class Yolov5DetectionModel(DetectionModel):
def check_dependencies(self) -> None:
check_requirements(["torch", "yolov5"])
def load_model(self):
"""
Detection model is initialized and set to self.model.
"""
import yolov5
try:
model = yolov5.load(self.model_path, device=self.device)
self.set_model(model)
except Exception as e:
raise TypeError("model_path is not a valid yolov5 model path: ", e)
def set_model(self, model: Any):
"""
Sets the underlying YOLOv5 model.
Args:
model: Any
A YOLOv5 model
"""
if model.__class__.__module__ not in ["yolov5.models.common", "models.common"]:
raise Exception(f"Not a yolov5 model: {type(model)}")
model.conf = self.confidence_threshold
self.model = model
# set category_mapping
if not self.category_mapping:
category_mapping = {str(ind): category_name for ind, category_name in enumerate(self.category_names)}
self.category_mapping = category_mapping
def perform_inference(self, image: np.ndarray):
"""
Prediction is performed using self.model and the prediction result is set to self._original_predictions.
Args:
image: np.ndarray
A numpy array that contains the image to be predicted. 3 channel image should be in RGB order.
"""
# Confirm model is loaded
if self.model is None:
raise ValueError("Model is not loaded, load it by calling .load_model()")
if self.image_size is not None:
prediction_result = self.model(image, size=self.image_size)
else:
prediction_result = self.model(image)
self._original_predictions = prediction_result
@property
def num_categories(self):
"""
Returns number of categories
"""
return len(self.model.names)
@property
def has_mask(self):
"""
Returns if model output contains segmentation mask
"""
import yolov5
from packaging import version
if version.parse(yolov5.__version__) < version.parse("6.2.0"):
return False
else:
return False # fix when yolov5 supports segmentation models
@property
def category_names(self):
if check_package_minimum_version("yolov5", "6.2.0"):
return list(self.model.names.values())
else:
return self.model.names
def _create_object_prediction_list_from_original_predictions(
self,
shift_amount_list: Optional[List[List[int]]] = [[0, 0]],
full_shape_list: Optional[List[List[int]]] = None,
):
"""
self._original_predictions is converted to a list of prediction.ObjectPrediction and set to
self._object_prediction_list_per_image.
Args:
shift_amount_list: list of list
To shift the box and mask predictions from sliced image to full sized image, should
be in the form of List[[shift_x, shift_y],[shift_x, shift_y],...]
full_shape_list: list of list
Size of the full image after shifting, should be in the form of
List[[height, width],[height, width],...]
"""
original_predictions = self._original_predictions
# compatilibty for sahi v0.8.15
shift_amount_list = fix_shift_amount_list(shift_amount_list)
full_shape_list = fix_full_shape_list(full_shape_list)
# handle all predictions
object_prediction_list_per_image = []
for image_ind, image_predictions_in_xyxy_format in enumerate(original_predictions.xyxy):
shift_amount = shift_amount_list[image_ind]
full_shape = None if full_shape_list is None else full_shape_list[image_ind]
object_prediction_list = []
# process predictions
for prediction in image_predictions_in_xyxy_format.cpu().detach().numpy():
x1 = prediction[0]
y1 = prediction[1]
x2 = prediction[2]
y2 = prediction[3]
bbox = [x1, y1, x2, y2]
score = prediction[4]
category_id = int(prediction[5])
category_name = self.category_mapping[str(category_id)]
# fix negative box coords
bbox[0] = max(0, bbox[0])
bbox[1] = max(0, bbox[1])
bbox[2] = max(0, bbox[2])
bbox[3] = max(0, bbox[3])
# fix out of image box coords
if full_shape is not None:
bbox[0] = min(full_shape[1], bbox[0])
bbox[1] = min(full_shape[0], bbox[1])
bbox[2] = min(full_shape[1], bbox[2])
bbox[3] = min(full_shape[0], bbox[3])
# ignore invalid predictions
if not (bbox[0] < bbox[2]) or not (bbox[1] < bbox[3]):
logger.warning(f"ignoring invalid prediction with bbox: {bbox}")
continue
object_prediction = ObjectPrediction(
bbox=bbox,
category_id=category_id,
score=score,
segmentation=None,
category_name=category_name,
shift_amount=shift_amount,
full_shape=full_shape,
)
object_prediction_list.append(object_prediction)
object_prediction_list_per_image.append(object_prediction_list)
self._object_prediction_list_per_image = object_prediction_list_per_image