def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', required=True,
help='Input image file.')
parser.add_argument('--threshold', '-t', type=float, default=0.1,
help='Classification probability threshold.')
parser.add_argument('--top_k', '-n', type=int, default=5,
help='Max number of returned classes.')
parser.add_argument('--sparse', '-s', action='store_true', default=False,
help='Use sparse tensors.')
parser.add_argument('--model', '-m', choices=('plants', 'insects', 'birds'), required=True,
help='Model to run.')
args = parser.parse_args()
model_type = {'plants': inaturalist_classification.PLANTS,
'insects': inaturalist_classification.INSECTS,
'birds': inaturalist_classification.BIRDS}[args.model]
with ImageInference(inaturalist_classification.model(model_type)) as inference:
image = Image.open(args.input)
if args.sparse:
configs = inaturalist_classification.sparse_configs(top_k=args.top_k,
threshold=args.threshold,
model_type=model_type)
result = inference.run(image, sparse_configs=configs)
classes = inaturalist_classification.get_classes_sparse(result)
else:
result = inference.run(image)
classes = inaturalist_classification.get_classes(result,
top_k=args.top_k,
threshold=args.threshold)
for i, (label, score) in enumerate(classes):
print('Result %d: %s (prob=%f)' % (i, label, score))
def main():
parser = argparse.ArgumentParser('Image classification camera inference example.')
parser.add_argument('--num_frames', '-n', type=int, default=None,
help='Sets the number of frames to run for, otherwise runs forever.')
parser.add_argument('--num_objects', '-c', type=int, default=3,
help='Sets the number of object interences to print.')
parser.add_argument('--nopreview', dest='preview', action='store_false', default=True,
help='Enable camera preview')
parser.add_argument('--threshold', '-t', type=float, default=0.1,
help='Classification probability threshold.')
parser.add_argument('--top_k', '-k', type=int, default=3,
help='Max number of returned classes.')
parser.add_argument('--sparse', '-s', action='store_true', default=False,
help='Use sparse tensors')
parser.add_argument('--model', '-m', choices=('plants', 'insects', 'birds'), required=True,
help='Model to run')
args = parser.parse_args()
model_type = {'plants': inaturalist_classification.PLANTS,
'insects': inaturalist_classification.INSECTS,
'birds': inaturalist_classification.BIRDS}[args.model]
with PiCamera(sensor_mode=4, framerate=10) as camera, \
CameraPreview(camera, enabled=args.preview), \
CameraInference(inaturalist_classification.model(model_type)) as inference:
for result in inference.run(args.num_frames):
classes = inaturalist_classification.get_classes(result, top_k=args.top_k, threshold=args.threshold)
print(classes_info(classes))
if classes:
camera.annotate_text = '%s (%.2f)' % classes[0]
def run(self):
while self._running:
try:
self.logger.debug(
'loading {} model ...'.format(self.model().name))
model = inaturalist_classification.model(self.model().value)
with CameraInference(model) as inference:
self.logger.debug('running inference ...')
for result in inference.run():
predictions = inaturalist_classification.get_classes(
result,
top_k=self.top_k_predictions())
outgoing_signals = []
for label, score in predictions:
signal_dict = {
'label': label,
'score': score,
}
outgoing_signal = Signal(signal_dict)
outgoing_signals.append(outgoing_signal)
if not self._running:
break
self.notify_signals(outgoing_signals)
except:
self.logger.exception('failed to get inference result!')
self.reset_camera()
self.release_camera()
def run(self, image):
self.result = self.inference.run(image)
self.bird_class = inaturalist_classification.get_classes(self.result,
top_k=1,
threshold=0.8)
if len(self.bird_class) == 0: # if nothing is found, return none
return None
elif self.bird_class[0][
0] == 'background': # if background is found, return none
return None
else: # If a bird clas is returned, flip the image, run again and ensure same class is returned
self.result_2 = self.inference.run(ImageOps.mirror(image))
self.bird_class_2 = inaturalist_classification.get_classes(
self.result_2, top_k=1, threshold=0.8)
if len(self.bird_class_2) == 0:
return None
else:
if self.bird_class_2[0][0] == self.bird_class[0][0]:
return self.bird_class[0]
else:
return None
def test_classification(self):
with TestImage(self.image_file) as image:
with ImageInference(m.model(self.model_type)) as inference:
if self.sparse:
configs = m.sparse_configs(top_k=self.TOP_K,
threshold=self.THRESHOLD,
model_type=self.model_type)
result = inference.run(image, sparse_configs=configs)
classes = m.get_classes_sparse(result)
else:
result = inference.run(image)
classes = m.get_classes(result, top_k=self.TOP_K, threshold=self.THRESHOLD)
self.check(classes)
def test_classification(self):
with TestImage(self.image_file) as image:
with ImageInference(m.model(self.model_type)) as inference:
if self.sparse:
configs = m.sparse_configs(top_k=self.TOP_K,
threshold=self.THRESHOLD,
model_type=self.model_type)
result = inference.run(image, sparse_configs=configs)
classes = m.get_classes_sparse(result)
else:
result = inference.run(image)
classes = m.get_classes(result, top_k=self.TOP_K, threshold=self.THRESHOLD)
self.check(classes)
#Test of varying resolution images#
scene_number = 3
from PIL import Image
import os
from aiy.vision.inference import ImageInference
from aiy.vision.models import inaturalist_classification
images_to_test = os.listdir("scene" + str(scene_number))
result_file = open('scene_' + str(scene_number) + '_res_test.txt', 'w+')
for image_name in images_to_test:
with ImageInference(
inaturalist_classification.model(
inaturalist_classification.BIRDS)) as inference:
image = Image.open('scene' + str(scene_number) + '/' + image_name)
print(image_name)
result = inference.run(image)
classes = inaturalist_classification.get_classes(result,
top_k=5,
threshold=0.0)
for i, (label, score) in enumerate(classes):
print('Test: %s Result %d: %s (prob=%f)' %
(image_name, i, label, score))
result_file.write('Test: %s Result %d: %s (prob=%f)\n' %
(image_name, i, label, score))
def main():
parser = argparse.ArgumentParser(
'Image classification camera inference example.')
parser.add_argument(
'--num_frames',
'-n',
type=int,
default=None,
help='Sets the number of frames to run for, otherwise runs forever.')
parser.add_argument('--num_objects',
'-c',
type=int,
default=3,
help='Sets the number of object interences to print.')
parser.add_argument('--nopreview',
dest='preview',
action='store_false',
default=True,
help='Enable camera preview')
parser.add_argument('--threshold',
'-t',
type=float,
default=0.1,
help='Classification probability threshold.')
parser.add_argument('--top_k',
'-k',
type=int,
default=3,
help='Max number of returned classes.')
parser.add_argument('--sparse',
'-s',
action='store_true',
default=False,
help='Use sparse tensors')
parser.add_argument('--model',
'-m',
choices=('plants', 'insects', 'birds'),
required=True,
help='Model to run')
args = parser.parse_args()
model_type = {
'plants': inaturalist_classification.PLANTS,
'insects': inaturalist_classification.INSECTS,
'birds': inaturalist_classification.BIRDS
}[args.model]
collector = collections.deque([], maxlen=7)
with PiCamera(sensor_mode=4, framerate=10) as camera, \
CameraPreview(camera, enabled=args.preview), \
CameraInference(inaturalist_classification.model(model_type)) as inference:
for result in inference.run(args.num_frames):
classes = inaturalist_classification.get_classes(
result, top_k=args.top_k, threshold=args.threshold)
if classes:
collector.appendleft(list(itertools.chain(*classes))[0])
if len(collector) > 5:
if collector[0] != 'background' and collector.count(
collector[0]) > 4:
log.info(classes_info(classes))
collector.clear()
if get_size(log_path) < max_log and flip(0.1):
camera.capture(log_path + ts() + '.jpg')
if classes:
camera.annotate_text = '%s (%.2f)' % classes[0]
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'--num_frames',
'-n',
type=int,
default=None,
help='Sets the number of frames to run for, otherwise runs forever.')
parser.add_argument('--threshold',
'-t',
type=float,
default=0.1,
help='Classification probability threshold.')
parser.add_argument('--top_k',
'-k',
type=int,
default=5,
help='Max number of returned classes.')
parser.add_argument('--sparse',
'-s',
action='store_true',
default=False,
help='Use sparse tensors.')
parser.add_argument('--model',
'-m',
choices=('plants', 'insects', 'birds'),
required=True,
help='Model to run.')
parser.add_argument('--nopreview',
dest='preview',
action='store_false',
default=True,
help='Enable camera preview')
args = parser.parse_args()
model_type = {
'plants': inaturalist_classification.PLANTS,
'insects': inaturalist_classification.INSECTS,
'birds': inaturalist_classification.BIRDS
}[args.model]
with PiCamera(sensor_mode=4, framerate=10) as camera, \
CameraPreview(camera, enabled=args.preview), \
CameraInference(inaturalist_classification.model(model_type)) as inference:
if args.sparse:
configs = inaturalist_classification.sparse_configs(
top_k=args.top_k,
threshold=args.threshold,
model_type=model_type)
for result in inference.run(args.num_frames,
sparse_configs=configs):
classes = inaturalist_classification.get_classes_sparse(result)
for i, (label, score) in enumerate(classes):
print('Result %d: %s (prob=%f)' % (i, label, score))
if classes:
camera.annotate_text = '%s (%.2f)' % classes[0]
else:
for result in inference.run(args.num_frames):
classes = inaturalist_classification.get_classes(
result, top_k=args.top_k, threshold=args.threshold)
for i, (label, score) in enumerate(classes):
print('Result %d: %s (prob=%f)' % (i, label, score))
if classes:
camera.annotate_text = '%s (%.2f)' % classes[0]
