Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-05-05 13:21:37 +02:00
parent dd2d713484
commit 7d857cda95
1 changed files with 7 additions and 7 deletions
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14
utils/datasets.py
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@ -130,7 +130,7 @@ class LoadWebcam: # for inference
class LoadImagesAndLabels(Dataset): # for training/testing class LoadImagesAndLabels(Dataset): # for training/testing
def __init__(self, path, img_size=416, batch_size=16, augment=False): def __init__(self, path, img_size=416, batch_size=16, augment=False, rect=True):
with open(path, 'r') as f: with open(path, 'r') as f:
img_files = f.read().splitlines() img_files = f.read().splitlines()
self.img_files = list(filter(lambda x: len(x) > 0, img_files)) self.img_files = list(filter(lambda x: len(x) > 0, img_files))
@ -144,8 +144,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
for x in self.img_files] for x in self.img_files]
# Rectangular Training https://github.com/ultralytics/yolov3/issues/232 # Rectangular Training https://github.com/ultralytics/yolov3/issues/232
self.train_rectangular = False self.pad_rectangular = rect
if self.train_rectangular: if self.pad_rectangular:
bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
nb = bi[-1] + 1 # number of batches nb = bi[-1] + 1 # number of batches
from PIL import Image from PIL import Image
@ -185,8 +185,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
try: try:
with open(file, 'r') as f: with open(file, 'r') as f:
self.labels[i] = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32) self.labels[i] = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
except: # missing label file except:
pass pass # missing label file
def __len__(self): def __len__(self):
return len(self.img_files) return len(self.img_files)
@ -195,7 +195,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
img_path = self.img_files[index] img_path = self.img_files[index]
label_path = self.label_files[index] label_path = self.label_files[index]
# if hasattr(self, 'imgs'): # if hasattr(self, 'imgs'): # preloaded
# img = self.imgs[index] # BGR # img = self.imgs[index] # BGR
img = cv2.imread(img_path) # BGR img = cv2.imread(img_path) # BGR
assert img is not None, 'File Not Found ' + img_path assert img is not None, 'File Not Found ' + img_path
@ -219,7 +219,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
# Letterbox # Letterbox
h, w, _ = img.shape h, w, _ = img.shape
if self.train_rectangular: if self.pad_rectangular:
new_shape = self.batch_shapes[self.batch[index]] new_shape = self.batch_shapes[self.batch[index]]
img, ratio, padw, padh = letterbox(img, new_shape=new_shape, mode='rect') img, ratio, padw, padh = letterbox(img, new_shape=new_shape, mode='rect')
else: else: