def load(self):
# Remove nodes from graph or reset entire default graph
tf.reset_default_graph()
# Generate colors for drawing bounding boxes.
hsv_tuples = [(x / len(self.class_names), 1., 1.) for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors))
random.seed(10101) # Fixed seed for consistent colors across runs.
random.shuffle(self.colors) # Shuffle colors to decorrelate adjacent classes.
random.seed(None) # Reset seed to default.
# Generate output tensor targets for filtered bounding boxes.
self.x = tf.placeholder(tf.float32, shape=[None, Input_height, Input_width, channels])
self.image_shape = tf.placeholder(tf.float32, shape=[2,])
# self.is_training = tf.placeholder(tf.bool)
# image_shape = np.array([image.size[0], image.size[1]]) # tf.placeholder(tf.float32, shape=[2,])
# Generate output tensor targets for filtered bounding boxes.
# scale1, scale2, scale3 = YOLOv3(self.x, len(self.class_names), trainable=self.trainable, is_training=self.is_training).feature_extractor()
scale1, scale2, scale3 = YOLOv3(self.x, len(self.class_names), trainable=self.trainable).feature_extractor()
scale_total = [scale1, scale2, scale3]
# detect
# for 3D
boxes, scores, classes, alphas, hwls = predict(scale_total, self.anchors, len(self.class_names), self.image_shape,
score_threshold=self.threshold, iou_threshold=self.ignore_thresh)
# Add ops to save and restore all the variables
saver = tf.train.Saver(var_list=None if self.COCO==True else tf.trainable_variables())
# Allowing GPU memory growth
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config = config)
sess.run(tf.global_variables_initializer())
epoch = input('Entrer a check point at epoch:')
# For the case of COCO
epoch = epoch if self.COCO == False else 2000
checkpoint = path + "/save_model/kitti/kitti_l2loss" + ".ckpt-" + str(epoch)
try:
aaa = checkpoint + '.meta'
my_abs_path = Path(aaa).resolve()
except FileNotFoundError:
print("Not yet training!")
else:
saver.restore(sess, checkpoint)
print("checkpoint: ", checkpoint)
print("already training!")
return boxes, scores, classes, alphas, hwls, sess
# tf Graph input
#graph = tf.Graph()
#global_step
# input
X = tf.placeholder(tf.float32, [None, 128, 128, 3])
# expected outputs
Y1 = tf.placeholder(tf.float32, shape=[None, Input_shape/32, Input_shape/32, 3, (5+num_classes)])
Y2 = tf.placeholder(tf.float32, shape=[None, Input_shape/16, Input_shape/16, 3, (5+num_classes)])
Y3 = tf.placeholder(tf.float32, shape=[None, Input_shape/8, Input_shape/8, 3, (5+num_classes)])
# Generate output tensor targets for filtered bounding boxes.
scale1, scale2, scale3 = YOLOv3(X, num_classes).feature_extractor()
scale_total = [scale1, scale2, scale3]
# detect
boxes, scores, classes = predict(scale_total, anchors, num_classes, input_shape)
# Label
Y_ = [Y1, Y2, Y3]
# Calculate loss
loss = compute_loss(scale_total, Y_, anchors, num_classes, print_loss=True)
# Optimizer
optimizer = tf.train.AdamOptimizer(lr).minimize(loss) #, global_step=global_step)
##################################################################################################################
# Init for saving models. They will be saved into a directory named 'checkpoints'.
# Only the last checkpoint is kept.
if not os.path.exists("checkpoints"):
os.mkdir("checkpoints")
saver = tf.train.Saver()
x_reshape = tf.reshape(X, [-1, Input_shape, Input_shape, 1])
tf.summary.image("input", x_reshape)
# STEP 2: Building the graph #######################################################################################
# Building the graph
# Generate output tensor targets for filtered bounding boxes.
scale1, scale2, scale3 = YOLOv3(X, len(classes_data)).feature_extractor()
scale_total = []
scale_total.append(scale1)
scale_total.append(scale2)
scale_total.append(scale3)
with tf.name_scope("Loss"):
# predict boxes, score, classes
boxes, scores, classes = predict(scale_total,
anchors,
len(classes_data),
S,
score_threshold=threshold,
iou_threshold=ignore_thresh)
# Label
y_true = preprocess_true_boxes(Y, 416, anchors, len(classes_data))
# Calculate loss
loss = compute_loss(scale_total, y_true, anchors, len(classes_data))
with tf.name_scope("Optimizer"):
# optimizer
learning_rate = 0.001
decay = 0.0005
optimizer = tf.train.RMSPropOptimizer(learning_rate,
decay).minimize(loss)
# STEP 3: Build the evaluation step ################################################################################
# with tf.name_scope("Accuracy"):
# # Model evaluation
def detect_image(self, image):
start = time.time()
# Generate colors for drawing bounding boxes.
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
random.seed(10101) # Fixed seed for consistent colors across runs.
random.shuffle(
self.colors) # Shuffle colors to decorrelate adjacent classes.
random.seed(None) # Reset seed to default.
if self.is_fixed_size:
assert self.INPUT_SIZE[0] % 32 == 0, 'Multiples of 32 required'
assert self.INPUT_SIZE[1] % 32 == 0, 'Multiples of 32 required'
boxed_image, image_shape = letterbox_image(
image, tuple(reversed(self.INPUT_SIZE)))
# boxed_image, image_shape = resize_image(image, tuple(reversed(self.INPUT_SIZE)))
else:
new_image_size = (image.width - (image.width % 32),
image.height - (image.height % 32))
boxed_image, image_shape = letterbox_image(image, new_image_size)
# boxed_image, image_shape = resize_image(image, new_image_size)
image_data = np.array(boxed_image, dtype='float32')
print("heights, widths:", image_shape)
image_data /= 255.
inputs = np.expand_dims(image_data, 0) # Add batch dimension. #
# Generate output tensor targets for filtered bounding boxes.
x = tf.placeholder(tf.float32,
shape=[None, Input_shape, Input_shape, channels])
# image_shape = np.array([image.size[0], image.size[1]]) # tf.placeholder(tf.float32, shape=[2,])
# Generate output tensor targets for filtered bounding boxes.
scale1, scale2, scale3 = YOLOv3(x, len(
self.class_names)).feature_extractor()
scale_total = [scale1, scale2, scale3]
# detect
boxes, scores, classes = predict(scale_total,
self.anchors,
len(self.class_names),
image_shape,
score_threshold=self.threshold,
iou_threshold=self.ignore_thresh)
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
writer = tf.summary.FileWriter('./graphs', tf.get_default_graph())
# tensorboard --logdir="./graphs" --port 6006
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Restore variables from disk.
epoch = input('Entrer a check point at epoch(%10=0):')
checkpoint = "/home/minh/stage/saver_model/model" + str(
epoch) + ".ckpt"
try:
my_abs_path = Path(checkpoint).resolve()
saver.restore(sess, checkpoint)
except FileNotFoundError:
print("Not yet training!")
else:
print("already training!")
out_boxes, out_scores, out_classes = sess.run(
[boxes, scores, classes], feed_dict={x: inputs})
writer.close()
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
# Visualisation#################################################################################################
font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] +
0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 400 # do day cua BB
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
top, left, bottom, right = box # y_min, x_min, y_max, x_max
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
print(label, (left, top),
(right, bottom)) # (x_min, y_min), (x_max, y_max)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for j in range(thickness):
draw.rectangle([left + j, top + j, right - j, bottom - j],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
end = time.time()
print(end - start)
return image
