def test_perform_inference_without_mask_output(self):
from sahi.model import MmdetDetectionModel
# init model
download_mmdet_retinanet_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=MmdetTestConstants.MMDET_RETINANET_MODEL_PATH,
config_path=MmdetTestConstants.MMDET_RETINANET_CONFIG_PATH,
confidence_threshold=0.5,
device=None,
category_remapping=None,
load_at_init=True,
)
# prepare image
image_path = "tests/data/small-vehicles1.jpeg"
image = read_image(image_path)
# perform inference
mmdet_detection_model.perform_inference(image)
original_predictions = mmdet_detection_model.original_predictions
boxes = original_predictions
# find box of first car detection with conf greater than 0.5
for box in boxes[2]:
print(len(box))
if len(box) == 5:
if box[4] > 0.5:
break
# compare
self.assertEqual(box[:4].astype("int").tolist(), [448, 309, 495, 341])
self.assertEqual(len(boxes), 80)
def test_get_sliced_prediction(self):
from sahi.model import MmdetDetectionModel
from sahi.predict import get_sliced_prediction
from tests.test_utils import (
download_mmdet_cascade_mask_rcnn_model,
mmdet_cascade_mask_rcnn_config_path,
mmdet_cascade_mask_rcnn_model_path,
)
# init model
download_mmdet_cascade_mask_rcnn_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=mmdet_cascade_mask_rcnn_model_path,
config_path=mmdet_cascade_mask_rcnn_config_path,
prediction_score_threshold=0.3,
device=None,
category_remapping=None,
)
mmdet_detection_model.load_model()
# prepare image
image_path = "tests/data/small-vehicles1.jpeg"
slice_height = 512
slice_width = 512
overlap_height_ratio = 0.1
overlap_width_ratio = 0.2
# get sliced prediction
prediction_result = get_sliced_prediction(
image=image_path,
detection_model=mmdet_detection_model,
slice_height=slice_height,
slice_width=slice_width,
overlap_height_ratio=overlap_height_ratio,
overlap_width_ratio=overlap_width_ratio,
)
object_prediction_list = prediction_result["object_prediction_list"]
# compare
self.assertEqual(len(object_prediction_list), 24)
num_person = 0
for object_prediction in object_prediction_list:
if object_prediction.category.name == "person":
num_person += 1
self.assertEqual(num_person, 0)
num_truck = 0
for object_prediction in object_prediction_list:
if object_prediction.category.name == "truck":
num_truck += 2
self.assertEqual(num_truck, 4)
num_car = 0
for object_prediction in object_prediction_list:
if object_prediction.category.name == "car":
num_car += 1
self.assertEqual(num_car, 22)
def test_get_prediction(self):
from sahi.model import MmdetDetectionModel
from sahi.predict import get_prediction
from sahi.prediction import PredictionInput
from tests.test_utils import (
download_mmdet_cascade_mask_rcnn_model,
mmdet_cascade_mask_rcnn_config_path,
mmdet_cascade_mask_rcnn_model_path,
)
# init model
download_mmdet_cascade_mask_rcnn_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=mmdet_cascade_mask_rcnn_model_path,
config_path=mmdet_cascade_mask_rcnn_config_path,
prediction_score_threshold=0.3,
device=None,
category_remapping=None,
)
mmdet_detection_model.load_model()
# prepare image
image_path = "tests/data/small-vehicles1.jpeg"
image = read_image(image_path)
# get full sized prediction
prediction_result = get_prediction(
image=image,
detection_model=mmdet_detection_model,
shift_amount=[0, 0],
full_image_size=None,
merger=None,
matcher=None,
)
object_prediction_list = prediction_result["object_prediction_list"]
# compare
self.assertEqual(len(object_prediction_list), 23)
num_person = 0
for object_prediction in object_prediction_list:
if object_prediction.category.name == "person":
num_person += 1
self.assertEqual(num_person, 0)
num_truck = 0
for object_prediction in object_prediction_list:
if object_prediction.category.name == "truck":
num_truck += 1
self.assertEqual(num_truck, 3)
num_car = 0
for object_prediction in object_prediction_list:
if object_prediction.category.name == "car":
num_car += 1
self.assertEqual(num_car, 20)
def test_load_model(self):
from sahi.model import MmdetDetectionModel
download_mmdet_cascade_mask_rcnn_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=mmdet_cascade_mask_rcnn_model_path,
config_path=mmdet_cascade_mask_rcnn_config_path,
prediction_score_threshold=0.3,
device=None,
category_remapping=None,
)
mmdet_detection_model.load_model()
self.assertNotEqual(mmdet_detection_model.model, None)
def test_get_prediction_mmdet(self):
from sahi.model import MmdetDetectionModel
from sahi.predict import get_prediction
from sahi.utils.mmdet import MmdetTestConstants, download_mmdet_cascade_mask_rcnn_model
# init model
download_mmdet_cascade_mask_rcnn_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=MmdetTestConstants.MMDET_CASCADEMASKRCNN_MODEL_PATH,
config_path=MmdetTestConstants.MMDET_CASCADEMASKRCNN_CONFIG_PATH,
confidence_threshold=0.3,
device=None,
category_remapping=None,
)
mmdet_detection_model.load_model()
# prepare image
image_path = "tests/data/small-vehicles1.jpeg"
image = read_image(image_path)
# get full sized prediction
prediction_result = get_prediction(
image=image,
detection_model=mmdet_detection_model,
shift_amount=[0, 0],
full_shape=None,
)
object_prediction_list = prediction_result.object_prediction_list
# compare
self.assertEqual(len(object_prediction_list), 19)
num_person = 0
for object_prediction in object_prediction_list:
if object_prediction.category.name == "person":
num_person += 1
self.assertEqual(num_person, 0)
num_truck = 0
for object_prediction in object_prediction_list:
if object_prediction.category.name == "truck":
num_truck += 1
self.assertEqual(num_truck, 0)
num_car = 0
for object_prediction in object_prediction_list:
if object_prediction.category.name == "car":
num_car += 1
self.assertEqual(num_car, 19)
def test_create_original_predictions_from_object_prediction_list(self):
from sahi.model import MmdetDetectionModel
from sahi.prediction import PredictionInput
# init model
download_mmdet_cascade_mask_rcnn_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=mmdet_cascade_mask_rcnn_model_path,
config_path=mmdet_cascade_mask_rcnn_config_path,
prediction_score_threshold=0.5,
device=None,
category_remapping=None,
)
mmdet_detection_model.load_model()
# prepare image
image_path = "tests/data/small-vehicles1.jpeg"
image = read_image(image_path)
# perform inference
mmdet_detection_model.perform_inference(image)
original_predictions_1 = mmdet_detection_model.original_predictions
# convert predictions to ObjectPrediction list
mmdet_detection_model.convert_original_predictions()
object_prediction_list = mmdet_detection_model.object_prediction_list
original_predictions_2 = mmdet_detection_model._create_original_predictions_from_object_prediction_list(
object_prediction_list)
# compare
self.assertEqual(len(original_predictions_1),
len(original_predictions_2)) # 2
self.assertEqual(len(original_predictions_1[0]),
len(original_predictions_2[0])) # 80
self.assertEqual(len(original_predictions_1[0][2]),
len(original_predictions_2[0][2])) # 25
self.assertEqual(type(original_predictions_1[0]),
type(original_predictions_2[0])) # list
self.assertEqual(original_predictions_1[0][2].dtype,
original_predictions_2[0][2].dtype) # float32
self.assertEqual(original_predictions_1[0][0][0].dtype,
original_predictions_2[0][0][0].dtype) # float32
self.assertEqual(original_predictions_1[1][0][0].dtype,
original_predictions_2[1][0][0].dtype) # bool
self.assertEqual(len(original_predictions_1[0][0][0]),
len(original_predictions_2[0][0][0])) # 5
self.assertEqual(len(original_predictions_1[0][1]),
len(original_predictions_1[0][1])) # 0
self.assertEqual(original_predictions_1[0][1].shape,
original_predictions_1[0][1].shape) # (0, 5)
def test_convert_original_predictions(self):
from sahi.model import MmdetDetectionModel
from sahi.prediction import PredictionInput
# init model
download_mmdet_cascade_mask_rcnn_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=mmdet_cascade_mask_rcnn_model_path,
config_path=mmdet_cascade_mask_rcnn_config_path,
prediction_score_threshold=0.5,
device=None,
category_remapping=None,
)
mmdet_detection_model.load_model()
# prepare image
image_path = "tests/data/small-vehicles1.jpeg"
image = read_image(image_path)
# perform inference
mmdet_detection_model.perform_inference(image)
# convert predictions to ObjectPrediction list
mmdet_detection_model.convert_original_predictions()
object_prediction_list_w_category_mapping = (
mmdet_detection_model.object_prediction_list)
# compare
self.assertEqual(len(object_prediction_list_w_category_mapping), 53)
self.assertEqual(
object_prediction_list_w_category_mapping[0].category.id, 0)
self.assertEqual(
object_prediction_list_w_category_mapping[0].category.name,
"person")
self.assertEqual(
object_prediction_list_w_category_mapping[0].bbox.to_coco_bbox(),
[337, 124, 8, 14],
)
self.assertEqual(
object_prediction_list_w_category_mapping[1].category.id, 2)
self.assertEqual(
object_prediction_list_w_category_mapping[1].category.name, "car")
self.assertEqual(
object_prediction_list_w_category_mapping[1].bbox.to_coco_bbox(),
[657, 204, 13, 10],
)
self.assertEqual(
object_prediction_list_w_category_mapping[5].category.id, 2)
self.assertEqual(
object_prediction_list_w_category_mapping[5].category.name, "car")
self.assertEqual(
object_prediction_list_w_category_mapping[2].bbox.to_coco_bbox(),
[760, 232, 20, 15],
)
def test_perform_inference_with_mask_output(self):
from sahi.model import MmdetDetectionModel
# init model
download_mmdet_cascade_mask_rcnn_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=MmdetTestConstants.MMDET_CASCADEMASKRCNN_MODEL_PATH,
config_path=MmdetTestConstants.MMDET_CASCADEMASKRCNN_CONFIG_PATH,
confidence_threshold=0.5,
device=None,
category_remapping=None,
load_at_init=True,
)
# prepare image
image_path = "tests/data/small-vehicles1.jpeg"
image = read_image(image_path)
# perform inference
mmdet_detection_model.perform_inference(image)
original_predictions = mmdet_detection_model.original_predictions
boxes = original_predictions[0]
masks = original_predictions[1]
# find box of first person detection with conf greater than 0.5
for box in boxes[0]:
print(len(box))
if len(box) == 5:
if box[4] > 0.5:
break
# compare
self.assertEqual(box[:4].astype("int").tolist(), [336, 123, 346, 139])
self.assertEqual(len(boxes), 80)
self.assertEqual(len(masks), 80)
def test_create_original_predictions_from_object_prediction_list_with_mask_output(
self, ):
from sahi.model import MmdetDetectionModel
# init model
download_mmdet_cascade_mask_rcnn_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=MmdetTestConstants.MMDET_CASCADEMASKRCNN_MODEL_PATH,
config_path=MmdetTestConstants.MMDET_CASCADEMASKRCNN_CONFIG_PATH,
confidence_threshold=0.5,
device=None,
category_remapping=None,
load_at_init=True,
)
# prepare image
image_path = "tests/data/small-vehicles1.jpeg"
image = read_image(image_path)
# perform inference
mmdet_detection_model.perform_inference(image)
original_predictions_1 = mmdet_detection_model.original_predictions
# convert predictions to ObjectPrediction list
mmdet_detection_model.convert_original_predictions()
object_prediction_list = mmdet_detection_model.object_prediction_list
original_predictions_2 = mmdet_detection_model._create_original_predictions_from_object_prediction_list(
object_prediction_list)
# compare
self.assertEqual(len(original_predictions_1),
len(original_predictions_2)) # 2
self.assertEqual(len(original_predictions_1[0]),
len(original_predictions_2[0])) # 80
self.assertEqual(len(original_predictions_1[0][2]),
len(original_predictions_2[0][2])) # 25
self.assertEqual(type(original_predictions_1[0]),
type(original_predictions_2[0])) # list
self.assertEqual(original_predictions_1[0][2].dtype,
original_predictions_2[0][2].dtype) # float32
self.assertEqual(original_predictions_1[0][0][0].dtype,
original_predictions_2[0][0][0].dtype) # float32
self.assertEqual(original_predictions_1[1][0][0].dtype,
original_predictions_2[1][0][0].dtype) # bool
self.assertEqual(len(original_predictions_1[0][0][0]),
len(original_predictions_2[0][0][0])) # 5
self.assertEqual(len(original_predictions_1[0][1]),
len(original_predictions_1[0][1])) # 0
self.assertEqual(original_predictions_1[0][1].shape,
original_predictions_1[0][1].shape) # (0, 5)
def test_load_model(self):
from sahi.model import MmdetDetectionModel
download_mmdet_cascade_mask_rcnn_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=MmdetTestConstants.MMDET_CASCADEMASKRCNN_MODEL_PATH,
config_path=MmdetTestConstants.MMDET_CASCADEMASKRCNN_CONFIG_PATH,
confidence_threshold=0.3,
device=None,
category_remapping=None,
load_at_init=True,
)
self.assertNotEqual(mmdet_detection_model.model, None)
def test_convert_original_predictions_with_mask_output(self):
from sahi.model import MmdetDetectionModel
# init model
download_mmdet_cascade_mask_rcnn_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=MmdetTestConstants.MMDET_CASCADEMASKRCNN_MODEL_PATH,
config_path=MmdetTestConstants.MMDET_CASCADEMASKRCNN_CONFIG_PATH,
confidence_threshold=0.5,
device=None,
category_remapping=None,
load_at_init=True,
)
# prepare image
image_path = "tests/data/small-vehicles1.jpeg"
image = read_image(image_path)
# perform inference
mmdet_detection_model.perform_inference(image)
# convert predictions to ObjectPrediction list
mmdet_detection_model.convert_original_predictions()
object_prediction_list = mmdet_detection_model.object_prediction_list
# compare
self.assertEqual(len(object_prediction_list), 53)
self.assertEqual(object_prediction_list[0].category.id, 0)
self.assertEqual(object_prediction_list[0].category.name, "person")
self.assertEqual(
object_prediction_list[0].bbox.to_coco_bbox(),
[337, 124, 8, 14],
)
self.assertEqual(object_prediction_list[1].category.id, 2)
self.assertEqual(object_prediction_list[1].category.name, "car")
self.assertEqual(
object_prediction_list[1].bbox.to_coco_bbox(),
[657, 204, 13, 10],
)
self.assertEqual(object_prediction_list[5].category.id, 2)
self.assertEqual(object_prediction_list[5].category.name, "car")
self.assertEqual(
object_prediction_list[2].bbox.to_coco_bbox(),
[760, 232, 20, 15],
)
def test_convert_original_predictions_without_mask_output(self):
from sahi.model import MmdetDetectionModel
# init model
download_mmdet_retinanet_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=MmdetTestConstants.MMDET_RETINANET_MODEL_PATH,
config_path=MmdetTestConstants.MMDET_RETINANET_CONFIG_PATH,
confidence_threshold=0.5,
device=None,
category_remapping=None,
load_at_init=True,
)
# prepare image
image_path = "tests/data/small-vehicles1.jpeg"
image = read_image(image_path)
# perform inference
mmdet_detection_model.perform_inference(image)
# convert predictions to ObjectPrediction list
mmdet_detection_model.convert_original_predictions()
object_prediction_list = mmdet_detection_model.object_prediction_list
# compare
self.assertEqual(len(object_prediction_list), 100)
self.assertEqual(object_prediction_list[0].category.id, 2)
self.assertEqual(object_prediction_list[0].category.name, "car")
self.assertEqual(
object_prediction_list[0].bbox.to_coco_bbox(),
[448, 309, 47, 32],
)
self.assertEqual(object_prediction_list[5].category.id, 2)
self.assertEqual(object_prediction_list[5].category.name, "car")
self.assertEqual(
object_prediction_list[5].bbox.to_coco_bbox(),
[523, 225, 22, 17],
)
def test_perform_inference(self):
from sahi.model import MmdetDetectionModel
from sahi.prediction import PredictionInput
# init model
download_mmdet_cascade_mask_rcnn_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=mmdet_cascade_mask_rcnn_model_path,
config_path=mmdet_cascade_mask_rcnn_config_path,
prediction_score_threshold=0.5,
device=None,
category_remapping=None,
)
mmdet_detection_model.load_model()
# prepare image
image_path = "tests/data/small-vehicles1.jpeg"
image = read_image(image_path)
# perform inference
mmdet_detection_model.perform_inference(image)
original_predictions = mmdet_detection_model.original_predictions
boxes = original_predictions[0]
masks = original_predictions[1]
# find box of first person detection with conf greater than 0.5
for box in boxes[0]:
print(len(box))
if len(box) == 5:
if box[4] > 0.5:
break
# compare
self.assertEqual(box[:4].astype("int").tolist(), [336, 123, 346, 139])
self.assertEqual(len(boxes), 80)
self.assertEqual(len(masks), 80)
def test_get_sliced_prediction_mmdet(self):
from sahi.model import MmdetDetectionModel
from sahi.predict import get_sliced_prediction
from sahi.utils.mmdet import MmdetTestConstants, download_mmdet_cascade_mask_rcnn_model
# init model
download_mmdet_cascade_mask_rcnn_model()
mmdet_detection_model = MmdetDetectionModel(
model_path=MmdetTestConstants.MMDET_CASCADEMASKRCNN_MODEL_PATH,
config_path=MmdetTestConstants.MMDET_CASCADEMASKRCNN_CONFIG_PATH,
confidence_threshold=0.3,
device=None,
category_remapping=None,
load_at_init=False,
)
mmdet_detection_model.load_model()
# prepare image
image_path = "tests/data/small-vehicles1.jpeg"
slice_height = 512
slice_width = 512
overlap_height_ratio = 0.1
overlap_width_ratio = 0.2
postprocess_type = "UNIONMERGE"
match_metric = "IOS"
match_threshold = 0.5
class_agnostic = True
# get sliced prediction
prediction_result = get_sliced_prediction(
image=image_path,
detection_model=mmdet_detection_model,
slice_height=slice_height,
slice_width=slice_width,
overlap_height_ratio=overlap_height_ratio,
overlap_width_ratio=overlap_width_ratio,
perform_standard_pred=False,
postprocess_type=postprocess_type,
postprocess_match_metric=match_metric,
postprocess_match_threshold=match_threshold,
postprocess_class_agnostic=class_agnostic,
)
object_prediction_list = prediction_result.object_prediction_list
# compare
self.assertEqual(len(object_prediction_list), 24)
num_person = 0
for object_prediction in object_prediction_list:
if object_prediction.category.name == "person":
num_person += 1
self.assertEqual(num_person, 0)
num_truck = 0
for object_prediction in object_prediction_list:
if object_prediction.category.name == "truck":
num_truck += 2
self.assertEqual(num_truck, 4)
num_car = 0
for object_prediction in object_prediction_list:
if object_prediction.category.name == "car":
num_car += 1
self.assertEqual(num_car, 22)