Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-07-04 14:03:13 +02:00
parent eeae43c414
commit 7a353a9c70
1 changed files with 9 additions and 8 deletions
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17
utils/datasets.py
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@ -13,12 +13,13 @@ from tqdm import tqdm
from utils.utils import xyxy2xywh, xywh2xyxy from utils.utils import xyxy2xywh, xywh2xyxy
img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif']
vid_formats = ['.mov', '.avi', '.mp4']
class LoadImages: # for inference class LoadImages: # for inference
def __init__(self, path, img_size=416): def __init__(self, path, img_size=416):
self.height = img_size self.height = img_size
img_formats = ['.jpg', '.jpeg', '.png', '.tif']
vid_formats = ['.mov', '.avi', '.mp4']
files = [] files = []
if os.path.isdir(path): if os.path.isdir(path):
@ -146,11 +147,11 @@ class LoadImagesAndLabels(Dataset): # for training/testing
self.augment = augment self.augment = augment
self.image_weights = image_weights self.image_weights = image_weights
self.rect = False if image_weights else rect self.rect = False if image_weights else rect
self.label_files = [x.replace('images', 'labels').
replace('.jpeg', '.txt'). # Define labels
replace('.jpg', '.txt'). self.label_files = [x.replace('images', 'labels') for x in self.img_files]
replace('.bmp', '.txt'). for f in img_formats:
replace('.png', '.txt') for x in self.img_files] self.label_files = [x.replace(f, '.txt') for x in self.label_files]
# Rectangular Training https://github.com/ultralytics/yolov3/issues/232 # Rectangular Training https://github.com/ultralytics/yolov3/issues/232
if self.rect: if self.rect:
@ -169,9 +170,9 @@ class LoadImagesAndLabels(Dataset): # for training/testing
# Sort by aspect ratio # Sort by aspect ratio
ar = s[:, 1] / s[:, 0] # aspect ratio ar = s[:, 1] / s[:, 0] # aspect ratio
i = ar.argsort() i = ar.argsort()
ar = ar[i]
self.img_files = [self.img_files[i] for i in i] self.img_files = [self.img_files[i] for i in i]
self.label_files = [self.label_files[i] for i in i] self.label_files = [self.label_files[i] for i in i]
ar = ar[i]
# Set training image shapes # Set training image shapes
shapes = [[1, 1]] * nb shapes = [[1, 1]] * nb