def test_other(self):
it(1).should_equal(1)
it(0).to_equal(0)
any_of(1, 3).to_equal(1)
all_of([1, 3]).should_be_int()
none_of(1, 3).to_eq(0)
from pyshould.expect import expect, expect_all, expect_any, expect_none
expect(1).to_equal(1)
# Note that matchers without params need the call parens when using this syntax
expect_all(1, 3).to_be_int()
expect_any([1, 3]).to_equal(1)
expect(any_of(1, 3)).to_equal(1)
def test_expect(self):
expect(1).to_equal(1)
expect(1).to_not_equal(0)
def test_expect_quantifiers(self):
expect(all_of(1, 2)).to_be_integer()
expect(any_of([1, 2])).to_eq(1)
def test_expect(self):
expect(1).to_equal(1)
expect(1).to_not_equal(0)
def test_ignore_keywords(self):
it(1).should.be_an_int()
expect(1).to.equal(1)
def test_expect_quantifiers(self):
expect(all_of(1, 2)).to_be_integer()
expect(any_of([1, 2])).to_eq(1)
def test_ignore_keywords(self):
it(1).should.be_an_int()
expect(1).to.equal(1)
def it_accurately_estimates_boxes_and_keypoints_for_a_video(
video_dir, expected_frames_header, expected_frames_processed):
collected_progress = []
def collect_progress(state, meta):
collected_progress.append((state, meta))
# we need a fake celery task to run the detect job
task = __AttrDict({
"request": __AttrDict({"id": "test"}),
"update_state": collect_progress
})
video_dir = fixture_path(video_dir)
video_path = os.path.join(video_dir, "video.mp4")
video_dims = get_video_dims(video_path)
result_meta = detect_boxes_and_keypoints_task(task, video_path,
"anything{}")
expect(result_meta).to_have_the_entry("current", 61)
expect(result_meta).to_have_the_entry("total", 61)
expect(result_meta).to_have_the_entry("status", "success")
expect(result_meta).to_have_the_entry("result", "anythingtest")
expect(result_meta).to_have_the_key("result_file")
expect(result_meta["result_file"]).to_end_with("boxes_and_keypoints.npz")
expect_progress_callbacks(collected_progress, expected_frames_header,
expected_frames_processed)
ground_truth_path = os.path.join(video_dir, "keypoints_ground_truth.npz")
expected_result = np.load(ground_truth_path)
result_npz = np.load(result_meta["result_file"])
result = {k: result_npz[k] for k in result_npz.files}
expect_person_class_box_and_keypoint_detections_for_every_frame(
expected_result, result)
expect_box_detections_for_every_frame_are_5_element_arrays(result)
expect_number_of_keypoints_detections_on_each_frame_matches_the_number_of_box_detections(
result)
expect_keypoints_detections_for_every_frame_are_4x17_ndarrays_for_17_keypoints(
result)
expect_keypoints_detections_for_every_frame_accurate_within_margin_of_error(
expected_result, result, video_dims)
