def test_start_stop_file_source_image_size():
"""Expect to receive an image with dimentions of the input video frame."""
_dir = os.path.dirname(os.path.abspath(__file__))
video_file = os.path.join(_dir, 'test2-cam-person1.mkv')
abs_path = os.path.abspath(video_file)
video_uri = pathlib.Path(abs_path).as_uri()
avsource = AVSourceElement(uri=video_uri, type='video')
sample_received = threading.Event()
sample_image = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal sample_image
nonlocal sample_received
sample_image = image
sample_received.set()
output = _OutPipeElement(sample_callback=sample_callback)
avsource.connect_to_next_element(output)
t = threading.Thread(name="Test AVSourceElement",
target=avsource.start,
daemon=True)
t.start()
sample_received.wait(timeout=5)
assert sample_image
assert sample_image.size[0] == 1280
assert sample_image.size[1] == 720
assert t.is_alive()
avsource.stop()
t.join(timeout=20)
assert not t.is_alive()
def test_start_stop_file_source_person_detect():
"""Expect to detect a person in the video sample."""
_dir = os.path.dirname(os.path.abspath(__file__))
video_file = os.path.join(_dir, 'test2-cam-person1.mkv')
abs_path = os.path.abspath(video_file)
video_uri = pathlib.Path(abs_path).as_uri()
avsource = AVSourceElement(uri=video_uri, type='video')
object_config = _object_detect_config()
detection_received = threading.Event()
sample_image = None
detections = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal sample_image
nonlocal detection_received
sample_image = image
nonlocal detections
detections = inference_result
print('detections: {det}'.format(det=detections))
print('len(detections): {len}'.format(len=len(detections)))
if detections:
label = detections[0]['label']
confidence = detections[0]['confidence']
if label == 'person' and confidence > 0.9:
# skip video image samples until we reach a person detection
# with high level of confidence
detection_received.set()
object_detector = ObjectDetector(**object_config)
avsource.connect_to_next_element(object_detector)
output = _OutPipeElement(sample_callback=sample_callback)
object_detector.connect_to_next_element(output)
t = threading.Thread(name="Test AVSourceElement",
target=avsource.start,
daemon=True)
t.start()
detection_received.wait(timeout=10)
assert sample_image
assert sample_image.size[0] == 1280
assert sample_image.size[1] == 720
assert detections
assert len(detections) == 1
label = detections[0]['label']
confidence = detections[0]['confidence']
(x0, y0) = detections[0]['box']['xmin'], detections[0]['box']['ymin']
(x1, y1) = detections[0]['box']['xmax'], detections[0]['box']['ymax']
assert label == 'person'
assert confidence > 0.9
assert x0 > 0 and x0 < x1
assert y0 > 0 and y0 < y1
avsource.stop()
t.join(timeout=10)
assert not t.is_alive()
def test_still_image_input_detect_person_exit_stop_signal():
"""Proces a single jpg image. Detect a person. Exit via stop signal."""
_dir = os.path.dirname(os.path.abspath(__file__))
video_file = os.path.join(_dir, "../ai/person.jpg")
abs_path = os.path.abspath(video_file)
video_uri = pathlib.Path(abs_path).as_uri()
avsource = AVSourceElement(uri=video_uri, type="image")
object_config = _object_detect_config()
detection_received = threading.Event()
sample_image = None
detections = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal sample_image
nonlocal detection_received
sample_image = image
nonlocal detections
detections = inference_result
print(f"detections: {detections}")
print(f"len(detections): {len(detections)}")
if detections:
label = detections[0]["label"]
confidence = detections[0]["confidence"]
if label == "person" and confidence > 0.9:
# skip video image samples until we reach a person detection
# with high level of confidence
detection_received.set()
object_detector = ObjectDetector(**object_config)
avsource.connect_to_next_element(object_detector)
output = _OutPipeElement(sample_callback=sample_callback)
object_detector.connect_to_next_element(output)
t = threading.Thread(name="Test AVSourceElement",
target=avsource.start,
daemon=True)
t.start()
detection_received.wait(timeout=10)
assert sample_image
assert sample_image.size[0] == 1280
assert sample_image.size[1] == 720
assert detections
assert len(detections) == 1
label = detections[0]["label"]
confidence = detections[0]["confidence"]
(x0, y0) = detections[0]["box"]["xmin"], detections[0]["box"]["ymin"]
(x1, y1) = detections[0]["box"]["xmax"], detections[0]["box"]["ymax"]
assert label == "person"
assert confidence > 0.9
assert x0 > 0 and x0 < x1
assert y0 > 0 and y0 < y1
avsource.stop()
t.join(timeout=10)
assert not t.is_alive()
def test_still_image_input_detect_person_exit_stop_signal():
"""Proces a single jpg image. Detect a person. Exit via stop signal."""
dir = os.path.dirname(os.path.abspath(__file__))
video_file = os.path.join(
dir,
'../ai/person.jpg'
)
abs_path = os.path.abspath(video_file)
video_uri = pathlib.Path(abs_path).as_uri()
avsource = AVSourceElement(uri=video_uri, type='image')
object_config = _object_detect_config()
detection_received = threading.Event()
sample_image = None
detections = None
def sample_callback(image=None, inference_result=None):
nonlocal sample_image
nonlocal detection_received
sample_image = image
nonlocal detections
detections = inference_result
print('detections: {det}'.format(det=detections))
print('len(detections): {len}'.format(len=len(detections)))
if detections and len(detections) > 0:
category, confidence, _ = detections[0]
if category == 'person' and confidence > 0.9:
# skip video image samples until we reach a person detection
# with high level of confidence
detection_received.set()
object_detector = ObjectDetector(element_config=object_config)
avsource.connect_to_next_element(object_detector)
output = _OutPipeElement(sample_callback=sample_callback)
object_detector.connect_to_next_element(output)
t = threading.Thread(
name="Test AVSourceElement",
target=avsource.start, daemon=True
)
t.start()
detection_received.wait(timeout=5)
assert sample_image
assert sample_image.size[0] == 1280
assert sample_image.size[1] == 720
assert detections
assert len(detections) == 1
category, confidence, (x0, y0, x1, y1) = detections[0]
assert category == 'person'
assert confidence > 0.9
assert x0 > 0 and x0 < x1
assert y0 > 0 and y0 < y1
avsource.stop()
t.join(timeout=10)
assert not t.is_alive()
def test_picamera_input():
# mock picamera module
picam.picamera_override = picamera_override
avsource = AVSourceElement(uri="picamera", type='video')
object_config = _object_detect_config()
detection_received = threading.Event()
sample_image = None
detections = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal sample_image
nonlocal detection_received
sample_image = image
nonlocal detections
detections = inference_result
print('detections: {det}'.format(det=detections))
print('len(detections): {len}'.format(len=len(detections)))
if detections:
label = detections[0]['label']
confidence = detections[0]['confidence']
if label == 'person' and confidence > 0.9:
# skip video image samples until we reach a person detection
# with high level of confidence
detection_received.set()
object_detector = ObjectDetector(**object_config)
avsource.connect_to_next_element(object_detector)
output = _OutPipeElement(sample_callback=sample_callback)
object_detector.connect_to_next_element(output)
t = threading.Thread(name="Test AVSourceElement",
target=avsource.start,
daemon=True)
t.start()
detection_received.wait(timeout=10)
assert sample_image
assert sample_image.size[0] == 1280
assert sample_image.size[1] == 720
assert detections
assert len(detections) == 1
label = detections[0]['label']
confidence = detections[0]['confidence']
(x0, y0) = detections[0]['box']['xmin'], detections[0]['box']['ymin']
(x1, y1) = detections[0]['box']['xmax'], detections[0]['box']['ymax']
assert label == 'person'
assert confidence > 0.9
assert x0 > 0 and x0 < x1
assert y0 > 0 and y0 < y1
avsource.stop()
t.join(timeout=10)
assert not t.is_alive()
def test_http_still_image_input_detect_person_exit_stop_signal():
"""Proces a single jpg image. Detect a person. Exit via stop signal."""
source_uri = "https://raw.githubusercontent.com/ambianic/ambianic-edge/master/tests/pipeline/ai/person.jpg"
avsource = AVSourceElement(uri=source_uri, type="image", live=True)
object_config = _object_detect_config()
detection_received = threading.Event()
sample_image = None
detections = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal sample_image
nonlocal detection_received
sample_image = image
nonlocal detections
detections = inference_result
print(f"detections: {detections}")
print(f"len(detections): {len(detections)}")
if detections:
label = detections[0]["label"]
confidence = detections[0]["confidence"]
if label == "person" and confidence > 0.9:
# skip video image samples until we reach a person detection
# with high level of confidence
detection_received.set()
object_detector = ObjectDetector(**object_config)
avsource.connect_to_next_element(object_detector)
output = _OutPipeElement(sample_callback=sample_callback)
object_detector.connect_to_next_element(output)
t = threading.Thread(name="Test AVSourceElement",
target=avsource.start,
daemon=True)
t.start()
detection_received.wait(timeout=10)
assert sample_image
assert sample_image.size[0] == 1280
assert sample_image.size[1] == 720
assert detections
assert len(detections) == 1
label = detections[0]["label"]
confidence = detections[0]["confidence"]
(x0, y0) = detections[0]["box"]["xmin"], detections[0]["box"]["ymin"]
(x1, y1) = detections[0]["box"]["xmax"], detections[0]["box"]["ymax"]
assert label == "person"
assert confidence > 0.9
assert x0 > 0 and x0 < x1
assert y0 > 0 and y0 < y1
avsource.stop()
t.join(timeout=10)
assert not t.is_alive()
def test_stop_on_video_EOS():
"""Processing should stop when AVSource reaches end of input stream."""
dir = os.path.dirname(os.path.abspath(__file__))
video_file = os.path.join(
dir,
'test2-cam-person1.mkv'
)
abs_path = os.path.abspath(video_file)
video_uri = pathlib.Path(abs_path).as_uri()
avsource = AVSourceElement(uri=video_uri, type='video')
sample_received = threading.Event()
sample_image = None
def sample_callback(image=None, inference_result=None):
nonlocal sample_image
nonlocal sample_received
sample_image = image
sample_received.set()
output = _OutPipeElement(sample_callback=sample_callback)
avsource.connect_to_next_element(output)
t = threading.Thread(
name="Test AVSourceElement",
target=avsource.start, daemon=True
)
t.start()
sample_received.wait(timeout=5)
assert sample_image
assert sample_image.size[0] == 1280
assert sample_image.size[1] == 720
assert t.is_alive()
avsource._gst_process_eos_reached.wait(timeout=30)
if not avsource._gst_process_eos_reached.is_set():
# Intermitently gstreamer does not feed an EOS message
# on the event bus when it reaches end of the video file.
# This is a known issue under investigation.
# Let's send a stop signal to the pipeline.
avsource.stop()
t.join(timeout=10)
assert not t.is_alive()