Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-06-18 21:34:44 +02:00
parent c3526e0eff
commit a40f421061
2 changed files with 22 additions and 4 deletions
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24
utils/datasets.py
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@ -11,7 +11,7 @@ import torch
from torch.utils.data import Dataset from torch.utils.data import Dataset
from tqdm import tqdm from tqdm import tqdm
from utils.utils import xyxy2xywh from utils.utils import xyxy2xywh, xywh2xyxy
class LoadImages: # for inference class LoadImages: # for inference
@ -188,7 +188,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
# Preload labels (required for weighted CE training) # Preload labels (required for weighted CE training)
self.imgs = [None] * n self.imgs = [None] * n
self.labels = [np.zeros((0, 5))] * n self.labels = [np.zeros((0, 5))] * n
iter = tqdm(self.label_files, desc='Reading labels') if n > 1000 else self.label_files iter = tqdm(self.label_files, desc='Reading labels') if n > 10 else self.label_files
extract_bounding_boxes = False
for i, file in enumerate(iter): for i, file in enumerate(iter):
try: try:
with open(file, 'r') as f: with open(file, 'r') as f:
@ -198,6 +199,23 @@ class LoadImagesAndLabels(Dataset): # for training/testing
assert (l >= 0).all(), 'negative labels: %s' % file assert (l >= 0).all(), 'negative labels: %s' % file
assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file
self.labels[i] = l self.labels[i] = l
# Extract object detection boxes for a second stage classifier
if extract_bounding_boxes:
p = Path(self.img_files[i])
img = cv2.imread(str(p))
h, w, _ = img.shape
for j, x in enumerate(l):
f = '%s%sclassification%s%g_%g_%s' % (p.parent.parent, os.sep, os.sep, x[0], j, p.name)
if not os.path.exists(Path(f).parent):
os.makedirs(Path(f).parent) # make new output folder
box = xywh2xyxy(x[1:].reshape(-1, 4)).ravel()
box = np.clip(box, 0, 1) # clip boxes outside of image
result = cv2.imwrite(f, img[int(box[1] * h):int(box[3] * h),
int(box[0] * w):int(box[2] * w)])
if not result:
print('stop')
except: except:
pass # print('Warning: missing labels for %s' % self.img_files[i]) # missing label file pass # print('Warning: missing labels for %s' % self.img_files[i]) # missing label file
assert len(np.concatenate(self.labels, 0)) > 0, 'No labels found. Incorrect label paths provided.' assert len(np.concatenate(self.labels, 0)) > 0, 'No labels found. Incorrect label paths provided.'
@ -372,7 +390,7 @@ def random_affine(img, targets=(), degrees=(-10, 10), translate=(.1, .1), scale=
M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!! M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
imw = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_LINEAR, imw = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_LINEAR,
borderValue=borderValue) # BGR order borderValue borderValue=borderValue) # BGR order borderValue
# Return warped points also # Return warped points also
if len(targets) > 0: if len(targets) > 0:

2
utils/gcp.sh
View File

@ -95,6 +95,6 @@ python3 test.py --data ../supermarket2/supermarket2.data --weights ../darknet/ba
# Debug/Development # Debug/Development
python3 train.py --data data/coco.data --epochs 2 --img-size 320 python3 train.py --data data/coco.data --epochs 1 --img-size 320 --single-scale --batch-size 16 --accumulate 4 --giou --evolve
gsutil cp evolve.txt gs://ultralytics gsutil cp evolve.txt gs://ultralytics
sudo shutdown sudo shutdown