From c171a6ece870be48b07d5de93ee6301b1da0c7de Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Sat, 15 Sep 2018 01:29:29 +0000
Subject: [PATCH] Trying out 10 card model setup
---
transform_data.py | 45 +++++++++++++++++++++++++++++++--------------
1 files changed, 31 insertions(+), 14 deletions(-)
diff --git a/transform_data.py b/transform_data.py
index a05bc89..dcfaed9 100644
--- a/transform_data.py
+++ b/transform_data.py
@@ -26,10 +26,10 @@
:param h_img: height of the entire image
:return: <x> <y> <width> <height>
"""
- x1 = min([pt[0] for pt in key_pts])
- x2 = max([pt[0] for pt in key_pts])
- y1 = min([pt[1] for pt in key_pts])
- y2 = max([pt[1] for pt in key_pts])
+ x1 = max(0, min([pt[0] for pt in key_pts]))
+ x2 = min(w_img, max([pt[0] for pt in key_pts]))
+ y1 = max(0, min([pt[1] for pt in key_pts]))
+ y2 = min(h_img, max([pt[1] for pt in key_pts]))
x = (x2 + x1) / 2 / w_img
y = (y2 + y1) / 2 / h_img
width = (x2 - x1) / w_img
@@ -41,7 +41,7 @@
"""
A template for generating a training image.
"""
- def __init__(self, img_bg, width, height, skew=None, cards=None):
+ def __init__(self, img_bg, class_ids, width, height, skew=None, cards=None):
"""
:param img_bg: background (textile) image
:param width: width of the training image
@@ -50,6 +50,7 @@
:param cards: list of Card objects
"""
self.img_bg = img_bg
+ self.class_ids = class_ids
self.img_result = None
self.width = width
self.height = height
@@ -107,6 +108,12 @@
# Scale & rotate card image
img_card = cv2.resize(card.img, (int(len(card.img[0]) * card.scale), int(len(card.img) * card.scale)))
+ if aug is not None:
+ seq = iaa.Sequential([
+ iaa.SimplexNoiseAlpha(first=iaa.Add(random.randrange(128)), size_px_max=[1, 3],
+ upscale_method="cubic"), # Lighting
+ ])
+ img_card = seq.augment_image(img_card)
mask_scale = cv2.resize(card_mask, (int(len(card_mask[0]) * card.scale), int(len(card_mask) * card.scale)))
img_mask = cv2.bitwise_and(img_card, mask_scale)
img_rotate = imutils.rotate_bound(img_mask, card.theta / math.pi * 180)
@@ -340,7 +347,8 @@
coords_in_gen = [card.coordinate_in_generator(key_pt[0], key_pt[1]) for key_pt in ext_obj.key_pts]
obj_yolo_info = key_pts_to_yolo(coords_in_gen, self.width, self.height)
if ext_obj.label == 'card':
- out_txt.write('0 %.6f %.6f %.6f %.6f\n' % obj_yolo_info)
+ class_id = self.class_ids[card.info['name']]
+ out_txt.write(str(class_id) + ' %.6f %.6f %.6f %.6f\n' % obj_yolo_info)
pass
elif ext_obj.label[:ext_obj.label.find[':']] == 'mana_symbol':
# TODO
@@ -491,10 +499,17 @@
#bg_images = [cv2.imread('data/frilly_0007.jpg')]
background = generate_data.Backgrounds(images=bg_images)
- card_pool = pd.DataFrame()
- for set_name in fetch_data.all_set_list:
- df = fetch_data.load_all_cards_text('%s/csv/%s.csv' % (data_dir, set_name))
- card_pool = card_pool.append(df)
+ #card_pool = pd.DataFrame()
+ #for set_name in fetch_data.all_set_list:
+ # df = fetch_data.load_all_cards_text('%s/csv/%s.csv' % (data_dir, set_name))
+ # card_pool = card_pool.append(df)
+ card_pool = fetch_data.load_all_cards_text('%s/csv/custom.csv' % data_dir)
+ class_ids = {}
+ with open('%s/obj.names' % data_dir) as names_file:
+ class_name_list = names_file.read().splitlines()
+ for i in range(len(class_name_list)):
+ class_ids[class_name_list[i]] = i
+ print(class_ids)
num_gen = 60000
num_iter = 1
@@ -504,7 +519,7 @@
# Since the training image are generated with random rotation, don't need to skew all four sides
skew = [[random.uniform(0, 0.25), 0], [0, 1], [1, 1],
[random.uniform(0.75, 1), 0]]
- generator = ImageGenerator(background.get_random(), 1440, 960, skew=skew)
+ generator = ImageGenerator(background.get_random(), class_ids, 1440, 960, skew=skew)
out_name = ''
for _, card_info in card_pool.sample(random.randint(2, 5)).iterrows():
img_name = '%s/card_img/png/%s/%s_%s.png' % (data_dir, card_info['set'], card_info['collector_number'],
@@ -527,18 +542,20 @@
iaa.AdditiveGaussianNoise(scale=random.uniform(0, 0.05) * 255, per_channel=0.1), # Noises
iaa.Dropout(p=[0, 0.05], per_channel=0.1)
])
+
if i % 3 == 0:
generator.generate_non_obstructive()
- generator.export_training_data(visibility=0.0, out_name='%s/train/non_obstructive_skew/%s_%d'
+ generator.export_training_data(visibility=0.0, out_name='%s/train/non_obstructive_custom/%s_%d'
% (data_dir, out_name, j), aug=seq)
elif i % 3 == 1:
generator.generate_horizontal_span(theta=random.uniform(-math.pi, math.pi))
- generator.export_training_data(visibility=0.0, out_name='%s/train/horizontal_span_skew/%s_%d'
+ generator.export_training_data(visibility=0.0, out_name='%s/train/horizontal_span_custom/%s_%d'
% (data_dir, out_name, j), aug=seq)
else:
generator.generate_vertical_span(theta=random.uniform(-math.pi, math.pi))
- generator.export_training_data(visibility=0.0, out_name='%s/train/vertical_span_skew/%s_%d'
+ generator.export_training_data(visibility=0.0, out_name='%s/train/vertical_span_custom/%s_%d'
% (data_dir, out_name, j), aug=seq)
+
#generator.generate_horizontal_span(theta=random.uniform(-math.pi, math.pi))
#generator.render(display=True, aug=seq, debug=True)
print('Generated %s%d' % (out_name, j))
--
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