Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2018-09-02 11:38:39 +02:00
parent e99bda0c54
commit 641e354948
4 changed files with 30 additions and 29 deletions
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2
detect.py
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@ -56,7 +56,7 @@ def detect(opt):
# Set Dataloader
classes = load_classes(opt.class_path) # Extracts class labels from file
dataloader = ImageFolder(opt.image_folder, batch_size=opt.batch_size, img_size=opt.img_size)
dataloader = load_images(opt.image_folder, batch_size=opt.batch_size, img_size=opt.img_size)
imgs = [] # Stores image paths
img_detections = [] # Stores detections for each image index

6
test.py
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@ -42,10 +42,10 @@ elif weights_path.endswith('.pt'): # pytorch format
model.to(device).eval()
# Get dataloader
# dataset = ListDataset(test_path)
# Get PyTorch dataloader
# dataset = load_images_with_labels(test_path)
# dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
dataloader = ListDataset(test_path, batch_size=opt.batch_size, img_size=opt.img_size)
dataloader = load_images_and_labels(test_path, batch_size=opt.batch_size, img_size=opt.img_size)
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor

2
train.py
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@ -42,7 +42,7 @@ def main(opt):
model = Darknet(opt.cfg, opt.img_size)
# Get dataloader
dataloader = ListDataset(train_path, batch_size=opt.batch_size, img_size=opt.img_size)
dataloader = load_images_and_labels(train_path, batch_size=opt.batch_size, img_size=opt.img_size, augment=True)
# reload saved optimizer state
start_epoch = 0

49
utils/datasets.py
View File

@ -12,7 +12,7 @@ import torch
from utils.utils import xyxy2xywh
class ImageFolder(): # for eval-only
class load_images(): # for inference
def __init__(self, path, batch_size=1, img_size=416):
if os.path.isdir(path):
self.files = sorted(glob.glob('%s/*.*' % path))
@ -59,8 +59,8 @@ class ImageFolder(): # for eval-only
return self.nB # number of batches
class ListDataset(): # for training
def __init__(self, path, batch_size=1, img_size=608):
class load_images_and_labels(): # for training
def __init__(self, path, batch_size=1, img_size=608, augment=False):
self.path = path
# self.img_files = sorted(glob.glob('%s/*.*' % path))
with open(path, 'r') as file:
@ -79,6 +79,7 @@ class ListDataset(): # for training
self.nB = math.ceil(self.nF / batch_size) # number of batches
self.batch_size = batch_size
self.height = img_size
self.augment = augment
assert self.nB > 0, 'No images found in path %s' % path
@ -113,7 +114,7 @@ class ListDataset(): # for training
continue
augment_hsv = True
if augment_hsv:
if self.augment and augment_hsv:
# SV augmentation by 50%
fraction = 0.50
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
@ -151,8 +152,8 @@ class ListDataset(): # for training
labels = np.array([])
# Augment image and labels
img, labels, M = random_affine(img, targets=labels, degrees=(-5, 5), translate=(0.2, 0.2),
scale=(0.8, 1.2)) # RGB
if self.augment:
img, labels, M = random_affine(img, labels, degrees=(-5, 5), translate=(0.2, 0.2), scale=(0.8, 1.2))
plotFlag = False
if plotFlag:
@ -167,19 +168,20 @@ class ListDataset(): # for training
# convert xyxy to xywh
labels[:, 1:5] = xyxy2xywh(labels[:, 1:5].copy()) / height
# random left-right flip
lr_flip = True
if lr_flip & (random.random() > 0.5):
img = np.fliplr(img)
if nL > 0:
labels[:, 1] = 1 - labels[:, 1]
if self.augment:
# random left-right flip
lr_flip = True
if lr_flip & (random.random() > 0.5):
img = np.fliplr(img)
if nL > 0:
labels[:, 1] = 1 - labels[:, 1]
# random up-down flip
ud_flip = False
if ud_flip & (random.random() > 0.5):
img = np.flipud(img)
if nL > 0:
labels[:, 2] = 1 - labels[:, 2]
# random up-down flip
ud_flip = False
if ud_flip & (random.random() > 0.5):
img = np.flipud(img)
if nL > 0:
labels[:, 2] = 1 - labels[:, 2]
img_all.append(img)
labels_all.append(torch.from_numpy(labels))
@ -199,13 +201,13 @@ class ListDataset(): # for training
def resize_square(img, height=416, color=(0, 0, 0)): # resize a rectangular image to a padded square
shape = img.shape[:2] # shape = [height, width]
ratio = float(height) / max(shape)
ratio = float(height) / max(shape) # ratio = old / new
new_shape = [round(shape[0] * ratio), round(shape[1] * ratio)]
dw = height - new_shape[1] # width padding
dh = height - new_shape[0] # height padding
top, bottom = dh // 2, dh - (dh // 2)
left, right = dw // 2, dw - (dw // 2)
img = cv2.resize(img, (new_shape[1], new_shape[0]), interpolation=cv2.INTER_AREA)
img = cv2.resize(img, (new_shape[1], new_shape[0]), interpolation=cv2.INTER_AREA) # resized, no border
return cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color), ratio, dw // 2, dh // 2
@ -220,8 +222,7 @@ def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scal
# Rotation and Scale
R = np.eye(3)
a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
# a += random.choice([-180, -90, 0, 90]) # random 90deg rotations added to small rotations
# a += random.choice([-180, -90, 0, 90]) # 90deg rotations added to small rotations
s = random.random() * (scale[1] - scale[0]) + scale[0]
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
@ -235,9 +236,9 @@ def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scal
S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # y shear (deg)
M = S @ T @ R # ORDER IS IMPORTANT HERE!!
M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
imw = cv2.warpPerspective(img, M, dsize=(height, height), flags=cv2.INTER_LINEAR,
borderValue=borderValue) # BGR order (YUV-equalized BGR means)
borderValue=borderValue) # BGR order borderValue
# Return warped points also
if targets is not None: