def test_fall_detection_case_8():
"""Expect to not detect a fall"""
config = _fall_detect_config()
result = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
fall_detector = FallDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
fall_detector.connect_to_next_element(output)
# No person in a frame
img_1 = _get_image(file_name="fall_img_6.png")
# The frame represents a person who is in a standing position.
img_2 = _get_image(file_name="fall_img_7.png")
fall_detector.receive_next_sample(image=img_1)
# set min time to a small number to speed up testing
fall_detector.min_time_between_frames = 0.01
time.sleep(fall_detector.min_time_between_frames)
fall_detector.receive_next_sample(image=img_2)
assert not result
def test_fall_detection_case_1():
"""Expected to not detect a fall as key-points are not detected."""
config = _fall_detect_config()
result = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
fall_detector = FallDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
fall_detector.connect_to_next_element(output)
# The frame represents a person who is in a standing position.
img_1 = _get_image(file_name="fall_img_1.png")
# The frame represents a person completely falls.
img_2 = _get_image(file_name="fall_img_3.png")
fall_detector.receive_next_sample(image=img_1)
fall_detector.min_time_between_frames = 0.01
time.sleep(fall_detector.min_time_between_frames)
fall_detector.receive_next_sample(image=img_2)
assert not result
def test_fall_detection_case_2_1():
"""Expected to not detect a fall even though key-points are detected
and the angle criteria is met. However the time distance between
frames is too short."""
config = _fall_detect_config()
result = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
fall_detector = FallDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
fall_detector.connect_to_next_element(output)
# The frame represents a person who is in a standing position.
img_1 = _get_image(file_name="fall_img_1.png")
# The frame represents a person falls.
img_2 = _get_image(file_name="fall_img_2.png")
start_time = time.monotonic()
fall_detector.receive_next_sample(image=img_1)
end_time = time.monotonic()
safe_min = end_time - start_time + 1
# set min time to a sufficiently big number to ensure test passes
# on slow environments
# the goal is to simulate two frames that are too close in time
# to be considered for a fall detection sequence
fall_detector.min_time_between_frames = safe_min
fall_detector.receive_next_sample(image=img_2)
assert not result
def test_fall_detection_case_5():
"""Expected to not detect a fall even the angle criteria is met
because image 2 is standing up rather than fall"""
config = _fall_detect_config()
result = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
fall_detector = FallDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
fall_detector.connect_to_next_element(output)
# The frame represents a person falls.
img_1 = _get_image(file_name="fall_img_2.png")
# The frame represents a person who is in a standing position.
img_2 = _get_image(file_name="fall_img_1.png")
fall_detector.receive_next_sample(image=img_1)
fall_detector.min_time_between_frames = 0.01
time.sleep(fall_detector.min_time_between_frames)
fall_detector.receive_next_sample(image=img_2)
assert not result
def test_bad_sample_good_sample():
"""One bad sample should not prevent good samples from being processed."""
config = _fall_detect_config()
result = 'nothing passed to me'
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
object_detector = ObjectDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
object_detector.connect_to_next_element(output)
# bad sample
object_detector.receive_next_sample(image=None)
assert result == 'nothing passed to me'
# good sample
fall_detector = FallDetector(**config)
fall_detector.connect_to_next_element(output)
img_1 = _get_image(file_name='fall_img_1.png')
img_2 = _get_image(file_name='fall_img_2.png')
fall_detector.receive_next_sample(image=img_1)
fall_detector.min_time_between_frames = 0.01
time.sleep(fall_detector.min_time_between_frames)
fall_detector.receive_next_sample(image=img_2)
assert result
assert len(result) == 1
category, confidence, box, angle = result[0]
assert box # Add this line to avoid 'Unused local variable'
assert category == 'FALL'
assert confidence > 0.7
assert angle > 60
def test_fall_detection_case_3():
"""Expect to detect a fall as key-points are detected by rotating the image."""
config = _fall_detect_config()
result = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
fall_detector = FallDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
fall_detector.connect_to_next_element(output)
img_1 = _get_image(file_name='fall_img_11.png')
img_2 = _get_image(file_name='fall_img_12.png')
fall_detector.receive_next_sample(image=img_1)
# set min time to a small number to speed up testing
fall_detector.min_time_between_frames = 0.01
time.sleep(fall_detector.min_time_between_frames)
fall_detector.receive_next_sample(image=img_2)
assert result
assert len(result) == 1
category, confidence, box, angle = result[0]
assert box # Add this line to avoid 'Unused local variable'
assert category == 'FALL'
assert confidence > 0.3
assert angle > 60
def test_fall_detection_case_2_2():
"""Expected to detect a fall because key-points are detected, the angle criteria is met and the time distance between frames is not too short."""
config = _fall_detect_config()
result = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
fall_detector = FallDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
fall_detector.connect_to_next_element(output)
img_1 = _get_image(file_name='fall_img_1.png')
img_2 = _get_image(file_name='fall_img_2.png')
fall_detector.receive_next_sample(image=img_1)
fall_detector.min_time_between_frames = 0.01
time.sleep(fall_detector.min_time_between_frames)
fall_detector.receive_next_sample(image=img_2)
assert result
assert len(result) == 1
category, confidence, box, angle = result[0]
assert box # Add this line to avoid 'Unused local variable'
assert category == 'FALL'
assert confidence > 0.7
assert angle > 60
def test_fall_detection_2_frame_back_case_1():
"""
Expected to detect a fall using frame[t] and frame[t-1].
frame[t-2] : A person is in standing position.
frame[t-1] : A person is almost in standing position as he is walking.
frame[t] : A person is fall down.
"""
config = _fall_detect_config()
result = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
fall_detector = FallDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
fall_detector.connect_to_next_element(output)
# A frame at t-2 timestamp when person is in standing position.
img_1 = _get_image(file_name="fall_img_1.png")
# A frame at t-1 timestamp when person is almost in standing position \
# as he is walking.
img_2 = _get_image(file_name="fall_img_1_1.png")
# A frame at t timestamp when person falls down.
img_3 = _get_image(file_name="fall_img_2.png")
fall_detector.min_time_between_frames = 0.01
fall_detector.receive_next_sample(image=img_1)
time.sleep(fall_detector.min_time_between_frames)
fall_detector.receive_next_sample(image=img_2)
time.sleep(fall_detector.min_time_between_frames)
assert not result
fall_detector.receive_next_sample(image=img_3)
assert result
assert len(result) == 1
category = result[0]["label"]
confidence = result[0]["confidence"]
angle = result[0]["leaning_angle"]
keypoint_corr = result[0]["keypoint_corr"]
assert keypoint_corr
assert category == "FALL"
assert confidence > 0.7
assert angle > 60
def test_fall_detection_2_frame_back_case_2():
"""
Expected to detect a fall using frame[t] and frame[t-2].
frame[t-2] : A person is in standing position.
frame[t-1] : A person is mid-way of fall.
frame[t] : A person is fall down.
"""
config = _fall_detect_config()
result = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
fall_detector = FallDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
fall_detector.connect_to_next_element(output)
# A frame at t-2 timestamp when person is in standing position.
img_1 = _get_image(file_name='fall_img_1.png')
# A frame at t-1 timestamp when person is mid-way of fall.
img_2 = _get_image(file_name='fall_img_2_2.png')
# A frame at t timestamp when person falls down.
img_3 = _get_image(file_name='fall_img_2.png')
fall_detector.min_time_between_frames = 0.01
fall_detector.max_time_between_frames = 15
fall_detector.receive_next_sample(image=img_1)
time.sleep(fall_detector.min_time_between_frames)
fall_detector.receive_next_sample(image=img_2)
time.sleep(fall_detector.min_time_between_frames)
assert not result
fall_detector.receive_next_sample(image=img_3)
assert result
assert len(result) == 1
category = result[0]['label']
confidence = result[0]['confidence']
angle = result[0]['leaning_angle']
keypoint_corr = result[0]['keypoint_corr']
assert keypoint_corr
assert category == 'FALL'
assert confidence > 0.7
assert angle > 60
def test_bad_sample_good_sample():
"""One bad sample should not prevent good samples from being processed."""
config = _fall_detect_config()
result = "nothing passed to me"
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
object_detector = ObjectDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
object_detector.connect_to_next_element(output)
# bad sample
object_detector.receive_next_sample(image=None)
assert result == "nothing passed to me"
# good sample
fall_detector = FallDetector(**config)
fall_detector.connect_to_next_element(output)
# The frame represents a person who is in a standing position.
img_1 = _get_image(file_name="fall_img_1.png")
# The frame represents a person falls.
img_2 = _get_image(file_name="fall_img_2.png")
fall_detector.receive_next_sample(image=img_1)
fall_detector.min_time_between_frames = 0.01
time.sleep(fall_detector.min_time_between_frames)
fall_detector.receive_next_sample(image=img_2)
assert result
assert len(result) == 1
category = result[0]["label"]
confidence = result[0]["confidence"]
angle = result[0]["leaning_angle"]
keypoint_corr = result[0]["keypoint_corr"]
assert keypoint_corr
assert category == "FALL"
assert confidence > 0.7
assert angle > 60
def test_fall_detection_2_frame_back_case_3():
"""
Expected to not detect a fall using frame[t],frame[t-1] and frame[t-2].
frame[t-2] : A person is in walking postion.
frame[t-1] : A person is in walking postion.
frame[t] : A person is slight in lean postion but no fall.
"""
config = _fall_detect_config()
result = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
fall_detector = FallDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
fall_detector.connect_to_next_element(output)
# A frame at t-2 timestamp when person is in walking postion.
img_1 = _get_image(file_name="fall_img_15.png")
# A frame at t-1 timestamp when person is in walking postion.
img_2 = _get_image(file_name="fall_img_16.png")
# A frame at t timestamp when person is slight in lean postion but no fall.
img_3 = _get_image(file_name="fall_img_17.png")
fall_detector.min_time_between_frames = 0.01
fall_detector.receive_next_sample(image=img_1)
time.sleep(fall_detector.min_time_between_frames)
fall_detector.receive_next_sample(image=img_2)
time.sleep(fall_detector.min_time_between_frames)
assert not result
fall_detector.receive_next_sample(image=img_3)
assert not result
def test_fall_detection_case_3_2():
"""Expect to detect a fall as key-points are detected
by rotating the image counter clockwise."""
config = _fall_detect_config()
result = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
fall_detector = FallDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
fall_detector.connect_to_next_element(output)
# The frame represents a person who is in a standing position.
img_1 = _get_image(file_name="fall_img_11_flip.png")
# The frame represents a person completely falls.
img_2 = _get_image(file_name="fall_img_12_flip.png")
fall_detector.receive_next_sample(image=img_1)
# set min time to a small number to speed up testing
fall_detector.min_time_between_frames = 0.01
time.sleep(fall_detector.min_time_between_frames)
fall_detector.receive_next_sample(image=img_2)
assert result
assert len(result) == 1
category = result[0]["label"]
confidence = result[0]["confidence"]
angle = result[0]["leaning_angle"]
keypoint_corr = result[0]["keypoint_corr"]
assert keypoint_corr
assert category == "FALL"
assert confidence > 0.3
assert angle > 60
def test_background_image():
"""Expect to not detect anything interesting in a background image."""
config = _fall_detect_config()
result = None
def sample_callback(image=None,
thumbnail=None,
inference_result=None,
**kwargs):
nonlocal result
result = image is not None and thumbnail is not None and not inference_result
fall_detector = FallDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
fall_detector.connect_to_next_element(output)
img = _get_image(file_name="background.jpg")
fall_detector.receive_next_sample(image=img)
fall_detector.min_time_between_frames = 0.01
time.sleep(fall_detector.min_time_between_frames)
img = _get_image(file_name="background.jpg")
fall_detector.receive_next_sample(image=img)
assert result is True
def test_fall_detection_case_2_2():
"""Expected to detect a fall because key-points are detected,
the angle criteria is met and the time distance between
frames is not too short."""
config = _fall_detect_config()
result = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
fall_detector = FallDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
fall_detector.connect_to_next_element(output)
# The frame represents a person who is in a standing position.
img_1 = _get_image(file_name="fall_img_1.png")
# The frame represents a person falls.
img_2 = _get_image(file_name="fall_img_2.png")
fall_detector.receive_next_sample(image=img_1)
fall_detector.min_time_between_frames = 0.01
time.sleep(fall_detector.min_time_between_frames)
fall_detector.receive_next_sample(image=img_2)
assert result
assert len(result) == 1
category = result[0]["label"]
confidence = result[0]["confidence"]
angle = result[0]["leaning_angle"]
keypoint_corr = result[0]["keypoint_corr"]
assert keypoint_corr
assert category == "FALL"
assert confidence > 0.7
assert angle > 60
def test_fall_detection_case_4():
"""No Fall"""
config = _fall_detect_config()
result = None
def sample_callback(image=None, inference_result=None, **kwargs):
nonlocal result
result = inference_result
fall_detector = FallDetector(**config)
output = _OutPipeElement(sample_callback=sample_callback)
fall_detector.connect_to_next_element(output)
img_1 = _get_image(file_name='fall_img_1.png')
img_2 = _get_image(file_name='fall_img_4.png')
fall_detector.receive_next_sample(image=img_1)
fall_detector.min_time_between_frames = 0.01
time.sleep(fall_detector.min_time_between_frames)
fall_detector.receive_next_sample(image=img_2)
assert not result