def classify_with_archive(archive, image_files, use_gpu=True):
"""
"""
tmpdir = unzip_archive(archive)
caffemodel = None
deploy_file = None
mean_file = None
labels_file = None
for filename in os.listdir(tmpdir):
full_path = os.path.join(tmpdir, filename)
if filename.endswith('.caffemodel'):
caffemodel = full_path
elif filename == 'deploy.prototxt':
deploy_file = full_path
elif filename.endswith('.binaryproto'):
mean_file = full_path
elif filename == 'labels.txt':
labels_file = full_path
else:
print 'Unknown file:', filename
assert caffemodel is not None, 'Caffe model file not found'
assert deploy_file is not None, 'Deploy file not found'
classify(caffemodel, deploy_file, image_files,
mean_file=mean_file, labels_file=labels_file, use_gpu=use_gpu)
def classify_with_archive(archive, image_files, batch_size=None, use_gpu=True):
"""
"""
tmpdir = unzip_archive(archive)
caffemodel = None
deploy_file = None
mean_file = None
labels_file = None
for filename in os.listdir(tmpdir):
full_path = os.path.join(tmpdir, filename)
if filename.endswith('.caffemodel'):
caffemodel = full_path
elif filename == 'deploy.prototxt':
deploy_file = full_path
elif filename.endswith('.binaryproto'):
mean_file = full_path
elif filename == 'labels.txt':
labels_file = full_path
else:
print('Unknown file:', filename)
assert caffemodel is not None, 'Caffe model file not found'
assert deploy_file is not None, 'Deploy file not found'
classify(caffemodel,
deploy_file,
image_files,
mean_file=mean_file,
labels_file=labels_file,
batch_size=batch_size,
use_gpu=use_gpu)
def classify_image(image_files, use_gpu=True):
caffemodel = '../caffe/20151207-223900-80d9_epoch_30.0/snapshot_iter_19140.caffemodel'
deploy_file = '../caffe/20151207-223900-80d9_epoch_30.0/deploy.prototxt'
mean_file = '../caffe/20151207-223900-80d9_epoch_30.0/mean.binaryproto'
labels_file = '../caffe/20151207-223900-80d9_epoch_30.0/labels.txt'
classify(caffemodel, deploy_file, image_files,
mean_file=mean_file, labels_file=labels_file, use_gpu=use_gpu)
def classify_with_archive(archive, image_files, batch_size=None, use_gpu=True):
"""
"""
tmpdir = unzip_archive(archive)
caffemodel = None
deploy_file = None
mean_file = None
labels_file = None
for filename in os.listdir(tmpdir):
full_path = os.path.join(tmpdir, filename)
if filename.endswith('.caffemodel'):
caffemodel = full_path
elif filename == 'deploy.prototxt':
deploy_file = full_path
elif filename.endswith('.binaryproto'):
mean_file = full_path
elif filename == 'labels.txt':
labels_file = full_path
else:
print 'Unknown file:', filename
assert caffemodel is not None, 'Caffe model file not found'
assert deploy_file is not None, 'Deploy file not found'
class_labels = classify(caffemodel,
deploy_file,
image_files,
mean_file=mean_file,
labels_file=labels_file,
batch_size=batch_size,
use_gpu=use_gpu)
with open('result.csv', 'wb') as myfile:
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)
wr.writerow(class_labels)
def main():
drone = ps_drone.Drone()
drone.reset()
i=0
while (True):
cap = misc.face()
number=validation.classify("test/snapshot_iter_21120.caffemodel", "test/deploy.prototxt", cap,
"test/mean.binaryproto", "test/labels.txt")
print number
time.sleep(0.5)
i=i+1
if i>10:
exit()
def classify_archive():
archive = '/home/user/Desktop/Run_DIGITS_Locally/INSIDEv4.tar.gz'
image_file = ['/home/user/Desktop/Run_DIGITS_Locally/tst.png']
batch_size = None
use_gpu = True
tmpdir = unzip_archive(archive)
caffemodel = None
deploy_file = None
mean_file = None
labels_file = None
for filename in os.listdir(tmpdir):
full_path = os.path.join(tmpdir, filename)
if filename.endswith('.caffemodel'):
caffemodel = full_path
elif filename == 'deploy.prototxt':
deploy_file = full_path
elif filename.endswith('.binaryproto'):
mean_file = full_path
elif filename == 'labels.txt':
labels_file = full_path
else:
print 'Unknown file:', filename
assert caffemodel is not None, 'Caffe model file not found'
assert deploy_file is not None, 'Deploy file not found'
#print("NOt working: print Image file before call classify.\n")
#print(image_file)
resultLabel = classify(caffemodel,
deploy_file,
image_file,
mean_file=mean_file,
labels_file=labels_file,
batch_size=batch_size,
use_gpu=use_gpu)
return resultLabel
#Starts the Video Stream
#drone.startVideo()
#drone.showVideo()
#time.sleep(5)
Running = True
while Running:
start = timeit.timeit()
#Get Pictures to put in the Model
cap= drone.VideoImage
print type(cap)
New=numpy.array(cap)
cap = cv2.cvtColor(New, cv2.COLOR_BGR2RGB)
number=validation.classify("test/snapshot_iter_21120.caffemodel", "test/deploy.prototxt", cap,
"test/mean.binaryproto", "test/labels.txt")
#cv2.imshow("Frame", cap)
#cv2.imwrite("frontd.png", cap)
#Call The Classification Funktion here !
DirectionClass=number
print number
#Controll via Classified Data
#Sleep defines the inertia time
STime = 0.5
"""
if DirectionClass == 0: