def saveGraph(path, ckpt_file, img_height, img_width):
tf.reset_default_graph()
fh = open(path, 'rb')
I = pil.open(fh)
I = I.resize((img_width, img_height), pil.ANTIALIAS)
I = np.array(I)
sfm = SfMLearner()
sfm.setup_inference(img_height,
img_width,
mode='depth')
saver = tf.train.Saver([var for var in tf.model_variables()])
with tf.Session() as sess:
saver.restore(sess, ckpt_file)
pred = sfm.inference(I[None,:,:,:], sess, mode='depth')
figure(figsize=(15,15))
fig = plt.figure(figsize=(15, 13))
# prep (x,y) for extra plotting
xs = np.linspace(0, 2*np.pi, 60) # from 0 to 2pi
ys = np.abs(np.sin(xs)) # absolute of sine
ax = []
# create subplot and append to ax
ax.append( fig.add_subplot(1, 2, 1) )
plt.imshow(I, alpha=1)
# create subplot and append to ax
ax.append( fig.add_subplot(1, 2, 2) )
plt.imshow(normalize_depth_for_display(pred['depth'][0,:,:,0]), alpha=1)
plt.savefig(os.path.join('DepthMask',path)) # finally, render the plot
# Clear the current axes.
plt.cla()
# Clear the current figure.
plt.clf()
# Closes all the figure windows.
plt.close('all')
fh.close()
def main():
basename = os.path.basename(FLAGS.ckpt_file)
# output_file = FLAGS.output_dir + '/' + basename
sfm = SfMLearner()
sfm.setup_inference(img_height=FLAGS.img_height, img_width=FLAGS.img_width, batch_size=FLAGS.batch_size, mode='depth')
saver = tf.train.Saver([var for var in tf.model_variables()])
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
for files in test_files_txt:
newfolder = os.path.splitext(files)[0]
savepathimage= FLAGS.save_output_depthimg + '/' + newfolder
#print(savepathimage)
if not os.path.exists(savepathimage):
os.makedirs(savepathimage)
savepathdata= FLAGS.save_output_depthdata + '/' + newfolder
if not os.path.exists(savepathdata):
os.makedirs(savepathdata)
print(files)
with open(os.path.join(FLAGS.test_file_list,files), 'r') as f:
test_files = f.readlines()
test_files = [t[:-1] for t in test_files]
# if not os.path.exists(FLAGS.output_dir):
# os.makedirs(FLAGS.output_dir)
with tf.Session(config=config) as sess:
saver.restore(sess, FLAGS.ckpt_file)
pred_all = []
for t in range(0, len(test_files), FLAGS.batch_size):
if t % 100 == 0:
print('processing %s: %d/%d' % (basename, t, len(test_files)))
inputs = np.zeros(
(FLAGS.batch_size, FLAGS.img_height, FLAGS.img_width, 3),
dtype=np.uint8)
for b in range(FLAGS.batch_size):
idx = t + b
print(len(test_files))
if idx >= len(test_files):
break
# fh = open(test_files[idx], 'r')
# print(fh)
# raw_im = Image.open(fh)
# scaled_im = raw_im.resize((FLAGS.img_width, FLAGS.img_height), Image.ANTIALIAS)
# inputs[b] = np.array(scaled_im)
im = scipy.misc.imread(test_files[idx])
inputs[b] = scipy.misc.imresize(im, (FLAGS.img_height, FLAGS.img_width))
pred = sfm.inference(inputs, sess, mode='depth')
for b in range(FLAGS.batch_size):
idx = t + b
if idx >= len(test_files):
break
dept= pred['depth'][b,:,:,0]
imagesave = normalize_depth_for_display(dept)
plt.imsave(os.path.join(savepathimage , newfolder + '_'+ str(idx) + '.png'), imagesave)
pred_all.append(pred['depth'][b,:,:,0])
sio.savemat(os.path.join(savepathdata,newfolder + '_'+ str(idx)), {'D': dept.squeeze()})
from SfMLearner import SfMLearner
from utils import normalize_depth_for_display
img_height = 128
img_width = 416
ckpt_file = 'models/model-190532'
fh = open('misc/sample.png', 'rb')
I = pil.open(fh)
I = I.resize((img_width, img_height), pil.ANTIALIAS)
I = np.array(I)
# In[2]:
sfm = SfMLearner()
sfm.setup_inference(img_height, img_width, mode='depth')
# In[3]:
saver = tf.train.Saver([var for var in tf.model_variables()])
with tf.Session() as sess:
saver.restore(sess, ckpt_file)
pred = sfm.inference(I[None, :, :, :], sess, mode='depth')
# In[4]:
figure(figsize=(15, 15))
subplot(1, 2, 1)
imshow(I)
subplot(1, 2, 2)
imshow(normalize_depth_for_display(pred['depth'][0, :, :, 0]))
def save_image(self, img_name, image):
print("Save File : {}".format(img_name))
plt.imsave(img_name,
normalize_depth_for_display(image['depth'][0, :, :, 0]))
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
# adding end
img_height = 128
img_width = 416
ckpt_file = 'models/kitti_depth_model/model-190532'
fh = open('misc/sample.png', 'rb')
I = pil.open(fh)
I = I.resize((img_width, img_height), pil.ANTIALIAS)
I = np.array(I)
sfm = SfMLearner()
sfm.setup_inference(img_height, img_width, mode='depth')
saver = tf.train.Saver([var for var in tf.model_variables()])
with tf.Session() as sess:
saver.restore(sess, ckpt_file)
pred = sfm.inference(I[None, :, :, :], sess, mode='depth')
plt.figure()
plt.subplot(121)
cv2.imshow("source", I)
plt.subplot(122)
cv2.imshow("result", normalize_depth_for_display(pred['depth'][0, :, :, 0]))
cv2.waitKey()
#cap2 = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FPS, 60) # カメラFPSを60FPSに設定
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 440) # カメラ画像の横幅を1280に設定
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 180) # カメラ画像の縦幅を720に設定
while True:
# VideoCaptureから1フレーム読み込む
ret, frame = cap.read()
# スクリーンショットを撮りたい関係で1/4サイズに縮小
frame = cv2.resize(frame, ( (int(416), int(128))) )
pred = sfm.inference(frame[None,:,:,:], sess, mode='depth')
# 加工なし画像を表示する
cv2.imshow('Raw Frame', frame)
# depth画像表示
#cv2.imshow('Raw Frame222222222', normalize_depth_for_display(pred['depth'][0,:,:,0]))
#img2 = cv2.imread(normalize_depth_for_display(pred['depth'][0,:,:,0]).astype(unit8))
cv2.imshow('Raw Frame33333333333333', normalize_depth_for_display(pred['depth'][0,:,:,0]))
# キー入力を1ms待って、k が27（ESC）だったらBreakする
k = cv2.waitKey(1)
if k == 27:
break
# キャプチャをリリースして、ウィンドウをすべて閉じる
cap.release()
cv2.destroyAllWindows()
