def main():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
config = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=config) as sess:
model = SiameseNet()
model.load_weights(
'/home/tracy/PycharmProjects/SiameseNet/checkpoint/with_reference/my_model'
)
img = cv2.imread('/home/tracy/YOLOv3_TensorFlow/temp/2_3.jpg')
img1, img2 = data_loader.dataloader(img)
pred, scores, _ = model.prediction(img1, img2)
pred, scores = sess.run([pred, scores])
print(pred, scores)
pred_labels = np.nonzero(pred)
scores_ = scores[pred_labels]
label_index = np.argmax(scores_)
print('\033[1;32m Label\t\t\tScore\033[0m')
for i in pred_labels[0]:
print('\t', i, '\t\t\t', scores[i], '\n')
class SiamesModel():
def __init__(self, config):
self.graph = tf.Graph()
self.sess = tf.Session(config=config, graph=self.graph)
with self.sess.as_default():
with self.graph.as_default():
self.siamese_model = SiameseNet()
self.siamese_model.load_weights(
'/home/tracy/PycharmProjects/SiameseNet/checkpoint/with_reference/best/my_model'
)
def run(self, img1, img2):
with self.graph.as_default():
return self.siamese_model.prediction(img1, img2)
# Crop the detected traffic signs
if x1 - x0 > 10 and y1 - y0 > 10 and labels_[j] == 0:
img_ori_ = cv2.cvtColor(img_ori,
cv2.COLOR_BGR2RGB).astype(np.float32)
img_cropped = img_ori_[int(y0):int(y1), int(x0):int(x1)]
tf.reset_default_graph()
new_graph = tf.Graph()
with new_graph.as_default():
with tf.Session(graph=new_graph) as new_sess:
siamese_model = SiameseNet()
siamese_model.load_weights(
'/home/tracy/PycharmProjects/SiameseNet/checkpoint/with_reference/my_model'
)
img1, img2 = dataloader(img_cropped)
label_pred, label_score, _ = siamese_model.prediction(
img1, img2)
label_pred_, label_score_ = new_sess.run(
[label_pred, label_score])
# with sess_yolo.as_default():
# with sess_yolo.graph.as_default():
# img1, img2 = dataloader(img_cropped)
# # input1_ = sess_yolo.run(img1)
# # input2_ = sess_yolo.run(img2)
# label_pred_, label_score_ = yolo_model.siamese_forward(img1, img2)
# print(label_pred_, label_score_)
# with sess_siam.as_default():
# with sess_siam.graph.as_default():
# img1, img2 = dataloader(img_cropped)
# label_pred, label_score = siamese_model.prediction(img1, img2)
def test_display_one_img(img_path):
img_ori = cv2.imread(img_path)
if args.letterbox_resize:
img, resize_ratio, dw, dh = letterbox_resize(img_ori, args.new_size[0],
args.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(args.new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
with sess_yolo.as_default():
with graph_yolo.as_default():
boxes_, scores_, labels_ = sess_yolo.run(
[boxes, scores, labels], feed_dict={input_data: img})
# rescale the coordinates to the original image
if args.letterbox_resize:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori / float(args.new_size[0]))
boxes_[:, [1, 3]] *= (height_ori / float(args.new_size[1]))
for j in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[j]
x0 = np.maximum(x0, 0)
y0 = np.maximum(y0, 0)
x1 = np.maximum(x1, 0)
y1 = np.maximum(y1, 0)
label_index = labels_[j]
# Crop the detected traffic signs
if x1 - x0 > 10 and y1 - y0 > 10 and labels_[j] == 0:
img_ori_ = cv2.cvtColor(img_ori,
cv2.COLOR_BGR2RGB).astype(np.float32)
img_cropped = img_ori_[int(y0):int(y1), int(x0):int(x1)]
if img_cropped.any():
tf.reset_default_graph()
new_graph = tf.Graph()
with new_graph.as_default():
with tf.Session(graph=new_graph) as new_sess:
siamese_model = SiameseNet()
siamese_model.load_weights(
'/home/tracy/PycharmProjects/SiameseNet/checkpoint/RGBscaled/best/my_model'
)
img1, img2 = dataloader(img_cropped)
label_pred, label_score, _ = siamese_model.prediction(
img1, img2)
label_pred_, label_score_ = new_sess.run(
[label_pred, label_score])
# with sess_siam.as_default():
# with sess_siam.graph.as_default():
# img1, img2 = dataloader(img_cropped)
# label_pred, label_score, _ = siamese_model.prediction(img1, img2)
# label_pred_, label_score_ = sess_siam.run([label_pred, label_score])
# cv2.imwrite('/home/tracy/YOLOv3_TensorFlow/temp/' + str(i) + '_' + str(j) + '.jpg', img_cropped)
# print("Writting %s"%img)
# test_one_img('/home/tracy/data/TrafficSign_test/Images1/' + img)
# print('Done writing %s'%img)
# Choose the one label with highest score
pred_labels = np.nonzero(label_pred_)
pred_scores = label_score_[pred_labels]
# print("pred_scores: ", pred_scores)
if len(pred_scores) > 0:
label_index = np.argmax(pred_scores)
label_index = pred_labels[0][label_index] + 2
# labels_[j] = label_index
plot_one_box(img_ori, [x0, y0, x1, y1],
label_index=label_index,
label=args.classes_all[label_index] +
', {:.2f}%'.format(scores_[j] * 100),
color=color_table[labels_[j]])
cv2.namedWindow('Detection result', 0)
cv2.resizeWindow('Detection result', 2400, 1800)
cv2.imshow('Detection result', img_ori)
cv2.imwrite('detection_result.jpg', img_ori)
cv2.waitKey(0)
'/home/tracy/PycharmProjects/SiameseNet/checkpoint/checkpoint_alerted/model_alterd'
)
label = sess_yolo.run(label_pred_)
# print(label_pred_plh)
print(label)
sess_yolo.close()
with sess_siam.as_default():
with sess_siam.graph.as_default():
img1, img2 = dataloader(img)
img1_, img2_ = img1.eval(), img2.eval()
label_pred_siam, label_score_siam, distance_siam = siamese_model.prediction(
img1_, img2_)
label_pred_siam_, label_score_siam_, distance_siam_ = sess_siam.run(
[label_pred_siam, label_score_siam, distance_siam])
# intermediate_layer_model = Model(inputs=siamese_model.input,
# outputs=siamese_model.get_layer(
# 'encoder/layer_with_weights-1/beta/.ATTRIBUTES/VARIABLE_VALUE').output)
# intermediate_output = intermediate_layer_model.predict(img1. img2)
print(
"\033[1;32m**************** result from siamese net ****************\033[0m"
)
# print('layer1: ', intermediate_output)
print(label_pred_siam_)
# print('---------------------------------------------------')
# sess_siam.run(tf.Print(img1, [img1, img1.shape], message='Debug message: '))