YOLOv8-16bit/ultralytics/yolo/data/utils.py

import contextlib
import hashlib
import os

import cv2
import numpy as np
from PIL import ExifTags, Image, ImageOps

from ..utils.ops import segments2boxes

HELP_URL = "See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data"
IMG_FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm"  # include image suffixes
VID_FORMATS = "asf", "avi", "gif", "m4v", "mkv", "mov", "mp4", "mpeg", "mpg", "ts", "wmv"  # include video suffixes
BAR_FORMAT = "{l_bar}{bar:10}{r_bar}{bar:-10b}"  # tqdm bar format
LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1))  # https://pytorch.org/docs/stable/elastic/run.html
RANK = int(os.getenv('RANK', -1))
PIN_MEMORY = str(os.getenv("PIN_MEMORY", True)).lower() == "true"  # global pin_memory for dataloaders
IMAGENET_MEAN = 0.485, 0.456, 0.406  # RGB mean
IMAGENET_STD = 0.229, 0.224, 0.225  # RGB standard deviation

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


def img2label_paths(img_paths):
    # Define label paths as a function of image paths
    sa, sb = f"{os.sep}images{os.sep}", f"{os.sep}labels{os.sep}"  # /images/, /labels/ substrings
    return [sb.join(x.rsplit(sa, 1)).rsplit(".", 1)[0] + ".txt" for x in img_paths]


def get_hash(paths):
    # Returns a single hash value of a list of paths (files or dirs)
    size = sum(os.path.getsize(p) for p in paths if os.path.exists(p))  # sizes
    h = hashlib.md5(str(size).encode())  # hash sizes
    h.update("".join(paths).encode())  # hash paths
    return h.hexdigest()  # return hash


def exif_size(img):
    # Returns exif-corrected PIL size
    s = img.size  # (width, height)
    with contextlib.suppress(Exception):
        rotation = dict(img._getexif().items())[orientation]
        if rotation in [6, 8]:  # rotation 270 or 90
            s = (s[1], s[0])
    return s


def verify_image_label(args):
    # Verify one image-label pair
    im_file, lb_file, prefix, keypoint = args
    nm, nf, ne, nc, msg, segments, keypoints = 0, 0, 0, 0, "", None, None  # number (missing, found, empty, corrupt), message, segments, keypoints
    try:
        # verify images
        im = Image.open(im_file)
        im.verify()  # PIL verify
        shape = exif_size(im)  # image size
        shape = (shape[1], shape[0])  # hw
        assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels"
        assert im.format.lower() in IMG_FORMATS, f"invalid image format {im.format}"
        if im.format.lower() in ("jpg", "jpeg"):
            with open(im_file, "rb") as f:
                f.seek(-2, 2)
                if f.read() != b"\xff\xd9":  # corrupt JPEG
                    ImageOps.exif_transpose(Image.open(im_file)).save(im_file, "JPEG", subsampling=0, quality=100)
                    msg = f"{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved"

        # verify labels
        if os.path.isfile(lb_file):
            nf = 1  # label found
            with open(lb_file) as f:
                lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
                if any(len(x) > 6 for x in lb) and (not keypoint):  # is segment
                    classes = np.array([x[0] for x in lb], dtype=np.float32)
                    segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...)
                    lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
                lb = np.array(lb, dtype=np.float32)
            nl = len(lb)
            if nl:
                if keypoint:
                    assert lb.shape[1] == 56, "labels require 56 columns each"
                    assert (lb[:, 5::3] <= 1).all(), "non-normalized or out of bounds coordinate labels"
                    assert (lb[:, 6::3] <= 1).all(), "non-normalized or out of bounds coordinate labels"
                    kpts = np.zeros((lb.shape[0], 39))
                    for i in range(len(lb)):
                        kpt = np.delete(lb[i, 5:], np.arange(2, lb.shape[1] - 5,
                                                             3))  # remove the occlusion paramater from the GT
                        kpts[i] = np.hstack((lb[i, :5], kpt))
                    lb = kpts
                    assert lb.shape[1] == 39, "labels require 39 columns each after removing occlusion paramater"
                else:
                    assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected"
                    assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}"
                    assert (lb[:, 1:] <=
                            1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"
                _, i = np.unique(lb, axis=0, return_index=True)
                if len(i) < nl:  # duplicate row check
                    lb = lb[i]  # remove duplicates
                    if segments:
                        segments = [segments[x] for x in i]
                    msg = f"{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed"
            else:
                ne = 1  # label empty
                lb = np.zeros((0, 39), dtype=np.float32) if keypoint else np.zeros((0, 5), dtype=np.float32)
        else:
            nm = 1  # label missing
            lb = np.zeros((0, 39), dtype=np.float32) if keypoint else np.zeros((0, 5), dtype=np.float32)
        if keypoint:
            keypoints = lb[:, 5:].reshape(-1, 17, 2)
        lb = lb[:, :5]
        return im_file, lb, shape, segments, keypoints, nm, nf, ne, nc, msg
    except Exception as e:
        nc = 1
        msg = f"{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}"
        return [None, None, None, None, None, nm, nf, ne, nc, msg]


def polygon2mask(img_size, polygons, color=1, downsample_ratio=1):
    """
    Args:
        img_size (tuple): The image size.
        polygons (np.ndarray): [N, M], N is the number of polygons,
            M is the number of points(Be divided by 2).
    """
    mask = np.zeros(img_size, dtype=np.uint8)
    polygons = np.asarray(polygons)
    polygons = polygons.astype(np.int32)
    shape = polygons.shape
    polygons = polygons.reshape(shape[0], -1, 2)
    cv2.fillPoly(mask, polygons, color=color)
    nh, nw = (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio)
    # NOTE: fillPoly firstly then resize is trying the keep the same way
    # of loss calculation when mask-ratio=1.
    mask = cv2.resize(mask, (nw, nh))
    return mask


def polygons2masks(img_size, polygons, color, downsample_ratio=1):
    """
    Args:
        img_size (tuple): The image size.
        polygons (list[np.ndarray]): each polygon is [N, M],
            N is the number of polygons,
            M is the number of points(Be divided by 2).
    """
    masks = []
    for si in range(len(polygons)):
        mask = polygon2mask(img_size, [polygons[si].reshape(-1)], color, downsample_ratio)
        masks.append(mask)
    return np.array(masks)


def polygons2masks_overlap(img_size, segments, downsample_ratio=1):
    """Return a (640, 640) overlap mask."""
    masks = np.zeros((img_size[0] // downsample_ratio, img_size[1] // downsample_ratio),
                     dtype=np.int32 if len(segments) > 255 else np.uint8)
    areas = []
    ms = []
    for si in range(len(segments)):
        mask = polygon2mask(
            img_size,
            [segments[si].reshape(-1)],
            downsample_ratio=downsample_ratio,
            color=1,
        )
        ms.append(mask)
        areas.append(mask.sum())
    areas = np.asarray(areas)
    index = np.argsort(-areas)
    ms = np.array(ms)[index]
    for i in range(len(segments)):
        mask = ms[i] * (i + 1)
        masks = masks + mask
        masks = np.clip(masks, a_min=0, a_max=i + 1)
    return masks, index