def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', dest='input', required=True,
help='Input image file.')
parser.add_argument('--output', '-o', dest='output',
help='Output image file with bounding boxes.')
parser.add_argument('--sparse', '-s', action='store_true', default=False,
help='Use sparse tensors.')
parser.add_argument('--threshold', '-t', type=float, default=0.3,
help='Detection probability threshold.')
args = parser.parse_args()
with ImageInference(object_detection.model()) as inference:
image = Image.open(args.input)
image_center, offset = crop_center(image)
if args.sparse:
result = inference.run(image_center,
sparse_configs=object_detection.sparse_configs(args.threshold))
objects = object_detection.get_objects_sparse(result, offset)
else:
result = inference.run(image_center)
objects = object_detection.get_objects(result, args.threshold, offset)
for i, obj in enumerate(objects):
print('Object #%d: %s' % (i, obj))
if args.output:
draw = ImageDraw.Draw(image)
for i, obj in enumerate(objects):
x, y, width, height = obj.bounding_box
draw.rectangle((x, y, x + width, y + height), outline='red')
image.save(args.output)
def test_detection(self):
with TestImage(self.image_file) as image:
image_center, offset = crop_center(image)
with ImageInference(od.model()) as inference:
if self.sparse:
sparse_configs = od.sparse_configs(threshold=self.THRESHOLD)
result = inference.run(image_center, sparse_configs=sparse_configs)
objects = od.get_objects_sparse(result, offset)
else:
result = inference.run(image_center)
objects = od.get_objects(result, self.THRESHOLD, offset)
self.check(objects)
def write(self, b):
'''
Here is where the image data is received and made available at self.output
'''
try:
# b is the numpy array of the image, 3 bytes of color depth
self.output = np.reshape(
np.frombuffer(b, dtype=np.uint8),
(self.args.input_height, self.args.input_width, 3))
image_center, offset = crop_center(Image.fromarray(self.output))
if self.args.sparse:
result = self.inference.run(
image_center,
sparse_configs=object_detection.sparse_configs(
self.args.threshold))
objects = object_detection.get_objects_sparse(result, offset)
else:
result = self.inference.run(image_center)
objects = object_detection.get_objects(result,
self.args.threshold,
offset)
if self.DEBUG:
for i, obj in enumerate(objects):
x, y, width, height = obj.bounding_box
x = x + int(width / 2)
y = y + int(height / 2)
print(
'ImgCap - Object #{}: kind={} score={:.3f} pos(x,y)=({},{})'
.format(i, obj.kind, obj.score, x, y))
if len(objects):
with self.lock:
self.new_obj = True # indicates a new object is available
self.last_objects = deepcopy(objects)
self.last_image = np.copy(self.output)
if self.DEBUG:
print("ImgCap - Image.shape {}".format(self.output.shape))
print("ImgCap - Running at {:2.2f} Hz".format(
1 / (time.time() - self.prev_time)))
self.prev_time = time.time()
except Exception as e:
print("ImgCap error: {}".format(e))
finally:
return len(b)