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229 | class InferenceModelsGazeAdapter(Model):
"""Roboflow ONNX Gaze model.
This class is responsible for handling the ONNX Gaze model, including
loading the model, preprocessing the input, and performing inference.
Attributes:
gaze_onnx_session (onnxruntime.InferenceSession): ONNX Runtime session for gaze detection inference.
"""
def __init__(self, *args, api_key: str = None, **kwargs):
"""Initializes the Gaze with the given arguments and keyword arguments."""
super().__init__()
self.task_type = "gaze-detection"
self.api_key = api_key if api_key else API_KEY
extra_weights_provider_headers = get_extra_weights_provider_headers()
self._pipeline: FaceAndGazeDetectionMPAndL2CS = (
AutoModelPipeline.from_pretrained(
"face-and-gaze-detection",
api_key=self.api_key,
extra_weights_provider_headers=extra_weights_provider_headers,
allow_untrusted_packages=ALLOW_INFERENCE_MODELS_UNTRUSTED_PACKAGES,
allow_direct_local_storage_loading=ALLOW_INFERENCE_MODELS_DIRECTLY_ACCESS_LOCAL_PACKAGES,
)
)
def infer_from_request(
self, request: GazeDetectionInferenceRequest
) -> List[GazeDetectionInferenceResponse]:
"""Detect faces and gazes in image(s).
Args:
request (GazeDetectionInferenceRequest): The request object containing the image.
Returns:
List[GazeDetectionInferenceResponse]: The list of response objects containing the faces and corresponding gazes.
"""
timer_start = perf_counter()
if isinstance(request.image, list):
if len(request.image) > GAZE_MAX_BATCH_SIZE:
raise ValueError(
f"The maximum number of images that can be inferred with gaze detection at one time is {GAZE_MAX_BATCH_SIZE}"
)
imgs = request.image
else:
imgs = [request.image]
np_imgs = [load_image_bgr(img) for img in imgs]
avg_image_loading_time = (perf_counter() - timer_start) / len(np_imgs)
if not request.do_run_face_detection:
predictions_time_start = perf_counter()
gaze_detections = self._pipeline._gaze_detector.infer(images=np_imgs)
avg_image_prediction_time = (perf_counter() - predictions_time_start) / len(
np_imgs
)
predictions = []
for i, image in enumerate(np_imgs):
image_yaw = gaze_detections.yaw[i].item()
image_pitch = gaze_detections.pitch[i].item()
faces = [
Detection(
bounding_box=BoundingBox(
origin_x=0,
origin_y=0,
width=image.shape[1],
height=image.shape[0],
),
categories=[Category(score=1.0, category_name="face")],
keypoints=[],
)
]
gazes = [(image_yaw, image_pitch)]
image_predictions = self._make_response(
faces=faces,
gazes=gazes,
imgW=image.shape[1],
imgH=image.shape[0],
time_total=avg_image_prediction_time + avg_image_loading_time,
time_face_det=0,
time_gaze_det=avg_image_prediction_time,
)
predictions.append(image_predictions)
return predictions
predictions_time_start = perf_counter()
landmarks, faces, gazes = self._pipeline(images=np_imgs)
# prepare response
avg_image_prediction_time = (perf_counter() - predictions_time_start) / len(
np_imgs
)
response = []
for i in range(len(np_imgs)):
imgH, imgW, _ = np_imgs[i].shape
faces_per_img = faces[i]
landmarks_per_img = landmarks[i]
gazes_per_img = gazes[i]
processed_faces_for_image = []
processed_gazes_for_image = []
for detection_id in range(faces_per_img.xyxy.shape[0]):
min_x, min_y, max_x, max_y = faces_per_img.xyxy[detection_id].tolist()
width = max_x - min_x
height = max_y - min_y
score = faces_per_img.confidence[detection_id].item()
detection_keypoints = landmarks_per_img.xy[detection_id].tolist()
processed_keypoints = []
for x, y in detection_keypoints:
processed_keypoints.append(
NormalizedKeypoint(x=x / imgW, y=y / imgH)
)
face_detection_mp = Detection(
bounding_box=BoundingBox(
origin_x=min_x,
origin_y=min_y,
width=width,
height=height,
),
categories=[Category(score=score, category_name="face")],
keypoints=processed_keypoints,
)
processed_faces_for_image.append(face_detection_mp)
if gazes_per_img is None:
processed_gazes_for_image.append(None)
else:
processed_gazes_for_image.append(
(
gazes_per_img.yaw[detection_id].item(),
gazes_per_img.pitch[detection_id].item(),
)
)
response.append(
self._make_response(
processed_faces_for_image,
processed_gazes_for_image,
imgW,
imgH,
avg_image_prediction_time + avg_image_loading_time,
)
)
return response
def _make_response(
self,
faces: List[Detection],
gazes: List[Optional[Tuple[float, float]]],
imgW: int,
imgH: int,
time_total: float,
time_face_det: float = None,
time_gaze_det: float = None,
) -> GazeDetectionInferenceResponse:
"""Prepare response object from detected faces and corresponding gazes.
Args:
faces (List[Detection]): The detected faces.
gazes (List[tuple(float, float)]): The detected gazes (yaw, pitch).
imgW (int): The width (px) of original image.
imgH (int): The height (px) of original image.
time_total (float): The processing time.
time_face_det (float): The processing time.
time_gaze_det (float): The processing time.
Returns:
GazeDetectionInferenceResponse: The response object including the detected faces and gazes info.
"""
predictions = []
for face, gaze in zip(faces, gazes):
landmarks = []
for keypoint in face.keypoints:
x = min(max(int(keypoint.x * imgW), 0), imgW - 1)
y = min(max(int(keypoint.y * imgH), 0), imgH - 1)
landmarks.append(Point(x=x, y=y))
bbox = face.bounding_box
x_center = bbox.origin_x + bbox.width / 2
y_center = bbox.origin_y + bbox.height / 2
score = face.categories[0].score
prediction = GazeDetectionPrediction(
face=FaceDetectionPrediction(
**dict(
x=x_center,
y=y_center,
width=bbox.width,
height=bbox.height,
confidence=score,
class_name="face",
landmarks=landmarks,
)
),
yaw=gaze[0] if gaze is not None else None,
pitch=gaze[1] if gaze is not None else None,
)
predictions.append(prediction)
return GazeDetectionInferenceResponse(
predictions=predictions,
time=time_total,
time_face_det=time_face_det,
time_gaze_det=time_gaze_det,
)
|