def test_pretrained(self):
# Load efficientdet pretrained on VOC2007
model = EfficientDet.from_pretrained('D0-VOC', score_threshold=.6)
print('Done loading...')
image = io.load_image('test/data/VOC2007/JPEGImages/000002.jpg',
(model.config.input_size, ) * 2)
n_image = normalize_image(image)
n_image = tf.expand_dims(n_image, axis=0)
classes = voc.IDX_2_LABEL
boxes, labels, scores = model(n_image, training=False)
labels = [classes[l] for l in labels[0]]
im = image.numpy()
for l, box, s in zip(labels, boxes[0].numpy(), scores[0]):
x1, y1, x2, y2 = box.astype('int32')
cv2.rectangle(im, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(im, l + ' {:.2f}'.format(s), (x1, y1 - 10),
cv2.FONT_HERSHEY_PLAIN, 2, (0, 255, 0), 2)
plt.imshow(im)
plt.axis('off')
plt.savefig('test.png')
plt.show(block=True)
def test_pretrained(self):
# Load efficientdet pretrained on VOC2007
model = EfficientDet.from_pretrained('D0-VOC', score_threshold=.3)
print('Done loading...')
image = io.load_image('imgs/cat-dog.jpg', model.config.input_size)
n_image = normalize_image(image)
n_image = tf.expand_dims(n_image, axis=0)
classes = voc.IDX_2_LABEL
boxes, labels, scores = model(n_image, training=False)
labels = [classes[l] for l in labels[0]]
colors = visualizer.colors_per_labels(labels)
im = visualizer.draw_boxes(image,
boxes[0],
labels,
scores[0],
colors=colors)
plt.imshow(im)
plt.axis('off')
plt.show(block=True)
from efficientdet import EfficientDet
from efficientdet.data import preprocess
from efficientdet.utils.io import load_image
from efficientdet.data.voc import IDX_2_LABEL
# import efficientdet
print(hub.__version__)
print(tf.__version__)
model_url = "https://tfhub.dev/tensorflow/efficientdet/d3/1"
# base_model = hub.KerasLayer(model_url, input_shape=(299, 299, 3))
# base_model = hub.load(model_url)
# print(base_model.summary())
"""my_model = tf.keras.applications.VGG16()
print(my_model.summary())
"""
"""hub_layer = hub.KerasLayer(model_url, output_shape=[20], input_shape=[], dtype=tf.string, trainable=True)
model = tf.keras.Sequential()
model.add(hub_layer)
model.add(tf.keras.layers.Dense(16, activation="relu", input_shape=[20]))
model.add(tf.keras.layers.Dense(1, activation="sigmoid"))
print(model.summary())"""
"""bla = tarfile.open("efficientdet-d0.tar.gz")
bla.extractall()
print(bla)
print("done")"""
model = EfficientDet.from_pretrained("D0-VOC", score_threshold=0.3)
image_size = model.config.input_size
print(image_size)
# In[ ]:
from efficientdet import visualizer
from efficientdet import EfficientDet
from efficientdet.data import preprocess
from efficientdet.utils.io import load_image
from efficientdet.data.voc import IDX_2_LABEL
import tensorflow as tf
# In[3]:
model = EfficientDet.from_pretrained('D0-VOC', score_threshold=.3)
image_size = model.config.input_size
# In[4]:
image = load_image('sample.jpg', image_size)
image.shape
# In[ ]:
n_image = preprocess.normalize_image(image)
n_image = tf.expand_dims(n_image, 0)