def segmentation(img):
no_of_frames =1
no_of_batches = int(no_of_frames/batch_size)
saver = tf.train.Saver(tf.trainable_variables(), write_version=tf.train.SaverDef.V2)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
saver.restore(sess, project_dir + "training_logs/model_4/checkpoints/model_4_epoch_50.ckpt")
# print("weights loaded")
step=0
batch_pointer = 0
for j in range(batch_size):
batch_imgs = np.zeros((batch_size, img_height, img_width, 3), dtype=np.float32)
img = cv2.resize(img, (img_width, img_height))
img = img - train_mean_channels
img=np.expand_dims(img,axis=0)
batch_imgs = img
batch_feed_dict = model.create_feed_dict(imgs_batch=batch_imgs,
early_drop_prob=0.0, late_drop_prob=0.0)
logits = sess.run(model.logits, feed_dict=batch_feed_dict)
predictions = np.argmax(logits, axis=3)
pred_img = predictions[j]
pred_img_color = label_img_to_color(pred_img)
img = batch_imgs[j] + train_mean_channels
#overlayed_img = 0.2*img + 0.8*pred_img_color
overlayed_img=1*pred_img_color
return[overlayed_img,pred_img_color]
def evaluate_on_val():
random.shuffle(val_data)
val_img_paths, val_trainId_label_paths = zip(*val_data)
val_batch_losses = []
batch_pointer = 0
for step in range(no_of_val_batches):
batch_imgs = np.zeros((batch_size, img_height, img_width, 3),
dtype=np.float32)
batch_onehot_labels = np.zeros(
(batch_size, img_height, img_width, no_of_classes),
dtype=np.float32)
for i in range(batch_size):
# read the next img:
img = cv2.imread(val_img_paths[batch_pointer + i], -1)
img = img - train_mean_channels
batch_imgs[i] = img
# read the next label:
trainId_label = cv2.imread(
val_trainId_label_paths[batch_pointer + i], -1)
# convert the label to onehot:
onehot_label = np.zeros((img_height, img_width, no_of_classes),
dtype=np.float32)
onehot_label[layer_idx, component_idx, trainId_label] = 1
batch_onehot_labels[i] = onehot_label
batch_pointer += batch_size
batch_feed_dict = model.create_feed_dict(
imgs_batch=batch_imgs,
early_drop_prob=0.0,
late_drop_prob=0.0,
onehot_labels_batch=batch_onehot_labels)
# run a forward pass, get the batch loss and the logits:
batch_loss, logits = sess.run([model.loss, model.logits],
feed_dict=batch_feed_dict)
val_batch_losses.append(batch_loss)
print(
"epoch: %d/%d, val step: %d/%d, val batch loss: %g" %
(epoch + 1, no_of_epochs, step + 1, no_of_val_batches, batch_loss))
if step < 4:
# save the predicted label images to disk for debugging and
# qualitative evaluation:
predictions = np.argmax(logits, axis=3)
for i in range(batch_size):
pred_img = predictions[i]
label_img_color = label_img_to_color(pred_img)
cv2.imwrite((model.debug_imgs_dir + "val_" + str(epoch) + "_" +
str(step) + "_" + str(i) + ".png"),
label_img_color)
val_loss = np.mean(val_batch_losses)
return val_loss
batch_pointer += batch_size
batch_feed_dict = model.create_feed_dict(imgs_batch=batch_imgs,
early_drop_prob=0.0, late_drop_prob=0.0)
# run a forward pass and get the logits:
logits = sess.run(model.logits, feed_dict=batch_feed_dict)
print("step: %d/%d" % (step+1, no_of_batches))
# save all predicted label images overlayed on the input frames to results_dir:
predictions = np.argmax(logits, axis=3)
for i in range(batch_size):
pred_img = predictions[i]
pred_img_color = label_img_to_color(pred_img)
img = batch_imgs[i] + train_mean_channels
img_file_name = img_paths[i].split("/")[-1]
img_name = img_file_name.split(".png")[0]
pred_path = results_dir + img_name + "_pred.png"
overlayed_img = 0.3*img + 0.7*pred_img_color
#overlayed_img = 1*pred_img_color
#imgg=np.concatenate((img,overlayed_img), axis=1)
cv2.imwrite(pred_path, overlayed_img)
end=t.time()
print('time consumed: {:.0f}m {:.0f}s '.format((end-start)//60,(end-start)%60))
def evaluate_on_val():
# random.shuffle(val_data)
# val_img_paths, val_trainId_label_paths = zip(*val_data)
val_batch_losses = []
batch_pointer = 0
for step in range(no_of_val_batches):
batch_imgs = np.zeros((batch_size, img_height, img_width, 1),
dtype=np.float32)
batch_onehot_labels = np.zeros(
(batch_size, img_height, img_width, no_of_classes),
dtype=np.float32)
batch_existance_labels = np.zeros((batch_size, 4), dtype=np.float32)
for i in range(batch_size):
# read the next img:
img = cv2.imread(val_img_paths[batch_pointer + i], -1)
img = cv2.resize(img, (img_width, img_height),
interpolation=cv2.INTER_NEAREST)
# read the next label:
trainId_label = cv2.imread(
train_trainId_label_paths[batch_pointer + i], -1)
if (np.random.randint(0, 2, 1) == 0):
img = img[100:, :, :]
trainId_label = trainId_label[100:, :]
if (np.random.randint(0, 2, 1) == 0):
img, trainId_label = scaler((img, trainId_label))
img, trainId_label = cropper((img, trainId_label))
trainId_label = cv2.resize(trainId_label, (img_width, img_height),
interpolation=cv2.INTER_NEAREST)
img, trainId_label = normalizer((img, trainId_label))
batch_imgs[i] = img
# convert the label to onehot:
onehot_label = np.zeros((img_height, img_width, no_of_classes),
dtype=np.float32)
onehot_label[layer_idx, component_idx, np.int32(trainId_label)] = 1
batch_onehot_labels[i] = onehot_label
# train_existance_label = np.array([0, 0, 0, 0])
# train_existance_label[np.unique(trainId_label)[np.unique(trainId_label)>0] - 1] = 1
# read existance label as well
train_existance_label = val_existance_labels[batch_pointer + i]
batch_existance_labels[i] = train_existance_label
batch_pointer += batch_size
batch_feed_dict = model.create_feed_dict(
imgs_batch=batch_imgs,
early_drop_prob=0.0,
late_drop_prob=0.0,
onehot_labels_batch=batch_onehot_labels,
existance_labels_batch=batch_existance_labels)
# run a forward pass, get the batch loss and the logits:
batch_loss, logits = sess.run([model.loss, model.net.logits],
feed_dict=batch_feed_dict)
val_batch_losses.append(batch_loss)
print(
"epoch: %d/%d, val step: %d/%d, val batch loss: %g" %
(epoch + 1, no_of_epochs, step + 1, no_of_val_batches, batch_loss))
if step < 4:
# save the predicted label images to disk for debugging and
# qualitative evaluation:
predictions = np.argmax(logits, axis=3)
for i in range(batch_size):
pred_img = predictions[i]
label_img_color = label_img_to_color(pred_img)
cv2.imwrite((model.debug_imgs_dir + "val_" + str(epoch) + "_" +
str(step) + "_" + str(i) + ".png"),
label_img_color)
val_loss = np.mean(val_batch_losses)
return val_loss
