh(x, w, h):
    # warning: inplace clip
    x[:, [0, 2]] = x[:, [0, 2]].clip(0, w - 1e-3)  # x1, x2
    x[:, [1, 3]] = x[:, [1, 3]].clip(0, h - 1e-3)  # y1, y2

    # Convert nx4 boxes
    # from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right
    y = numpy.copy(x)
    y[:, 0] = ((x[:, 0] + x[:, 2]) / 2) / w  # x center
    y[:, 1] = ((x[:, 1] + x[:, 3]) / 2) / h  # y center
    y[:, 2] = (x[:, 2] - x[:, 0]) / w  # width
    y[:, 3] = (x[:, 3] - x[:, 1]) / h  # height
    return y


def resample():
    choices = (cv2.INTER_AREA, cv2.INTER_CUBIC, cv2.INTER_LINEAR, cv2.INTER_NEAREST, cv2.INTER_LANCZOS4)
    return random.choice(seq=choices)


def augment_hsv(image, params):
    # HSV color-space augmentation
    h = params["hsv_h"]
    s = params["hsv_s"]
    v = params["hsv_v"]

    r = numpy.random.uniform(-1, 1, 3) * [h, s, v] + 1
    h, s, v = cv2.split(cv2.cvtColor(image, cv2.COLOR_BGR2HSV))

    x = numpy.arange(0, 256, dtype=r.dtype)
    lut_h = ((x * r[0]) % 180).astype("uint8")
    lut_s = numpy.clip(x * r[1], 0, 255).astype("uint8")
    lut_v = numpy.clip(x * r[2], 0, 255).astype("uint8")

    hsv = cv2.merge((cv2.LUT(h, lut_h), cv2.LUT(s, lut_s), cv2.LUT(v, lut_v)))
    cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR, dst=image)  # no return needed


def resize(image, input_size, augment):
    # Resize and pad image while meeting stride-multiple constraints
    shape = image.shape[:2]  # current shape [height, width]

    # Scale ratio (new / old)
    r = min(input_size / shape[0], input_size / shape[1])
    if not augment:  # only scale down, do not scale up (for better val mAP)
        r = min(r, 1.0)

    # Compute padding
    pad = int(round(shape[1] * r)), int(round(shape[0] * r))
    w = (input_size - pad[0]) / 2
    h = (input_size - pad[1]) / 2

    if shape[::-1] != pad:  # resize
        image = cv2.resize(image, dsize=pad, interpolation=resample() if augment else cv2.INTER_LINEAR)
    top, bottom = int(round(h - 0.1)), int(round(h + 0.1))
    left, right = int(round(w - 0.1)), int(round(w + 0.1))
    image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT)  # add border
    return image, (r, r), (w, h)


def candidates(box1, box2):
    # box1(4,n), box2(4,n)
    w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
    w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
    aspect_ratio = numpy.maximum(w2 / (h2 + 1e-16), h2 / (w2 + 1e-16))  # aspect ratio
    return (w2 > 2) & (h2 > 2) & (w2 * h2 / (w1 * h1 + 1e-16) > 0.1) & (aspect_ratio < 100)


def random_perspective(image, label, params, border=(0, 0)):
    h = image.shape[0] + border[0] * 2
    w = image.shape[1] + border[1] * 2

    # Center
    center = numpy.eye(3)
    center[0, 2] = -image.shape[1] / 2  # x translation (pixels)
    center[1, 2] = -image.shape[0] / 2  # y translation (pixels)

    # Perspective
    perspective = numpy.eye(3)

    # Rotation and Scale
    rotate = numpy.eye(3)
    a = random.uniform(-params["degrees"], params["degrees"])
    s = random.uniform(1 - params["scale"], 1 + params["scale"])
    rotate[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)

    # Shear
    shear = numpy.eye(3)
    shear[0, 1] = math.tan(random.uniform(-params["shear"], params["shear"]) * math.pi / 180)
    shear[1, 0] = math.tan(random.uniform(-params["shear"], params["shear"]) * math.pi / 180)

    # Translation
    translate = numpy.eye(3)
    translate[0, 2] = random.uniform(0.5 - params["translate"], 0.5 + params["translate"]) * w
    translate[1, 2] = random.uniform(0.5 - params["translate"], 0.5 + params["translate"]) * h

    # Combined rotation matrix, order of operations (right to left) is IMPORTANT
    matrix = translate @ shear @ rotate @ perspective @ center
    if (border[0] != 0) or (border[1] != 0) or (matrix != numpy.eye(3)).any():  # image changed
        image = cv2.warpAffine(image, matrix[:2], dsize=(w, h), borderValue=(0, 0, 0))

    # Transform label coordinates
    n = len(label)
    if n:
        xy = numpy.ones((n * 4, 3))
        xy[:, :2] = label[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
        xy = xy @ matrix.T  # transform
        xy = xy[:, :2].reshape(n, 8)  # perspective rescale or affine

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

        # clip
        box[:, [0, 2]] = box[:, [0, 2]].clip(0, w)
        box[:, [1, 3]] = box[:, [1, 3]].clip(0, h)
        # filter candidates
        indices = candidates(box1=label[:, 1:5].T * s, box2=box.T)

        label = label[indices]
        label[:, 1:5] = box[indices]

    return image, label


def mix_up(image1, label1, image2, label2):
    # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
    alpha = numpy.random.beta(a=32.0, b=32.0)  # mix-up ratio, alpha=beta=32.0
    image = (image1 * alpha + image2 * (1 - alpha)).astype(numpy.uint8)
    label = numpy.concatenate((label1, label2), 0)
    return image, label


class Albumentations:
    def __init__(self):
        self.transform = None
        try:
            import albumentations

            transforms = [
                albumentations.Blur(p=0.01),
                albumentations.CLAHE(p=0.01),
                albumentations.ToGray(p=0.01),
                albumentations.MedianBlur(p=0.01),
            ]
            self.transform = albumentations.Compose(transforms, albumentations.BboxParams("yolo", ["class_labels"]))

        except ImportError:  # package not installed, skip
            pass

    def __call__(self, image, box, cls):
        if self.transform:
            x = self.transform(image=image, bboxes=box, class_labels=cls)
            image = x["image"]
            box = numpy.array(x["bboxes"])
            cls = numpy.array(x["class_labels"])
        return image, box, cls