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_keras_pretrained(self):
# Load efficientdet pretrained on VOC2007
model = EfficientDet(D=0,
num_classes=20,
weights='D0-VOC',
score_threshold=.4)
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.savefig('test.png')
plt.show(block=True)
def load_image(im_path: str,
im_size: Tuple[int, int],
normalize_image: bool = False) -> tf.Tensor:
# Reads the image and resizes it
im = tf.io.read_file(im_path)
im = tf.image.decode_jpeg(im, channels=3)
im = tf.image.convert_image_dtype(im, tf.float32)
if normalize_image:
im = preprocess.normalize_image(im)
return tf.image.resize(im, im_size)
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)
# In[ ]:
predictions = model(n_image, training=False)
boxes, labels, scores = predictions
labels = [IDX_2_LABEL[o] for o in labels[0]]
# In[7]:
colors = visualizer.colors_per_labels(labels)