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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-10-08 14:08:23 +02:00
parent 88ba61505f
commit 69745d8b8e
2 changed files with 28 additions and 20 deletions
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2
train.py
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@ -32,7 +32,7 @@ hyp = {'giou': 1.582, # giou loss gain
'momentum': 0.97, # SGD momentum
'weight_decay': 0.0004569, # optimizer weight decay
'fl_gamma': 0.5, # focal loss gamma
'hsv_h': 0.0, # image HSV-Hue augmentation (fraction)
'hsv_h': 0.10, # image HSV-Hue augmentation (fraction)
'hsv_s': 0.5703, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.3174, # image HSV-Value augmentation (fraction)
'degrees': 1.113, # image rotation (+/- deg)

46
utils/datasets.py
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@ -439,13 +439,9 @@ class LoadImagesAndLabels(Dataset): # for training/testing
labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + padh
if self.augment:
hyp = self.hyp
# Augment colorspace
augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
# Augment imagespace
g = 0.0 if mosaic else 1.0 # do not augment mosaics
hyp = self.hyp
img, labels = random_affine(img, labels,
degrees=hyp['degrees'] * g,
translate=hyp['translate'] * g,
@ -510,27 +506,39 @@ def load_image(self, index):
if self.augment and r < 1: # if training (NOT testing), downsize to inference shape
h, w, _ = img.shape
img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # _LINEAR fastest
# Augment colorspace
if self.augment:
augment_hsv(img, hgain=self.hyp['hsv_h'], sgain=self.hyp['hsv_s'], vgain=self.hyp['hsv_v'])
return img
def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
# SV augmentation by 50%
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # hue, sat, val
S = img_hsv[:, :, 1].astype(np.float32) # saturation
V = img_hsv[:, :, 2].astype(np.float32) # value
a = random.uniform(-1, 1) * sgain + 1
b = random.uniform(-1, 1) * vgain + 1
S *= a
V *= b
img_hsv[:, :, 1] = S if a < 1 else S.clip(None, 255)
img_hsv[:, :, 2] = V if b < 1 else V.clip(None, 255)
x = (np.random.uniform(-1, 1, 3) * np.array([hgain, sgain, vgain]) + 1).astype(np.float32) # random gains
img_hsv = (cv2.cvtColor(img, cv2.COLOR_BGR2HSV) * x.reshape((1, 1, 3))).clip(None, 255).astype(np.uint8)
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed
# def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): # original version
# # SV augmentation by 50%
# img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # hue, sat, val
#
# S = img_hsv[:, :, 1].astype(np.float32) # saturation
# V = img_hsv[:, :, 2].astype(np.float32) # value
#
# a = random.uniform(-1, 1) * sgain + 1
# b = random.uniform(-1, 1) * vgain + 1
# S *= a
# V *= b
#
# img_hsv[:, :, 1] = S if a < 1 else S.clip(None, 255)
# img_hsv[:, :, 2] = V if b < 1 else V.clip(None, 255)
# cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed
def load_mosaic(self, index):
# loads up images in a mosaic
# loads images in a mosaic
labels4 = []
s = self.img_size
@ -542,7 +550,7 @@ def load_mosaic(self, index):
img = load_image(self, index)
h, w, _ = img.shape
# merge img into img4
# place img in img4
if i == 0: # top left
x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)