annotations_dir = os.path.join('competition_data', 'val_annotations')
annotations_files = sorted(os.listdir(annotations_dir))
annotation = []
for file in annotations_files:
with open(os.path.join(annotations_dir,file)) as f:
data = json.load(f)
annotation.append(data)
#SSDインポート
from ssd import SSD
ssd = SSD("ssd_7")
models_dir = os.path.join(".", "trained_models")
model_path = os.path.join(models_dir, "ssd7.h5")
ssd.load_weights(model_path)
def plot_bbox(img, gt, out):
# グラフサイズの指定
plt.figure(figsize=(11,11))
# 衛星データを表示（BGR->RGB）
plt.imshow(img[:,:,::-1])
# 今操作したいaxis（画像）を選択
current_axis = plt.gca()
# 正解となるbboxを可視化（赤色で表示）
for label in gt["labels"]:
# バウンディングボックスの座標から始点と幅、高さを算出し、四角形を生成
xmin = label["box2d"]["x1"]
raise ValueError(
"Wrong or unsupported dataset. Available 'COCO' or 'VOC'")
print("\n\Testing on %s dataset" % (DATASET_NAME + TESTSET_YEAR))
dataset.show_info()
_ = input("Press Enter to continue...")
## 2. Dataloader initialization
print("\t2. Dataloader initialization...")
dataloader = Dataloader(dataset, TEST_SIZE)
test_generator = dataloader.generate_batch("test")
## 3. Network initialization
print("\t3. Network initialization...")
ssd = SSD(num_classes=len(dataset.label_ids) + 1, input_shape=INPUT_SHAPE)
latest = tf.train.latest_checkpoint(CHECKPOINT_DIR)
ssd.load_weights(latest)
ssd.summary()
_ = input("Press Enter to continue...")
## 4. Generate default boxes
print("\t4. Default boxes generation...")
fm_shapes = ssd.output_shape
aspect_ratios = ASPECT_RATIOS
scales = SCALES
default_boxes = Image.generate_default_boxes(fm_shapes, aspect_ratios,
scales)
# ---------------------------------------------------------------- #
print("Initialization completed!")
print("Start Testing...")
