def test_store_positive_detection():
"""The first time a positive sample is processed, it should be saved."""
out_dir = os.path.dirname(os.path.abspath(__file__))
out_dir = os.path.join(
out_dir,
'tmp/'
)
out_dir = os.path.abspath(out_dir)
context = PipelineContext(unique_pipeline_name='test pipeline')
context.data_dir = out_dir
store = _TestSaveDetectionSamples(context=context,
event_log=logging.getLogger())
img = Image.new('RGB', (60, 30), color='red')
detections = [
('person', 0.98, (0, 1, 2, 3))
]
processed_samples = list(store.process_sample(image=img,
thumbnail=img,
inference_result=detections))
assert len(processed_samples) == 1
print(processed_samples)
img_out = processed_samples[0]['image']
assert img_out == img
inf = processed_samples[0]['inference_result']
print(inf)
category, confidence, box = inf[0]
assert category == 'person'
assert confidence == 0.98
assert box[0] == 0 and box[1] == 1 and box[2] == 2 and box[3] == 3
assert store._save_sample_called
assert store._inf_result == detections
assert store._img_path
img_dir = os.path.dirname(os.path.abspath(store._img_path / "../../"))
assert img_dir == out_dir
out_img = Image.open(store._img_path)
print(img_dir)
print(store._img_path)
assert out_img.mode == 'RGB'
assert out_img.size[0] == 60
assert out_img.size[1] == 30
json_dir = os.path.dirname(os.path.abspath(store._json_path / "../../"))
assert json_dir == out_dir
print(json_dir)
print(store._json_path)
with open(store._json_path) as f:
json_inf = json.load(f)
print(json_inf)
img_fname = json_inf['image_file_name']
rel_dir = json_inf['rel_dir']
img_fpath = os.path.join(out_dir, rel_dir, img_fname)
assert img_fpath == str(store._img_path)
json_inf_res = json_inf['inference_result']
assert len(json_inf_res) == 1
json_inf_res = json_inf_res[0]
assert json_inf_res['label'] == 'person'
assert json_inf_res['confidence'] == 0.98
assert json_inf_res['box']['xmin'] == 0
assert json_inf_res['box']['ymin'] == 1
assert json_inf_res['box']['xmax'] == 2
assert json_inf_res['box']['ymax'] == 3
def test_store_negative_detection_no_inference():
"""
Expect store to save the image from an inference without any detection.
"""
out_dir = os.path.dirname(os.path.abspath(__file__))
out_dir = os.path.join(out_dir, 'tmp/')
out_dir = os.path.abspath(out_dir)
out_dir = os.path.abspath(out_dir)
context = PipelineContext(unique_pipeline_name='test pipeline')
context.data_dir = out_dir
store = _TestSaveDetectionSamples(context=context,
event_log=logging.getLogger())
img = Image.new('RGB', (60, 30), color='red')
detections = None
processed_samples = list(
store.process_sample(image=img,
thumbnail=img,
inference_result=detections))
assert len(processed_samples) == 1
print(processed_samples)
img_out = processed_samples[0]['image']
assert img_out == img
inf = processed_samples[0]['inference_result']
print(inf)
assert not inf
assert store._save_sample_called
assert store._inf_result == detections
assert store._img_path
img_dir = os.path.dirname(os.path.abspath(store._img_path / "../../"))
assert img_dir == out_dir
out_img = Image.open(store._img_path)
print(img_dir)
print(store._img_path)
assert out_img.mode == 'RGB'
assert out_img.size[0] == 60
assert out_img.size[1] == 30
json_dir = os.path.dirname(os.path.abspath(store._json_path / "../../"))
assert json_dir == out_dir
print(json_dir)
print(store._json_path)
with open(store._json_path) as f:
json_inf = json.load(f)
print(json_inf)
img_fname = json_inf['image_file_name']
rel_dir = json_inf['rel_dir']
img_fpath = os.path.join(out_dir, rel_dir, img_fname)
assert img_fpath == str(store._img_path)
assert os.path.exists(img_fpath)
json_inf_res = json_inf['inference_result']
assert not json_inf_res
shutil.rmtree(out_dir)
def test_notification_with_attachments():
"""Ensure a positive detection is notified"""
called: Event = Event()
mock_server = HTTPMockServer(called)
# register the mock server endpoint
config.update({
'notifications': {
'test': {
'include_attachments': True,
'providers':
['json://localhost:%s/webhook' % (mock_server.port)],
}
}
})
out_dir = os.path.dirname(os.path.abspath(__file__))
out_dir = os.path.join(out_dir, 'tmp/')
out_dir = os.path.abspath(out_dir)
context = PipelineContext(unique_pipeline_name='test pipeline notify')
context.data_dir = out_dir
notify = {'providers': ['test']}
store = _TestSaveDetectionSamples(
context=context,
event_log=logging.getLogger(),
notify=notify,
)
img = Image.new('RGB', (60, 30), color='red')
detections = [{
'label': 'person',
'confidence': 0.98,
'box': {
'xmin': 0,
'ymin': 1,
'xmax': 2,
'ymax': 3
}
}]
processed_samples = list(
store.process_sample(image=img,
thumbnail=img,
inference_result=detections))
assert len(processed_samples) == 1
mock_server.stop()
assert called.is_set()
def test_process_sample_exception():
"""Exception during processing should not prevent passing the sample on."""
context = PipelineContext(unique_pipeline_name='test pipeline')
context.data_dir = "./tmp/"
store = _TestSaveDetectionSamples2(context=context,
event_log=logging.getLogger())
img = Image.new('RGB', (60, 30), color='red')
detections = [{
'label': 'person',
'confidence': 0.98,
'box': {
'xmin': 0,
'ymin': 1,
'xmax': 2,
'ymax': 3
}
}]
processed_samples = list(
store.process_sample(image=img,
inference_result=detections,
inference_meta=None))
assert store._save_sample_called
assert len(processed_samples) == 1
print(processed_samples)
img_out = processed_samples[0]['image']
assert img_out == img
inf = processed_samples[0]['inference_result']
print(inf)
category = inf[0]['label']
confidence = inf[0]['confidence']
(x0, y0) = inf[0]['box']['xmin'], inf[0]['box']['ymin']
(x1, y1) = inf[0]['box']['xmax'], inf[0]['box']['ymax']
assert category == 'person'
assert confidence == 0.98
assert x0 == 0 and y0 == 1 and x1 == 2 and y1 == 3
def test_process_sample_exception():
"""Exception during processing should not prevent passing the sample on."""
context = PipelineContext(unique_pipeline_name='test pipeline')
context.data_dir = "./tmp/"
store = _TestSaveDetectionSamples2(context=context,
event_log=logging.getLogger())
img = Image.new('RGB', (60, 30), color='red')
detections = [('person', 0.98, (0, 1, 2, 3))]
processed_samples = list(
store.process_sample(image=img,
inference_result=detections,
inference_meta=None))
assert store._save_sample_called
assert len(processed_samples) == 1
print(processed_samples)
img_out = processed_samples[0]['image']
assert img_out == img
inf = processed_samples[0]['inference_result']
print(inf)
category, confidence, box = inf[0]
assert category == 'person'
assert confidence == 0.98
assert box[0] == 0 and box[1] == 1 and box[2] == 2 and box[3] == 3
def test_process_sample():
out_dir = os.path.dirname(os.path.abspath(__file__))
out_dir = os.path.join(out_dir, 'tmp/')
out_dir = os.path.abspath(out_dir)
context = PipelineContext(unique_pipeline_name='test pipeline')
context.data_dir = out_dir
store = _TestSaveDetectionSamples(context=context,
event_log=logging.getLogger())
img = Image.new('RGB', (60, 30), color='red')
detections = [{
'label': 'person',
'confidence': np.float32(0.98),
'box': {
'xmin': np.float32(0.1),
'ymin': np.float32(1.1),
'xmax': np.float32(2.1),
'ymax': np.float32(3.1)
}
}]
processed_samples = list(
store.process_sample(image=img,
thumbnail=img,
inference_result=detections))
assert len(processed_samples) == 1
print(processed_samples)
img_out = processed_samples[0]['image']
assert img_out == img
inf = processed_samples[0]['inference_result']
print(inf)
category = inf[0]['label']
confidence = inf[0]['confidence']
(x0, y0) = inf[0]['box']['xmin'], inf[0]['box']['ymin']
(x1, y1) = inf[0]['box']['xmax'], inf[0]['box']['ymax']
assert category == 'person'
assert isinstance(confidence, np.float32)
assert isinstance(x0, np.float32)
assert isinstance(y0, np.float32)
assert isinstance(x1, np.float32)
assert isinstance(y1, np.float32)
assert store._save_sample_called
assert store._inf_result == detections
assert store._img_path
img_dir = os.path.dirname(os.path.abspath(store._img_path / "../../"))
assert img_dir == out_dir
out_img = Image.open(store._img_path)
print(img_dir)
print(store._img_path)
assert out_img.mode == 'RGB'
assert out_img.size[0] == 60
assert out_img.size[1] == 30
json_dir = os.path.dirname(os.path.abspath(store._json_path / "../../"))
assert json_dir == out_dir
print(json_dir)
print(store._json_path)
with open(store._json_path) as f:
json_inf = json.load(f)
print(json_inf)
img_fname = json_inf['image_file_name']
rel_dir = json_inf['rel_dir']
img_fpath = os.path.join(out_dir, rel_dir, img_fname)
assert img_fpath == str(store._img_path)
assert os.path.exists(img_fpath)
json_inf_res = json_inf['inference_result']
assert len(json_inf_res) == 1
json_inf_res = json_inf_res[0]
assert json_inf_res['label'] == 'person'
assert isinstance(json_inf_res['confidence'], float)
assert isinstance(json_inf_res['box']['xmin'], float)
assert isinstance(json_inf_res['box']['ymin'], float)
assert isinstance(json_inf_res['box']['xmax'], float)
assert isinstance(json_inf_res['box']['ymax'], float)
shutil.rmtree(out_dir)