def processImage(record):
object_df = record.object_df
object_coords = object_df[['ymin', 'xmin', 'ymax', 'xmax']].as_matrix()
object_labels = object_df['class'].tolist()
image = record.image
vs.draw_bounding_boxes_on_image(image=image,
boxes=object_coords,
display_str_list_list=object_labels)
image.show()
def test_draw_bounding_boxes_on_image(self):
test_image = self.create_colorful_test_image()
test_image = Image.fromarray(test_image)
width_original, height_original = test_image.size
boxes = np.array([[0.25, 0.75, 0.4, 0.6], [0.1, 0.1, 0.9, 0.9]])
visualization_utils.draw_bounding_boxes_on_image(test_image, boxes)
width_final, height_final = test_image.size
self.assertEqual(width_original, width_final)
self.assertEqual(height_original, height_final)
def test_draw_bounding_boxes_on_image(self):
test_image = self.create_colorful_test_image()
test_image = Image.fromarray(test_image)
width_original, height_original = test_image.size
boxes = np.array([[0.25, 0.75, 0.4, 0.6],
[0.1, 0.1, 0.9, 0.9]])
visualization_utils.draw_bounding_boxes_on_image(test_image, boxes)
width_final, height_final = test_image.size
self.assertEqual(width_original, width_final)
self.assertEqual(height_original, height_final)
def main(_):
assert FLAGS.tfrecord, '`tfrecord` is missing.'
filename = [FLAGS.tfrecord]
number = number_of_jpeg(filename)
filename_queue = tf.train.string_input_producer(filename) #Read tfrecord
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
'image/height':
tf.FixedLenFeature([], tf.int64),
'image/width':
tf.FixedLenFeature([], tf.int64),
'image/object/bbox/xmin':
tf.VarLenFeature(tf.float32),
'image/object/bbox/xmax':
tf.VarLenFeature(tf.float32),
'image/object/bbox/ymin':
tf.VarLenFeature(tf.float32),
'image/object/bbox/ymax':
tf.VarLenFeature(tf.float32),
'image/object/class/label':
tf.VarLenFeature(tf.int64),
'image/encoded':
tf.FixedLenFeature([], tf.string),
})
image = tf.image.decode_jpeg(features['image/encoded'])
#label = tf.cast(features['image/object/class/label'], tf.int32)
if not os.path.exists(FLAGS.output_filepath):
os.makedirs(FLAGS.output_filepath)
if FLAGS.pdf:
pp = PdfPages(FLAGS.output_filepath + '/out.pdf')
category_index = None
if FLAGS.pbtxt:
category_index = label_map_util.create_category_index_from_labelmap(
FLAGS.pbtxt)
print(category_index)
with tf.Session() as sess:
init_op = tf.initialize_all_variables()
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
t_h = tf.cast(features['image/height'], tf.int32)
t_w = tf.cast(features['image/width'], tf.int32)
#single_shape = tf.parallel_stack([1])
d_label = tf.sparse_tensor_to_dense(
features['image/object/class/label'], default_value=0)
d_xm = tf.sparse_tensor_to_dense(features['image/object/bbox/xmin'],
default_value=0)
d_xM = tf.sparse_tensor_to_dense(features['image/object/bbox/xmax'],
default_value=0)
d_ym = tf.sparse_tensor_to_dense(features['image/object/bbox/ymin'],
default_value=0)
d_yM = tf.sparse_tensor_to_dense(features['image/object/bbox/ymax'],
default_value=0)
#t_xm = tf.cast(features['image/object/bbox/xmin'], [tf.float32])
#t_xM = tf.cast(features['image/object/bbox/xmax'], [tf.float32])
#t_ym = tf.cast(features['image/object/bbox/ymin'], [tf.float32])
#t_yM = tf.cast(features['image/object/bbox/ymax'], [tf.float32])
t_l = tf.reshape(d_label, [-1])
t_xm = tf.reshape(d_xm, [-1])
t_xM = tf.reshape(d_xM, [-1])
t_ym = tf.reshape(d_ym, [-1])
t_yM = tf.reshape(d_yM, [-1])
boxid = 0
#axarr.autoscale(True)
for i in range(number):
#image = tf.reshape(image, [w, h, 3])
example, ls, w, h, xm, xM, ym, yM = sess.run(
[image, d_label, t_w, t_h, t_xm, t_xM, t_ym, t_yM])
img_data_jpg = tf.image.convert_image_dtype(example,
dtype=tf.uint8)
encode_image_jpg = tf.image.encode_jpeg(img_data_jpg)
folder = FLAGS.output_filepath
bx = []
bl = []
label = []
for idx, l in enumerate(ls):
#if l not in label:
#label.append(str(l))
bx.append([ym[idx], xm[idx], yM[idx], xM[idx]])
#bl.append([str(l)])
labelname = str(l)
if category_index:
labeldic = category_index[int(l)]
labelname = labeldic['name']
bl.append([labelname])
if labelname not in label:
label.append(labelname)
newname=folder+"/"+str(boxid)+'['+str(w)+'x'+str(h)+']'+'Label_'+labelname+'@[' \
+str(int(round(xm[idx]*w)))+','+str(int(round(ym[idx]*h)))+']to[' \
+str(int(round(xM[idx]*w)))+','+str(int(round(yM[idx]*h)))+'].jpg'
with tf.gfile.GFile(newname, 'wb') as f:
f.write(encode_image_jpg.eval())
boxid += 1
if FLAGS.pdf:
num = len(label)
with Image.open(newname) as im:
shape = len(np.array(im).shape)
#print(shape)
for idx, l in enumerate(bl):
lid = label.index(l[0])
if shape == 3:
gb = int(lid * 255.0 / num)
c = (255, 255 - gb, gb)
elif shape == 2:
c = int(255 - (lid * 128.0 / num))
#print(bx[idx])
#print(bl[idx])
#print(c)
vis_util.draw_bounding_boxes_on_image(
im,
np.array([bx[idx]]),
color=c,
display_str_list_list=[bl[idx]])
plt.imshow(im)
pp.savefig()
print(str(bl[idx]) + ":" + str(w) + "x" + str(h))
#input('press return to continue')
if FLAGS.pdf:
pp.close()
coord.request_stop()
coord.join(threads)
