def main():
# モデル定義
model = resnet.resnet152(pretrained=True)
if torch.cuda.is_available(): # GPUが利用可能か確認
device = 'cuda'
else:
device = 'cpu'
print('device is {0}'.format(device))
# print(model.weight.type)
model.to(device)
# 絶対パスに変換
opt = parse_opts()
outpath = os.path.abspath(opt.output)
apath = os.path.abspath(opt.input)
video_names = sorted(glob.glob(os.path.join(apath, '*')))
if os.path.exists('tmp'):
subprocess.call('rm -rf tmp', shell=True)
for vpath in video_names:
vname = os.path.splitext(os.path.basename(vpath))[0]
subprocess.call('mkdir tmp', shell=True)
subprocess.call(
'ffmpeg -loglevel warning -i {} tmp/image_%05d.jpg'.format(vpath),
shell=True)
images = sorted(glob.glob('tmp/*.jpg'))
if opt.only_hand:
print('convert to masked images')
for im in tqdm(images):
frame = cv2.imread(im)
maskedframe, _ = hd.detect(frame)
cv2.imwrite(im, maskedframe)
print('complete convert images')
print('extract {}\'s DeepFeatrue'.format(vname))
outputs = input_image(images, model)
# ファイルに保存
if not os.path.exists(outpath):
subprocess.call('mkdir {}'.format(outpath), shell=True)
savename = os.path.join(outpath, vname + '.npy')
np.save(savename, outputs)
subprocess.call('rm -rf tmp', shell=True)
foundRHandFullSize = RESIZE_FULL_RES_FACTOR*foundRHand #
print foundRHand
newx,newy,neww,newh = foundRHand
detectRight = False
else:
# check for left in same frame and in num_attmept next frames
if numLeftAttempts < MAX_NUM_LEFT_ATTMEPTS:
print "searching for left hand attempt#",numLeftAttempts
lhands = handDetect(hand_cascade, image, detectLeft = True, getNegSamples = False)
print lhands
lhValidator.feedNewHandList(lhands)
if lhValidator.checkForDetectionAndUpdateCounter():
foundLHand = np.array(lhValidator.foundHandPos)
print foundLHand
#foundLHand = HandDetection.convertLHdet_to_imgCoords(foundLHand, origSize=(160,120))
if HandDetection.validateLH_RelativeToRH(foundLHand, foundRHand):
lbeep.play()
fullResIm = HandDetection.getFullResImage(camera , fullResStream ,(160*RESIZE_FULL_RES_FACTOR , 120*RESIZE_FULL_RES_FACTOR))
foundLHandFullSize = RESIZE_FULL_RES_FACTOR*foundLHand
commandMenu.voiceOverImage(fullResIm,foundLHandFullSize,foundRHandFullSize)
fullResStream.seek(0)
fullResStream.truncate()
camera.resolution = (160,120)
numLeftAttempts += 1
if numLeftAttempts == MAX_NUM_LEFT_ATTMEPTS:
numLeftAttempts = 0 # reset
detectRight = True
detectRight = True
else:
# check for left in same frame and in num_attmept next frames
if numLeftAttempts < MAX_NUM_LEFT_ATTMEPTS:
print "searching for left hand attempt#", numLeftAttempts
lhands = handDetect(hand_cascade,
image,
detectLeft=True,
getNegSamples=False)
print lhands
lhValidator.feedNewHandList(lhands)
if lhValidator.checkForDetectionAndUpdateCounter():
foundLHand = np.array(lhValidator.foundHandPos)
print foundLHand
#foundLHand = HandDetection.convertLHdet_to_imgCoords(foundLHand, origSize=(160,120))
if HandDetection.validateLH_RelativeToRH(
foundLHand, foundRHand):
lbeep.play()
fullResIm = HandDetection.getFullResImage(
camera, fullResStream,
(160 * RESIZE_FULL_RES_FACTOR,
120 * RESIZE_FULL_RES_FACTOR))
foundLHandFullSize = RESIZE_FULL_RES_FACTOR * foundLHand
commandMenu.voiceOverImage(
fullResIm, foundLHandFullSize,
foundRHandFullSize)
fullResStream.seek(0)
fullResStream.truncate()
camera.resolution = (160, 120)
numLeftAttempts += 1
if numLeftAttempts == MAX_NUM_LEFT_ATTMEPTS:
#print rhValidator.foundHandPos
foundRHand = np.array(rhValidator.foundHandPos)
foundRHandFullSize = RESIZE_FULL_RES_FACTOR*foundRHand #
print foundRHand
newx,newy,neww,newh = foundRHand
# check for left in same frame and in num_attmept next frames
for i in range(NUM_LEFT_ATTMEPTS):
lhands = handDetect(hand_cascade, image, detectLeft = True, getNegSamples = False)
lhValidator.feedNewHandList(lhands)
if lhValidator.checkForDetectionAndUpdateCounter():
foundLHand = np.array(lhValidator.foundHandPos)
foundLHand = HandDetection.convertLHdet_to_imgCoords(foundLHand, origSize=(160,120))
if HandDetection.validateLH_RelativeToRH(foundLHand, foundRHand):
lbeep.play()
fullResIm = HandDetection.getFullResImage(camera , fullResStream ,(160*RESIZE_FULL_RES_FACTOR , 120*RESIZE_FULL_RES_FACTOR))
foundLHandFullSize = RESIZE_FULL_RES_FACTOR*foundLHand
commandMenu.readCommand(fullResIm,foundLHandFullSize,foundRHandFullSize)
fullResStream.seek(0)
fullResStream.truncate()
camera.resolution = (160,120)
camera.capture(stream, format='bgr' , use_video_port = True)
image = stream.array
cv2.imshow('image',image)
