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275 lines
12 KiB
275 lines
12 KiB
import contextlib
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import hashlib
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import os
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import subprocess
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import time
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from pathlib import Path
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from tarfile import is_tarfile
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from zipfile import is_zipfile
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import cv2
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import numpy as np
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import torch
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from PIL import ExifTags, Image, ImageOps
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from ultralytics.yolo.utils import LOGGER, ROOT, colorstr
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from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii
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from ultralytics.yolo.utils.downloads import download
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from ultralytics.yolo.utils.files import unzip_file, yaml_load
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from ..utils.ops import segments2boxes
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HELP_URL = "See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data"
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IMG_FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm" # include image suffixes
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VID_FORMATS = "asf", "avi", "gif", "m4v", "mkv", "mov", "mp4", "mpeg", "mpg", "ts", "wmv" # include video suffixes
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LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html
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RANK = int(os.getenv('RANK', -1))
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PIN_MEMORY = str(os.getenv("PIN_MEMORY", True)).lower() == "true" # global pin_memory for dataloaders
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IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean
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IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation
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# Get orientation exif tag
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for orientation in ExifTags.TAGS.keys():
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if ExifTags.TAGS[orientation] == "Orientation":
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break
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def img2label_paths(img_paths):
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# Define label paths as a function of image paths
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sa, sb = f"{os.sep}images{os.sep}", f"{os.sep}labels{os.sep}" # /images/, /labels/ substrings
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return [sb.join(x.rsplit(sa, 1)).rsplit(".", 1)[0] + ".txt" for x in img_paths]
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def get_hash(paths):
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# Returns a single hash value of a list of paths (files or dirs)
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size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes
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h = hashlib.md5(str(size).encode()) # hash sizes
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h.update("".join(paths).encode()) # hash paths
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return h.hexdigest() # return hash
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def exif_size(img):
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# Returns exif-corrected PIL size
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s = img.size # (width, height)
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with contextlib.suppress(Exception):
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rotation = dict(img._getexif().items())[orientation]
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if rotation in [6, 8]: # rotation 270 or 90
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s = (s[1], s[0])
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return s
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def verify_image_label(args):
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# Verify one image-label pair
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im_file, lb_file, prefix, keypoint = args
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# number (missing, found, empty, corrupt), message, segments, keypoints
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nm, nf, ne, nc, msg, segments, keypoints = 0, 0, 0, 0, "", [], None
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try:
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# verify images
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im = Image.open(im_file)
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im.verify() # PIL verify
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shape = exif_size(im) # image size
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shape = (shape[1], shape[0]) # hw
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assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels"
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assert im.format.lower() in IMG_FORMATS, f"invalid image format {im.format}"
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if im.format.lower() in ("jpg", "jpeg"):
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with open(im_file, "rb") as f:
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f.seek(-2, 2)
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if f.read() != b"\xff\xd9": # corrupt JPEG
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ImageOps.exif_transpose(Image.open(im_file)).save(im_file, "JPEG", subsampling=0, quality=100)
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msg = f"{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved"
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# verify labels
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if os.path.isfile(lb_file):
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nf = 1 # label found
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with open(lb_file) as f:
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lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
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if any(len(x) > 6 for x in lb) and (not keypoint): # is segment
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classes = np.array([x[0] for x in lb], dtype=np.float32)
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segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...)
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lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)
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lb = np.array(lb, dtype=np.float32)
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nl = len(lb)
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if nl:
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if keypoint:
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assert lb.shape[1] == 56, "labels require 56 columns each"
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assert (lb[:, 5::3] <= 1).all(), "non-normalized or out of bounds coordinate labels"
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assert (lb[:, 6::3] <= 1).all(), "non-normalized or out of bounds coordinate labels"
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kpts = np.zeros((lb.shape[0], 39))
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for i in range(len(lb)):
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kpt = np.delete(lb[i, 5:], np.arange(2, lb.shape[1] - 5,
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3)) # remove the occlusion parameter from the GT
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kpts[i] = np.hstack((lb[i, :5], kpt))
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lb = kpts
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assert lb.shape[1] == 39, "labels require 39 columns each after removing occlusion parameter"
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else:
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assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected"
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assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}"
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assert (lb[:, 1:] <=
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1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"
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_, i = np.unique(lb, axis=0, return_index=True)
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if len(i) < nl: # duplicate row check
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lb = lb[i] # remove duplicates
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if segments:
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segments = [segments[x] for x in i]
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msg = f"{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed"
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else:
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ne = 1 # label empty
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lb = np.zeros((0, 39), dtype=np.float32) if keypoint else np.zeros((0, 5), dtype=np.float32)
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else:
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nm = 1 # label missing
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lb = np.zeros((0, 39), dtype=np.float32) if keypoint else np.zeros((0, 5), dtype=np.float32)
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if keypoint:
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keypoints = lb[:, 5:].reshape(-1, 17, 2)
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lb = lb[:, :5]
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return im_file, lb, shape, segments, keypoints, nm, nf, ne, nc, msg
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except Exception as e:
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nc = 1
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msg = f"{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}"
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return [None, None, None, None, None, nm, nf, ne, nc, msg]
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def polygon2mask(imgsz, polygons, color=1, downsample_ratio=1):
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"""
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Args:
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imgsz (tuple): The image size.
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polygons (np.ndarray): [N, M], N is the number of polygons,
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M is the number of points(Be divided by 2).
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"""
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mask = np.zeros(imgsz, dtype=np.uint8)
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polygons = np.asarray(polygons)
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polygons = polygons.astype(np.int32)
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shape = polygons.shape
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polygons = polygons.reshape(shape[0], -1, 2)
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cv2.fillPoly(mask, polygons, color=color)
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nh, nw = (imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio)
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# NOTE: fillPoly firstly then resize is trying the keep the same way
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# of loss calculation when mask-ratio=1.
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mask = cv2.resize(mask, (nw, nh))
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return mask
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def polygons2masks(imgsz, polygons, color, downsample_ratio=1):
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"""
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Args:
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imgsz (tuple): The image size.
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polygons (list[np.ndarray]): each polygon is [N, M],
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N is the number of polygons,
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M is the number of points(Be divided by 2).
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"""
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masks = []
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for si in range(len(polygons)):
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mask = polygon2mask(imgsz, [polygons[si].reshape(-1)], color, downsample_ratio)
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masks.append(mask)
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return np.array(masks)
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def polygons2masks_overlap(imgsz, segments, downsample_ratio=1):
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"""Return a (640, 640) overlap mask."""
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masks = np.zeros((imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio),
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dtype=np.int32 if len(segments) > 255 else np.uint8)
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areas = []
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ms = []
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for si in range(len(segments)):
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mask = polygon2mask(
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imgsz,
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[segments[si].reshape(-1)],
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downsample_ratio=downsample_ratio,
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color=1,
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)
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ms.append(mask)
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areas.append(mask.sum())
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areas = np.asarray(areas)
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index = np.argsort(-areas)
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ms = np.array(ms)[index]
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for i in range(len(segments)):
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mask = ms[i] * (i + 1)
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masks = masks + mask
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masks = np.clip(masks, a_min=0, a_max=i + 1)
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return masks, index
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def check_dataset_yaml(data, autodownload=True):
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# Download, check and/or unzip dataset if not found locally
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data = check_file(data)
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DATASETS_DIR = Path.cwd() / "../datasets"
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# Download (optional)
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extract_dir = ''
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if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
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download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1)
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data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))
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extract_dir, autodownload = data.parent, False
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# Read yaml (optional)
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if isinstance(data, (str, Path)):
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data = yaml_load(data) # dictionary
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# Checks
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for k in 'train', 'val', 'names':
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assert k in data, f"data.yaml '{k}:' field missing ❌"
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if isinstance(data['names'], (list, tuple)): # old array format
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data['names'] = dict(enumerate(data['names'])) # convert to dict
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data['nc'] = len(data['names'])
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# Resolve paths
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path = Path(extract_dir or data.get('path') or '') # optional 'path' default to '.'
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if not path.is_absolute():
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path = (Path.cwd() / path).resolve()
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data['path'] = path # download scripts
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for k in 'train', 'val', 'test':
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if data.get(k): # prepend path
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if isinstance(data[k], str):
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x = (path / data[k]).resolve()
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if not x.exists() and data[k].startswith('../'):
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x = (path / data[k][3:]).resolve()
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data[k] = str(x)
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else:
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data[k] = [str((path / x).resolve()) for x in data[k]]
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# Parse yaml
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train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download'))
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if val:
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val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path
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if not all(x.exists() for x in val):
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LOGGER.info('\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()])
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if not s or not autodownload:
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raise Exception('Dataset not found ❌')
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t = time.time()
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if s.startswith('http') and s.endswith('.zip'): # URL
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f = Path(s).name # filename
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LOGGER.info(f'Downloading {s} to {f}...')
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torch.hub.download_url_to_file(s, f)
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Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root
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unzip_file(f, path=DATASETS_DIR) # unzip
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Path(f).unlink() # remove zip
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r = None # success
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elif s.startswith('bash '): # bash script
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LOGGER.info(f'Running {s} ...')
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r = os.system(s)
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else: # python script
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r = exec(s, {'yaml': data}) # return None
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dt = f'({round(time.time() - t, 1)}s)'
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s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f"failure {dt} ❌"
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LOGGER.info(f"Dataset download {s}")
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check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True) # download fonts
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return data # dictionary
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def check_dataset(dataset: str):
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data = Path.cwd() / "datasets" / dataset
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data_dir = data if data.is_dir() else (Path.cwd() / data)
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if not data_dir.is_dir():
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LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
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t = time.time()
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if str(data) == 'imagenet':
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subprocess.run(f"bash {ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
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else:
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url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip'
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download(url, dir=data_dir.parent)
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s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
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LOGGER.info(s)
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train_set = data_dir / "train"
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test_set = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val' # data/test or data/val
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nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()]) # number of classes
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names = [name for name in os.listdir(data_dir / 'train') if os.path.isdir(data_dir / 'train' / name)]
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data = {"train": train_set, "val": test_set, "nc": nc, "names": names}
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return data
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