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ultralytics 8.0.65 YOLOv8 Pose models (#1347)

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Ayush Chaurasia 2023-04-06 03:55:32 +05:30 • committed by GitHub
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commit 1cb92d7f42
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57 changed files with 1578 additions and 489 deletions
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141
ultralytics/yolo/data/utils.py
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@ -6,10 +6,10 @@ import json
import os
import subprocess
import time
import zipfile
from multiprocessing.pool import ThreadPool
from pathlib import Path
from tarfile import is_tarfile
from zipfile import is_zipfile
import cv2
import numpy as np
@ -61,7 +61,7 @@ def exif_size(img):
def verify_image_label(args):
# Verify one image-label pair
im_file, lb_file, prefix, keypoint, num_cls = args
im_file, lb_file, prefix, keypoint, num_cls, nkpt, ndim = args
# number (missing, found, empty, corrupt), message, segments, keypoints
nm, nf, ne, nc, msg, segments, keypoints = 0, 0, 0, 0, '', [], None
try:
@ -92,25 +92,19 @@ def verify_image_label(args):
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 occlusion param from GT
kpts[i] = np.hstack((lb[i, :5], kpt))
lb = kpts
assert lb.shape[1] == 39, 'labels require 39 columns each after removing occlusion parameter'
assert lb.shape[1] == (5 + nkpt * ndim), f'labels require {(5 + nkpt * ndim)} columns each'
assert (lb[:, 5::ndim] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
assert (lb[:, 6::ndim] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
else:
assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected'
assert (lb[:, 1:] <= 1).all(), \
f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}'
assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'
# All labels
max_cls = int(lb[:, 0].max()) # max label count
assert max_cls <= num_cls, \
f'Label class {max_cls} exceeds dataset class count {num_cls}. ' \
f'Possible class labels are 0-{num_cls - 1}'
assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'
_, i = np.unique(lb, axis=0, return_index=True)
if len(i) < nl: # duplicate row check
lb = lb[i] # remove duplicates
@ -119,12 +113,18 @@ def verify_image_label(args):
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)
lb = np.zeros((0, (5 + nkpt * ndim)), 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)
lb = np.zeros((0, (5 + nkpt * ndim)), dtype=np.float32) if keypoint else np.zeros((0, 5), dtype=np.float32)
if keypoint:
keypoints = lb[:, 5:].reshape(-1, 17, 2)
keypoints = lb[:, 5:].reshape(-1, nkpt, ndim)
if ndim == 2:
kpt_mask = np.ones(keypoints.shape[:2], dtype=np.float32)
kpt_mask = np.where(keypoints[..., 0] < 0, 0.0, kpt_mask)
kpt_mask = np.where(keypoints[..., 1] < 0, 0.0, kpt_mask)
keypoints = np.concatenate([keypoints, kpt_mask[..., None]], axis=-1) # (nl, nkpt, 3)
lb = lb[:, :5]
return im_file, lb, shape, segments, keypoints, nm, nf, ne, nc, msg
except Exception as e:
@ -195,7 +195,7 @@ def check_det_dataset(dataset, autodownload=True):
# Download (optional)
extract_dir = ''
if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
if isinstance(data, (str, Path)) and (zipfile.is_zipfile(data) or is_tarfile(data)):
new_dir = safe_download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False)
data = next((DATASETS_DIR / new_dir).rglob('*.yaml'))
extract_dir, autodownload = data.parent, False
@ -356,23 +356,8 @@ class HUBDatasetStats():
assert dir.is_dir(), f'Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/'
return True, str(dir), self._find_yaml(dir) # zipped, data_dir, yaml_path
def _hub_ops(self, f, max_dim=1920):
# HUB ops for 1 image 'f': resize and save at reduced quality in /dataset-hub for web/app viewing
f_new = self.im_dir / Path(f).name # dataset-hub image filename
try: # use PIL
im = Image.open(f)
r = max_dim / max(im.height, im.width) # ratio
if r < 1.0: # image too large
im = im.resize((int(im.width * r), int(im.height * r)))
im.save(f_new, 'JPEG', quality=50, optimize=True) # save
except Exception as e: # use OpenCV
LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}')
im = cv2.imread(f)
im_height, im_width = im.shape[:2]
r = max_dim / max(im_height, im_width) # ratio
if r < 1.0: # image too large
im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA)
cv2.imwrite(str(f_new), im)
def _hub_ops(self, f):
compress_one_image(f, self.im_dir / Path(f).name) # save to dataset-hub
def get_json(self, save=False, verbose=False):
# Return dataset JSON for Ultralytics HUB
@ -426,3 +411,93 @@ class HUBDatasetStats():
pass
LOGGER.info(f'Done. All images saved to {self.im_dir}')
return self.im_dir
def compress_one_image(f, f_new=None, max_dim=1920, quality=50):
"""
Compresses a single image file to reduced size while preserving its aspect ratio and quality using either the
Python Imaging Library (PIL) or OpenCV library. If the input image is smaller than the maximum dimension, it will
not be resized.
Args:
f (str): The path to the input image file.
f_new (str, optional): The path to the output image file. If not specified, the input file will be overwritten.
max_dim (int, optional): The maximum dimension (width or height) of the output image. Default is 1920 pixels.
quality (int, optional): The image compression quality as a percentage. Default is 50%.
Returns:
None
Usage:
from pathlib import Path
from ultralytics.yolo.data.utils import compress_one_image
for f in Path('/Users/glennjocher/Downloads/dataset').rglob('*.jpg'):
compress_one_image(f)
"""
try: # use PIL
im = Image.open(f)
r = max_dim / max(im.height, im.width) # ratio
if r < 1.0: # image too large
im = im.resize((int(im.width * r), int(im.height * r)))
im.save(f_new or f, 'JPEG', quality=quality, optimize=True) # save
except Exception as e: # use OpenCV
LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}')
im = cv2.imread(f)
im_height, im_width = im.shape[:2]
r = max_dim / max(im_height, im_width) # ratio
if r < 1.0: # image too large
im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA)
cv2.imwrite(str(f_new or f), im)
def delete_dsstore(path):
"""
Deletes all ".DS_store" files under a specified directory.
Args:
path (str, optional): The directory path where the ".DS_store" files should be deleted.
Returns:
None
Usage:
from ultralytics.yolo.data.utils import delete_dsstore
delete_dsstore('/Users/glennjocher/Downloads/dataset')
Note:
".DS_store" files are created by the Apple operating system and contain metadata about folders and files. They
are hidden system files and can cause issues when transferring files between different operating systems.
"""
# Delete Apple .DS_store files
files = list(Path(path).rglob('.DS_store'))
LOGGER.info(f'Deleting *.DS_store files: {files}')
for f in files:
f.unlink()
def zip_directory(dir, use_zipfile_library=True):
"""Zips a directory and saves the archive to the specified output path.
Args:
dir (str): The path to the directory to be zipped.
use_zipfile_library (bool): Whether to use zipfile library or shutil for zipping.
Returns:
None
Usage:
from ultralytics.yolo.data.utils import zip_directory
zip_directory('/Users/glennjocher/Downloads/playground')
zip -r coco8-pose.zip coco8-pose
"""
delete_dsstore(dir)
if use_zipfile_library:
dir = Path(dir)
with zipfile.ZipFile(dir.with_suffix('.zip'), 'w', zipfile.ZIP_DEFLATED) as zip_file:
for file_path in dir.glob('**/*'):
if file_path.is_file():
zip_file.write(file_path, file_path.relative_to(dir))
else:
import shutil
shutil.make_archive(dir, 'zip', dir)