mirrors/yolov5
1
0
Fork 0
mirror of https://github.com/ultralytics/yolov5.git synced 2025-06-03 14:49:29 +08:00
Code Issues Projects Releases Wiki Activity

changed prints to logging in utils/datasets (#1315)

Browse Source 
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
igornishka 2020-11-24 16:03:19 +01:00 committed by GitHub
parent 7aeef2dca5
commit 44f42b1589
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 14 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

26
utils/datasets.py
View File

@ -1,6 +1,7 @@
# Dataset utils and dataloaders
import glob
import logging
import math
import os
import random
@ -21,6 +22,8 @@ from tqdm import tqdm
from utils.general import xyxy2xywh, xywh2xyxy
from utils.torch_utils import torch_distributed_zero_first
logger = logging.getLogger(__name__)
# Parameters
help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng'] # acceptable image suffixes
@ -165,14 +168,14 @@ class LoadImages: # for inference
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='')
logger.debug('video %g/%g (%g/%g) %s: ', self.count + 1, self.nf, self.frame, self.nframes, path)
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='')
logger.debug('image %g/%g %s: ', self.count, self.nf, path)
# Padded resize
img = letterbox(img0, new_shape=self.img_size)[0]
@ -234,7 +237,7 @@ class LoadWebcam: # for inference
# Print
assert ret_val, 'Camera Error %s' % self.pipe
img_path = 'webcam.jpg'
print('webcam %g: ' % self.count, end='')
logger.debug('webcam %g: ', self.count)
# Padded resize
img = letterbox(img0, new_shape=self.img_size)[0]
@ -265,7 +268,7 @@ class LoadStreams: # multiple IP or RTSP cameras
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='')
logger.debug('%g/%g: %s... ', i + 1, n, s)
cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s)
assert cap.isOpened(), 'Failed to open %s' % s
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
@ -273,15 +276,14 @@ class LoadStreams: # multiple IP or RTSP cameras
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))
logger.debug(' 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.')
logger.warning('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
@ -418,7 +420,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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
nd += 1 # logger.warning('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
@ -455,7 +457,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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
ne += 1 # logger.info('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
if rank in [-1, 0]:
@ -463,7 +465,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
cache_path, nf, nm, ne, nd, n)
if nf == 0:
s = 'WARNING: No labels found in %s. See %s' % (os.path.dirname(file) + os.sep, help_url)
print(s)
logger.info(s)
assert not augment, '%s. Can not train without labels.' % s
# Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
@ -496,7 +498,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
l = np.zeros((0, 5), dtype=np.float32)
x[img] = [l, shape]
except Exception as e:
print('WARNING: Ignoring corrupted image and/or label %s: %s' % (img, e))
logger.warning('WARNING: Ignoring corrupted image and/or label %s: %s', img, e)
x['hash'] = get_hash(self.label_files + self.img_files)
torch.save(x, path) # save for next time
@ -507,7 +509,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
# def __iter__(self):
# self.count = -1
# print('ran dataset iter')
# logger.info('ran dataset iter')
# #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
# return self