def create_pred_movie_no_conf(conf, predList, moviename, outmovie, outtype):
predLocs, predscores, predmaxscores = predList
# assert false, 'stop here'
tdir = tempfile.mkdtemp()
cap = cv2.VideoCapture(moviename)
nframes = int(cap.get(cvc.FRAME_COUNT))
cmap = cm.get_cmap('jet')
rgba = cmap(np.linspace(0, 1, conf.n_classes))
fig = mpl.figure.Figure(figsize=(9, 4))
canvas = FigureCanvasAgg(fig)
for curl in range(nframes):
framein = myutils.readframe(cap, curl)
framein = crop_images(framein, conf)
fig.clf()
ax1 = fig.add_subplot(1, 2, 1)
ax1.imshow(framein[:, :, 0], cmap=cm.gray)
ax1.scatter(predLocs[curl, :, 0, 0], predLocs[curl, :, 0, 1], # hold=True,
c=cm.hsv(np.linspace(0, 1 - old_div(1., conf.n_classes), conf.n_classes)),
s=20, linewidths=0, edgecolors='face')
ax1.axis('off')
ax2 = fig.add_subplot(1, 2, 2)
if outtype == 1:
curpreds = predscores[curl, :, :, :, 0]
elif outtype == 2:
curpreds = predscores[curl, :, :, :, 0] * 2 - 1
rgbim = create_pred_image(curpreds, conf.n_classes)
ax2.imshow(rgbim)
ax2.axis('off')
fname = "test_{:06d}.png".format(curl)
# to printout without X.
# From: http://www.dalkescientific.com/writings/diary/archive/2005/04/23/matplotlib_without_gui.html
# The size * the dpi gives the final image size
# a4"x4" image * 80 dpi ==> 320x320 pixel image
canvas.print_figure(os.path.join(tdir, fname), dpi=80)
# below is the easy way.
# plt.savefig(os.path.join(tdir,fname))
tfilestr = os.path.join(tdir, 'test_*.png')
mencoder_cmd = "mencoder mf://" + tfilestr + " -frames " + "{:d}".format(
nframes) + " -mf type=png:fps=15 -o " + outmovie + " -ovc lavc -lavcopts vcodec=mpeg4:vbitrate=2000000"
os.system(mencoder_cmd)
cap.release()
def getpatch(cap,fnum,curloc):
# matlab sometimes can access an additional frame at the end
# which others can't.
curp = None
psz = conf.sel_sz
if fnum > cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT):
if fnum > cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT)+1:
raise ValueError('Accessing frames beyond the length of the video')
return curp
framein = myutils.readframe(cap,fnum-1)
framein = framein[:,0:(old_div(framein.shape[1],2)),:]
testp = myutils.padgrab(framein,0,curloc[1]-old_div(psz,2),curloc[1]+old_div(psz,2),
curloc[0]-old_div(psz,2),curloc[0]+old_div(psz,2),0,framein.shape[2])
curp = np.array(scalepatches(testp,conf.scale,conf.numscale,conf.rescale))
return curp
def getpatch(cap, fnum, curloc):
# matlab sometimes can access an additional frame at the end
# which others can't.
curp = None
psz = conf.sel_sz
if fnum > cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT):
if fnum > cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT) + 1:
raise ValueError('Accessing frames beyond the length of the video')
return curp
framein = myutils.readframe(cap, fnum - 1)
framein = framein[:, 0:(old_div(framein.shape[1], 2)), :]
testp = myutils.padgrab(framein, 0, curloc[1] - old_div(psz, 2),
curloc[1] + old_div(psz, 2),
curloc[0] - old_div(psz, 2),
curloc[0] + old_div(psz, 2), 0, framein.shape[2])
curp = np.array(
scalepatches(testp, conf.scale, conf.numscale, conf.rescale))
return curp
def predictMovie(model_file, inmovie, outmovie):
pred, saver, pholders = initPredSession()
tdir = tempfile.mkdtemp()
cap = cv2.VideoCapture(inmovie)
nframes = int(cap.get(cvc.FRAME_COUNT))
plt.gray()
# with writer.saving(fig,"test_results.mp4",4):
fig = plt.figure()
with tf.Session() as sess:
saver.restore(sess, model_file)
count = 0
for fnum in range(nframes):
plt.clf()
plt.axis('off')
framein = myutils.readframe(cap, fnum)
framein = framein[:, 0:(old_div(framein.shape[1], 2)), 0:1]
out = predict(copy.copy(framein), sess, pred, pholders)
plt.imshow(framein[:, :, 0])
maxndx = np.argmax(out[0, :, :, 0])
loc = np.unravel_index(maxndx, out.shape[1:3])
scalefactor = conf.rescale * conf.pool_scale
plt.scatter(loc[1] * scalefactor, loc[0] * scalefactor, hold=True)
fname = "test_{:06d}.png".format(count)
plt.savefig(os.path.join(tdir, fname))
count += 1
# ffmpeg_cmd = "ffmpeg -r 30 " + \
# "-f image2 -i '/path/to/your/picName%d.png' -qscale 0 '/path/to/your/new/video.avi'
tfilestr = os.path.join(tdir, 'test_*.png')
mencoder_cmd = "mencoder mf://" + tfilestr + " -frames " + "{:d}".format(
count
) + " -mf type=png:fps=15 -o " + outmovie + " -ovc lavc -lavcopts vcodec=mpeg4:vbitrate=2000000"
# print(mencoder_cmd)
os.system(mencoder_cmd)
cap.release()
def predictMovie(model_file,inmovie,outmovie):
pred,saver,pholders = initPredSession()
tdir = tempfile.mkdtemp()
cap = cv2.VideoCapture(inmovie)
nframes = int(cap.get(cvc.FRAME_COUNT))
plt.gray()
# with writer.saving(fig,"test_results.mp4",4):
fig = plt.figure()
with tf.Session() as sess:
saver.restore(sess, model_file)
count = 0
for fnum in range(nframes):
plt.clf()
plt.axis('off')
framein = myutils.readframe(cap,fnum)
framein = framein[:,0:(old_div(framein.shape[1],2)),0:1]
out = predict(copy.copy(framein),sess,pred,pholders)
plt.imshow(framein[:,:,0])
maxndx = np.argmax(out[0,:,:,0])
loc = np.unravel_index(maxndx,out.shape[1:3])
scalefactor = conf.rescale*conf.pool_scale
plt.scatter(loc[1]*scalefactor,loc[0]*scalefactor,hold=True)
fname = "test_{:06d}.png".format(count)
plt.savefig(os.path.join(tdir,fname))
count+=1
# ffmpeg_cmd = "ffmpeg -r 30 " + \
# "-f image2 -i '/path/to/your/picName%d.png' -qscale 0 '/path/to/your/new/video.avi'
tfilestr = os.path.join(tdir,'test_*.png')
mencoder_cmd = "mencoder mf://" + tfilestr + " -frames " + "{:d}".format(count) + " -mf type=png:fps=15 -o " + outmovie + " -ovc lavc -lavcopts vcodec=mpeg4:vbitrate=2000000"
# print(mencoder_cmd)
os.system(mencoder_cmd)
cap.release()
def createDB():
L = sio.loadmat(conf.labelfile)
pts = L['pts']
ts = L['ts']
expid = L['expidx']
count = 0; valcount = 0
psz = conf.sel_sz
map_size = 100000*conf.psz**2*3
createValdata(False)
isval,localdirs,seldirs = loadValdata()
lmdbfilename =os.path.join(conf.cachedir,conf.trainfilename)
vallmdbfilename =os.path.join(conf.cachedir,conf.valfilename)
if os.path.isdir(lmdbfilename):
shutil.rmtree(lmdbfilename)
if os.path.isdir(vallmdbfilename):
shutil.rmtree(vallmdbfilename)
env = lmdb.open(lmdbfilename, map_size=map_size)
valenv = lmdb.open(vallmdbfilename, map_size=map_size)
with env.begin(write=True) as txn,valenv.begin(write=True) as valtxn:
for ndx,dirname in enumerate(localdirs):
if not seldirs[ndx]:
continue
expname = os.path.basename(dirname)
frames = np.where(expid[0,:] == (ndx + 1))[0]
curdir = localdirs[ndx]
cap = cv2.VideoCapture(os.path.join(curdir,'movie_comb.avi'))
curtxn = valtxn if isval.count(ndx) else txn
for curl in frames:
fnum = ts[0,curl]
curloc = np.round(pts[0,:,curl]).astype('int')
if fnum > cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT):
if fnum > cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT)+1:
raise ValueError('Accessing frames beyond the length of the video')
continue
framein = myutils.readframe(cap,fnum-1)
framein = framein[:,0:(old_div(framein.shape[1],2)),0:1]
datum = createDatum(framein,1)
str_id = createID(expname,curloc,fnum)
curtxn.put(str_id.encode('ascii'), datum.SerializeToString())
if isval.count(ndx):
valcount+=1
else:
count+=1
cap.release() # close the movie handles
print('Done %d of %d movies' % (ndx,len(localdirs)))
env.close() # close the database
valenv.close()
print('%d,%d number of pos examples added to the db and valdb' %(count,valcount))
plt.rcParams['animation.ffmpeg_path'] = '/usr/bin/ffmpeg'
curdir = '/home/mayank/Dropbox/AdamVideos/multiPoint/M122_20140828/M122_20140828_v002'
# FFMpegWriter = manimation.writers['ffmpeg_file']
FFMpegWriter = manimation.writers['ffmpeg']
writer = FFMpegWriter(fps=15, bitrate=-1)
fig = plt.figure()
cap = cv2.VideoCapture(os.path.join(curdir, 'movie_comb.avi'))
plt.gray()
with writer.saving(fig, "test_results_mencoder.mp4", 4):
for fnum in range(0, 50):
# print(fnum)
plt.clf()
framein = myutils.readframe(cap, fnum)
plt.imshow(framein)
writer.grab_frame()
cap.release()
# In[13]:
get_ipython().magic(u'matplotlib inline')
plt.rcParams['animation.ffmpeg_path'] = '/usr/bin/ffmpeg'
curdir = '/home/mayank/Dropbox/AdamVideos/multiPoint/M122_20140828/M122_20140828_v002'
fig = plt.figure()
cap = cv2.VideoCapture(os.path.join(curdir, 'movie_comb.avi'))
plt.gray()
for fnum in range(0, 2):
def createDB():
L = sio.loadmat(conf.labelfile)
pts = L['pts']
ts = L['ts']
expid = L['expidx']
count = 0
valcount = 0
psz = conf.sel_sz
map_size = 100000 * conf.psz**2 * 3
createValdata(False)
isval, localdirs, seldirs = loadValdata()
lmdbfilename = os.path.join(conf.cachedir, conf.trainfilename)
vallmdbfilename = os.path.join(conf.cachedir, conf.valfilename)
if os.path.isdir(lmdbfilename):
shutil.rmtree(lmdbfilename)
if os.path.isdir(vallmdbfilename):
shutil.rmtree(vallmdbfilename)
env = lmdb.open(lmdbfilename, map_size=map_size)
valenv = lmdb.open(vallmdbfilename, map_size=map_size)
with env.begin(write=True) as txn, valenv.begin(write=True) as valtxn:
for ndx, dirname in enumerate(localdirs):
if not seldirs[ndx]:
continue
expname = os.path.basename(dirname)
frames = np.where(expid[0, :] == (ndx + 1))[0]
curdir = localdirs[ndx]
cap = cv2.VideoCapture(os.path.join(curdir, 'movie_comb.avi'))
curtxn = valtxn if isval.count(ndx) else txn
for curl in frames:
fnum = ts[0, curl]
curloc = np.round(pts[0, :, curl]).astype('int')
if fnum > cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT):
if fnum > cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT) + 1:
raise ValueError(
'Accessing frames beyond the length of the video')
continue
framein = myutils.readframe(cap, fnum - 1)
framein = framein[:, 0:(old_div(framein.shape[1], 2)), 0:1]
datum = createDatum(framein, 1)
str_id = createID(expname, curloc, fnum)
curtxn.put(str_id.encode('ascii'), datum.SerializeToString())
if isval.count(ndx):
valcount += 1
else:
count += 1
cap.release() # close the movie handles
print('Done %d of %d movies' % (ndx, len(localdirs)))
env.close() # close the database
valenv.close()
print('%d,%d number of pos examples added to the db and valdb' %
(count, valcount))
def create_full_tf_record(conf):
lbl = h5py.File(conf.labelfile, 'r')
pts = np.array(lbl['pts'])
ts = np.array(lbl['ts']).squeeze().astype('int')
exp_id = np.array(lbl['expidx']).squeeze().astype('int')
view = conf.view
count = 0
val_count = 0
create_val_data(conf)
is_val, local_dirs, sel_dirs = load_val_data(conf)
train_filename = os.path.join(conf.cachedir, conf.fulltrainfilename)
env = tf.python_io.TFRecordWriter(train_filename + '.tfrecords')
for ndx, dirname in enumerate(local_dirs):
if not sel_dirs[ndx]:
continue
exp_name = conf.getexpname(dirname)
frames = np.where(exp_id == (ndx + 1))[0]
cap = cv2.VideoCapture(local_dirs[ndx])
cur_env = env
for curl in frames:
fnum = ts[curl]
if fnum > cap.get(cvc.FRAME_COUNT):
if fnum > cap.get(cvc.FRAME_COUNT) + 1:
raise ValueError('Accessing frames beyond ' +
'the length of the video for' +
' {} expid {:d} '.format(exp_name, ndx) +
' at t {:d}'.format(fnum)
)
continue
frame_in = myutils.readframe(cap, fnum - 1)
c_loc = conf.cropLoc[tuple(frame_in.shape[0:2])]
frame_in = PoseTools.crop_images(frame_in, conf)
frame_in = frame_in[:, :, 0:1]
cur_loc = np.round(pts[curl, :, view, :]).astype('int')
cur_loc[:, 0] = cur_loc[:, 0] - c_loc[1] # ugh, the nasty x-y business.
cur_loc[:, 1] = cur_loc[:, 1] - c_loc[0]
cur_loc = cur_loc.clip(min=0.1)
rows = frame_in.shape[0]
cols = frame_in.shape[1]
if np.ndim(frame_in) > 2:
depth = frame_in.shape[2]
else:
depth = 1
image_raw = frame_in.tostring()
example = tf.train.Example(features=tf.train.Features(feature={
'height': int64_feature(rows),
'width': int64_feature(cols),
'depth': int64_feature(depth),
'locs': float_feature(cur_loc.flatten()),
'expndx': float_feature(ndx),
'ts': float_feature(curl),
'image_raw': bytes_feature(image_raw)}))
cur_env.write(example.SerializeToString())
count += 1
cap.release() # close the movie handles
print('Done %d of %d movies, count:%d val:%d' % (ndx, len(local_dirs), count, val_count))
env.close() # close the database
print('%d,%d number of pos examples added to the db and val-db' % (count, val_count))
def create_full_tf_record(conf):
lbl = h5py.File(conf.labelfile, 'r')
pts = np.array(lbl['pts'])
ts = np.array(lbl['ts']).squeeze().astype('int')
exp_id = np.array(lbl['expidx']).squeeze().astype('int')
view = conf.view
count = 0
val_count = 0
create_val_data(conf)
is_val, local_dirs, sel_dirs = load_val_data(conf)
train_filename = os.path.join(conf.cachedir, conf.fulltrainfilename)
env = tf.python_io.TFRecordWriter(train_filename + '.tfrecords')
for ndx, dirname in enumerate(local_dirs):
if not sel_dirs[ndx]:
continue
exp_name = conf.getexpname(dirname)
frames = np.where(exp_id == (ndx + 1))[0]
cap = cv2.VideoCapture(local_dirs[ndx])
cur_env = env
for curl in frames:
fnum = ts[curl]
if fnum > cap.get(cvc.FRAME_COUNT):
if fnum > cap.get(cvc.FRAME_COUNT) + 1:
raise ValueError('Accessing frames beyond ' +
'the length of the video for' +
' {} expid {:d} '.format(exp_name, ndx) +
' at t {:d}'.format(fnum))
continue
frame_in = myutils.readframe(cap, fnum - 1)
c_loc = conf.cropLoc[tuple(frame_in.shape[0:2])]
frame_in = PoseTools.crop_images(frame_in, conf)
frame_in = frame_in[:, :, 0:1]
cur_loc = np.round(pts[curl, :, view, :]).astype('int')
cur_loc[:,
0] = cur_loc[:,
0] - c_loc[1] # ugh, the nasty x-y business.
cur_loc[:, 1] = cur_loc[:, 1] - c_loc[0]
cur_loc = cur_loc.clip(min=0.1)
rows = frame_in.shape[0]
cols = frame_in.shape[1]
if np.ndim(frame_in) > 2:
depth = frame_in.shape[2]
else:
depth = 1
image_raw = frame_in.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'height': int64_feature(rows),
'width': int64_feature(cols),
'depth': int64_feature(depth),
'locs': float_feature(cur_loc.flatten()),
'expndx': float_feature(ndx),
'ts': float_feature(curl),
'image_raw': bytes_feature(image_raw)
}))
cur_env.write(example.SerializeToString())
count += 1
cap.release() # close the movie handles
print('Done %d of %d movies, count:%d val:%d' %
(ndx, len(local_dirs), count, val_count))
env.close() # close the database
print('%d,%d number of pos examples added to the db and val-db' %
(count, val_count))
ddff = np.abs(ddff).astype('uint8')
print(stat)
if stat:
plt.imshow(ddff)
print(ddff.max())
cap.release()
# In[ ]:
import myutils
reload(myutils)
import cv2
cap = cv2.VideoCapture('/home/mayank/Dropbox/AdamVideos/multiPoint/M118_20140730/M118_20140730_v002/movie_comb.avi')
ff = myutils.readframe(cap,1998)
print(cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT ))
cap.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, 1998)
print(cap.get(cv2.cv.CV_CAP_PROP_POS_FRAMES))
stat,ff = cap.read()
print(cap.get(cv2.cv.CV_CAP_PROP_POS_FRAMES))
print(stat)
# In[ ]:
import moviepy.video.io.ffmpeg_reader as freader
reader = freader.FFMPEG_VideoReader('/home/mayank/Dropbox/AdamVideos/multiPoint/M118_20140730/M118_20140730_v002/movie_comb.avi')
f1 = reader.get_frame((-2.-0.1)/reader.fps)
f2 = reader.get_frame((1.-0.1)/reader.fps)
fe = reader.get_frame((1998.-0.1)/reader.fps)