def predict():
fasterRCNN = Network()
fasterRCNN.build(is_training=False)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, os.path.join(CHECKPOINTS_PATH, "model_final.ckpt"))
print("Model restored.")
base_extractor = VGG16(include_top=False)
extractor = Model(inputs=base_extractor.input, outputs=base_extractor.get_layer('block5_conv3').output)
predict_img_names = os.listdir(PREDICT_IMG_DATA_PATH)
for predict_img_name in predict_img_names:
img_data, img_info = get_predict_data(predict_img_name)
features = extractor.predict(img_data, steps=1)
rois, scores, regression_parameter = sess.run(
[fasterRCNN._predictions["rois"], fasterRCNN._predictions["cls_prob"],
fasterRCNN._predictions["bbox_pred"]],
feed_dict={fasterRCNN.feature_map: features,
fasterRCNN.image_info: img_info})
boxes = rois[:, 1:5] / img_info[2]
scores = np.reshape(scores, [scores.shape[0], -1])
regression_parameter = np.reshape(regression_parameter, [regression_parameter.shape[0], -1])
pred_boxes = bbox_transform_inv(boxes, regression_parameter)
pred_boxes = clip_boxes(pred_boxes, [img_info[0] / img_info[2], img_info[1] / img_info[2]])
result_list = []
for class_index, class_name in enumerate(CLASSES[1:]):
class_index += 1 # 因为跳过了背景类别
cls_boxes = pred_boxes[:, 4 * class_index:4 * (class_index + 1)] # TODO:
cls_scores = scores[:, class_index]
detections = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(detections, NMS_THRESH)
detections = detections[keep, :]
inds = np.where(detections[:, -1] >= CONF_THRESH)[0] # 筛选结果
for i in inds:
result_for_a_class = []
bbox = detections[i, :4]
score = detections[i, -1]
result_for_a_class.append(predict_img_name)
result_for_a_class.append(class_name)
result_for_a_class.append(score)
for coordinate in bbox:
result_for_a_class.append(coordinate)
result_list.append(result_for_a_class)
# result_for_a_class = [fileName,class_name,score,x1,y1,x2,y2]
if len(result_list) == 0:
continue
if TXT_RESULT_WANTED:
write_txt_result(result_list)
if IS_VISIBLE:
visualization(result_list)
def train():
fasterRCNN = Network()
fasterRCNN.build(is_training=True)
train_op = tf.train.MomentumOptimizer(learning_rate=0.001,
momentum=0.9).minimize(
fasterRCNN._losses['total_loss'])
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
base_extractor = VGG16(include_top=False)
extractor = Model(
inputs=base_extractor.input,
outputs=base_extractor.get_layer('block5_conv3').output)
train_img_names = os.listdir(TRAIN_IMG_DATA_PATH)
trained_times = 0
for epoch in range(1, MAX_EPOCH + 1):
random.shuffle(train_img_names)
for train_img_name in train_img_names:
img_data, boxes, img_info = get_train_data(train_img_name)
features = extractor.predict(img_data, steps=1)
sess.run(train_op,
feed_dict={
fasterRCNN.feature_map: features,
fasterRCNN.gt_boxes: boxes,
fasterRCNN.image_info: img_info
})
trained_times += 1
if trained_times % 10 == 0:
total_loss = sess.run(fasterRCNN._losses['total_loss'],
feed_dict={
fasterRCNN.feature_map: features,
fasterRCNN.gt_boxes: boxes,
fasterRCNN.image_info: img_info
})
print('epoch:{}, trained_times:{}, loss:{}'.format(
epoch, trained_times, total_loss))
if epoch % 10 == 0:
save_path = saver.save(
sess,
os.path.join(CHECKPOINTS_PATH,
"model_" + str(epoch) + ".ckpt"))
print("Model saved in path: %s" % save_path)
save_path = saver.save(
sess, os.path.join(CHECKPOINTS_PATH, "model_final.ckpt"))
print("Model saved in path: %s" % save_path)
