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Random padding, Val/train new split append to previous list

pull/2109/head
hanoch 2024-11-17 15:10:26 +02:00
parent 4afc52b1cd
commit 2de1a2593c
3 changed files with 5659 additions and 6410 deletions
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6406
tir_od/training_set.txt
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5645
tir_od/validation_set.txt
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18
utils/datasets.py
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@ -495,6 +495,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
self.input_channels = input_channels# in case GL image but NN is RGB hence replicate
self.tir_channel_expansion = tir_channel_expansion
self.is_tir_signal = not (no_tir_signal)
self.random_pad = True
#self.albumentations = Albumentations() if augment else None
self.albumentations_gamma_contrast = Albumentations_gamma_contrast(alb_prob=hyp['gamma_liklihood'],
@ -956,7 +957,10 @@ def load_mosaic(self, index, filling_value):
if self.scaling_before_mosaic:
img4 = np.full((s * 2, s * 2, img.shape[2]), 0.5, dtype=np.float32) # base image with 4 tiles
else:
img4 = np.full((s * 2, s * 2, img.shape[2]), img.mean(), dtype=np.uint16) # base image with 4 tiles
if self.random_pad:
img4 = np.random.normal(img.mean(), 500, (s * 2, s * 2, img.shape[2])).astype('uint16')
else:
img4 = np.full((s * 2, s * 2, img.shape[2]), img.mean(), dtype=np.uint16) # base image with 4 tiles
else:
img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
@ -1025,7 +1029,10 @@ def load_mosaic9(self, index, filling_value):
if self.scaling_before_mosaic:
img9 = np.full((s * 3, s * 3, img.shape[2]), 0.5, dtype=np.float32) # base image with 4 tiles
else:
img9 = np.full((s * 3, s * 3, img.shape[2]), img.mean(), dtype=np.uint16) # base image with 4 tiles pad with : uint16 based on the DR of the image which is unknown
if self.random_pad:
img9 = np.random.normal(img.mean(), 500, (s * 3, s * 3, img.shape[2])).astype('uint16')
else:
img9 = np.full((s * 3, s * 3, img.shape[2]), img.mean(), dtype=np.uint16) # base image with 4 tiles pad with : uint16 based on the DR of the image which is unknown
else:
img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
@ -1114,7 +1121,10 @@ def load_samples(self, index):
if self.scaling_before_mosaic:
img4 = np.full((s * 2, s * 2, img.shape[2]), 0.5, dtype=np.float32) # base image with 4 tiles fill with 0.5 in [0 1] equals to 114 in [0 255]
else:
img4 = np.full((s * 2, s * 2, img.shape[2]), img.mean(), dtype=np.uint16) # base image with 4 tiles
if self.random_pad:
img4 = np.random.normal(img.mean(), 500, (s * 2, s * 2, img.shape[2])).astype('uint16')
else:
img4 = np.full((s * 2, s * 2, img.shape[2]), img.mean(), dtype=np.uint16) # base image with 4 tiles
else:
img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles # base image with 4 tiles
@ -1301,7 +1311,7 @@ def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, s
R = np.eye(3)
a = random.uniform(-degrees, degrees)
# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
s = random.uniform(1 - scale, 1.1 + scale)
s = random.uniform(1 - scale, 1.1 + scale) #@@HK TODO why not symetric
# s = 2 ** random.uniform(-scale, scale)
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)