def process(inputs, ctx, **kwargs):
frame, is_streaming = helpers.load_image(inputs, 'input', rgb=False)
LOG.info("frame shape: {}".format(frame.shape))
bboxes, probabilities = detect_bboxes(
ctx.drivers[0],
frame,
PARAMS.get("threshold", .5),
)
for i, bbox in enumerate(bboxes):
draw_bbox(
frame,
bbox.astype(int),
label="Detected {}\nprobability: {:.2f}".format(
PARAMS["object_name"], probabilities[i]),
)
if is_streaming:
output = frame[:, :, ::-1]
else:
_, buf = cv2.imencode('.jpg', frame)
output = buf.tostring()
return {
'output': output,
'bboxes': bboxes.tolist(),
'probabilities': probabilities.tolist(),
}
def process(inputs, ctx, **kwargs):
frame, is_streaming = helpers.load_image(inputs, 'input', rgb=False)
LOG.info("frame shape: {}".format(frame.shape))
detect_driver = ctx.drivers[0]
bboxes, probabilities = detect_bboxes(
detect_driver,
frame,
PARAMS.get("threshold", .5),
)
head_poses = []
if len(bboxes) > 0:
head_pose_driver = ctx.drivers[1]
head_poses = head.head_pose(head_pose_driver, frame, bboxes, rgb=False)
for i, bbox in enumerate(bboxes):
draw_bbox(
frame,
bbox.astype(int),
label="Detected face\n"
"probability: {:.2f}\n"
"yaw: {:.2f}\n"
"pitch: {:.2f}\n"
"roll: {:.2f}".format(probabilities[i], head_poses[i][0],
head_poses[i][1], head_poses[i][2]),
)
head.draw_axis(frame, bbox, head_poses[i])
head_poses = head_poses.tolist()
if is_streaming:
output = frame[:, :, ::-1]
else:
_, buf = cv2.imencode('.jpg', frame)
output = buf.tostring()
return {
'output': output,
'bboxes': bboxes.tolist(),
'probabilities': probabilities.tolist(),
'poses': head_poses,
}
def process(inputs, ctx, **kwargs):
frame, is_streaming = helpers.load_image(inputs, 'input', rgb=False)
LOG.info("frame shape: {}".format(frame.shape))
bboxes, probabilities = detect_bboxes(ctx.drivers[0], frame,
PARAMS.get("detect_threshold", .5))
for bbox in bboxes.astype(int):
cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]),
(0, 255, 0), 1)
if is_streaming:
output = frame
else:
_, buf = cv2.imencode('.jpg', frame)
output = buf.tostring()
return {
'output': output,
'bboxes': bboxes.tolist(),
'probabilities': probabilities.tolist(),
}
def process(inputs, ctx, **kwargs):
frame, is_streaming = helpers.load_image(inputs, 'input', rgb=False)
LOG.info("frame shape: {}".format(frame.shape))
detect_driver = ctx.drivers[0]
bboxes, probabilities = detect_bboxes(
detect_driver,
frame,
PARAMS.get("threshold", .5),
)
age, gender = [], []
if len(bboxes) > 0:
age_gender_driver = ctx.drivers[1]
age, gender = age_gender(age_gender_driver, frame, bboxes, rgb=False)
for i, bbox in enumerate(bboxes):
draw_bbox(
frame,
bbox.astype(int),
label="Detected face\nprobability: {:.2f}\nage: {}, gender: {}"
.format(probabilities[i], age[i],
"male" if gender[i] == 1 else "female"),
)
if is_streaming:
output = frame[:, :, ::-1]
else:
_, buf = cv2.imencode('.jpg', frame)
output = buf.tostring()
return {
'output': output,
'bboxes': bboxes.tolist(),
'probabilities': probabilities.tolist(),
'age': age,
'gender': gender,
}