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)