def test_clear(self): fpobj = FrameProcessor() fpobj.load_annotator(FrameAnnotator()) fpobj.clear() assert fpobj.pipeline == {}
def test_input_pipeline(self): fanno = FrameAnnotator() fpobj = FrameProcessor({"base": fanno}) pline = fpobj.pipeline assert list(pline.keys()) == ["base"] assert issubclass(type(list(pline.values())[0]), FrameAnnotator)
def test_fixed_frames(self): # only grab these frames frames = [0, 3, 17, 18, 21] # create processor pipeline fpobj = FrameProcessor() fpobj.load_annotator(CIElabAnnotator(frames=frames)) fpobj.load_annotator(ClutterAnnotator(frames=frames)) fpobj.load_annotator( EmbedAnnotator(embedding=EmbedFrameKerasResNet50(), frames=frames)) fpobj.load_annotator( FaceAnnotator(detector=FaceDetectDlib(), frames=frames)) fpobj.load_annotator( ObjectAnnotator(detector=ObjectDetectRetinaNet(), frames=frames)) # run over the input, making sure to include a batch (the middle one) # that does not have any data finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=3) # check that the output is the correct size assert fpobj.collect("clutter")["frame"].tolist() == frames assert fpobj.collect("cielab")["frame"].tolist() == frames assert fpobj.collect("embed")["frame"].tolist() == frames assert set(fpobj.collect("face")["frame"]) == set(frames) assert set(fpobj.collect("object")["frame"]) == set(frames)
def test_fprint(self): face_anno = FaceAnnotator( detector=FaceDetectDlib(), embedding=FaceEmbedVgg2(), freq=4 ) fpobj = FrameProcessor() fpobj.load_annotator(face_anno) finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect_all() pa = PeopleAggregator( face_names=["person 1", "person 2"], fprint=obj_out["face"]["embed"][[0, 1]], ) agg = pa.aggregate(obj_out).todf() assert set(agg.keys()) == set( [ "video", "frame", "top", "bottom", "right", "left", "confidence", "person", "person-dist", ] )
def test_process_empty_output(self): fpobj = FrameProcessor() fpobj.load_annotator(FrameAnnotator()) finput = FrameInput("test-data/video-clip.mp4", bsize=4) fpobj.process(finput) assert fpobj.collect("base") == DictFrame()
def test_max_batch(self): fpobj = FrameProcessor() fpobj.load_annotator(DiffAnnotator()) finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=3) obj_diff = fpobj.collect("diff") assert obj_diff.shape[0] == (3 * 8)
def test_process_full_output(self): fpobj = FrameProcessor() fpobj.load_annotator(DiffAnnotator()) finput = FrameInput("test-data/video-clip.mp4", bsize=128) fpobj.process(finput) obj_diff = fpobj.collect("diff") assert obj_diff.shape[0] == (128 * 3)
def test_no_quantiles(self): fpobj = FrameProcessor() fpobj.load_annotator(DiffAnnotator()) finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect("diff") assert set(obj_out.keys()) == set(["video", "frame", "avg_value"]) assert issubclass(type(obj_out["avg_value"]), np.ndarray)
def test_object_detection_cutoff(self): anno = ObjectAnnotator(freq=4, detector=ObjectDetectRetinaNet(cutoff=0.6)) fpobj = FrameProcessor() fpobj.load_annotator(anno) finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect("object") assert obj_out.shape == (12, 8)
def test_histogram_only(self): fpobj = FrameProcessor() fpobj.load_annotator(CIElabAnnotator(num_dominant=0)) finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect("cielab") keys = list(obj_out.keys()) assert set(obj_out.keys()) == set(["video", "frame", "cielab"]) assert issubclass(type(obj_out["cielab"]), np.ndarray) assert obj_out["cielab"].shape == (16, 4096)
def test_clutter(self): fpobj = FrameProcessor() fpobj.load_annotator(ClutterAnnotator()) finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect("clutter") keys = list(obj_out.keys()) assert set(obj_out.keys()) == set(["video", "frame", "clutter"]) assert issubclass(type(obj_out["clutter"]), np.ndarray) assert obj_out["clutter"].shape == (16, 1)
def test_cutoff(self): fpobj = FrameProcessor() fpobj.load_annotator(DiffAnnotator(quantiles=[40])) finput = FrameInput("test-data/video-clip.mp4", bsize=128) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect_all() ca = CutAggregator(cut_vals={"h40": 0.2, "q40": 3}) agg = ca.aggregate(obj_out) assert set(agg.keys()) == set(["video", "frame_start", "frame_end"])
def test_embed_resnet(self, setup_tensorflow): anno = EmbedAnnotator(freq=4, embedding=EmbedFrameKerasResNet50()) fpobj = FrameProcessor() fpobj.load_annotator(anno) finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect("embed") assert set(obj_out.keys()) == set(["frame", "video", "embed"]) assert issubclass(type(obj_out["embed"]), np.ndarray) assert obj_out["embed"].shape == (4, 2048)
def test_collect_all(self): fpobj = FrameProcessor() fpobj.load_annotator(FrameAnnotator()) fpobj.load_annotator(DiffAnnotator()) finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=2) output = fpobj.collect_all() assert output["base"] == DictFrame() assert output["diff"].shape[0] == (2 * 8)
def test_cutoff_empty(self): fpobj = FrameProcessor() fpobj.load_annotator(DiffAnnotator(quantiles=[40])) finput = FrameInput("test-data/video-clip.mp4", bsize=128) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect_all() ca = CutAggregator() agg = ca.aggregate(obj_out) assert agg["frame_start"] == list(range(256))
def test_png_resize(self): dname = tempfile.mkdtemp() # creates directory fpobj = FrameProcessor() fpobj.load_annotator(PngAnnotator(output_dir=dname, size=(32, 64))) finput = FrameInput("test-data/video-clip.mp4", bsize=4) fpobj.process(finput, max_batch=2) expected_files = set(["frame-{0:06d}.png".format(x) for x in range(8)]) obj_out = fpobj.collect("png") assert obj_out == DictFrame() assert set(os.listdir(dname)) == expected_files
def test_cutoff_ignore(self): fpobj = FrameProcessor() fpobj.load_annotator(DiffAnnotator(quantiles=[40])) finput = FrameInput("test-data/video-clip.mp4", bsize=128) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect_all() ca = CutAggregator( cut_vals={"h40": 0.2, "q40": 3}, ignore_vals={"avg_value": 70} ) agg = ca.aggregate(obj_out) assert agg == DictFrame()
def test_quantiles(self): fpobj = FrameProcessor() fpobj.load_annotator(DiffAnnotator(quantiles=[40, 50])) finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect("diff") keys = list(obj_out.keys()) assert issubclass(type(obj_out["q40"]), np.ndarray) assert issubclass(type(obj_out["q50"]), np.ndarray) assert issubclass(type(obj_out["h40"]), np.ndarray) assert issubclass(type(obj_out["h50"]), np.ndarray)
def test_meta_output_video(self): fpobj = FrameProcessor() fpobj.load_annotator(MetaAnnotator()) finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect("meta") expected_keys = ["width", "type", "vname", "fps", "height", "frames"] assert set(obj_out.keys()) == set(expected_keys) assert obj_out["width"] == [708] assert obj_out["height"] == [480] assert obj_out["type"] == ["video"] assert obj_out["vname"] == ["video-clip.mp4"] assert obj_out["fps"] == [29.97002997002997] assert obj_out["frames"] == [379]
def test_load_annotators(self): fpobj = FrameProcessor() fpobj.load_annotator(FrameAnnotator()) pline = fpobj.pipeline assert len(pline) == 1 assert list(pline.keys()) == ["base"] assert issubclass(type(list(pline.values())[0]), FrameAnnotator) fobj = FrameAnnotator() fobj.name = "other" fpobj.load_annotator(fobj) pline = fpobj.pipeline assert len(pline) == 2 assert list(pline.keys()) == ["base", "other"]
def test_meta_output_images(self): fpobj = FrameProcessor() fpobj.load_annotator(MetaAnnotator()) iobj = ImageInput(input_paths=[ "test-data/img/frame-000076.png", "test-data/img/frame-000506.png", ]) fpobj.process(iobj, max_batch=2) obj_out = fpobj.collect("meta") expected_keys = ["width", "type", "vname", "height"] assert set(obj_out.keys()) == set(expected_keys) assert obj_out["width"] == [-1] assert obj_out["height"] == [-1] assert obj_out["type"] == ["image"] assert obj_out["vname"] == [""]
def test_face_detector_cutoff_mtcnn(self): anno = FaceAnnotator(detector=FaceDetectMtcnn(cutoff=0.99997), freq=4) fpobj = FrameProcessor() fpobj.load_annotator(anno) finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect("face") expected_keys = [ "video", "frame", "confidence", "top", "bottom", "left", "right", ] assert set(obj_out.keys()) == set(expected_keys) assert obj_out.shape == (4, 7)
def test_object_detection(self): anno = ObjectAnnotator(freq=4, detector=ObjectDetectRetinaNet()) fpobj = FrameProcessor() fpobj.load_annotator(anno) finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect("object") expected_keys = [ "video", "frame", "score", "top", "bottom", "left", "right", "class", ] assert set(obj_out.keys()) == set(expected_keys) assert obj_out.shape == (17, 8)
def test_png_image(self): dname = tempfile.mkdtemp() # creates directory fpobj = FrameProcessor() fpobj.load_annotator(PngAnnotator(output_dir=dname)) iobj = ImageInput(input_paths=[ "test-data/img/frame-000076.png", "test-data/img/frame-000506.png", ]) fpobj.process(iobj, max_batch=2) # assert that all of the images in the input exist in output expected_files = set(["frame-000076.png", "frame-000506.png"]) obj_out = fpobj.collect("png") assert obj_out == DictFrame() assert set(os.listdir(dname)) == expected_files # make sure images are the same img1 = cv2.imread(os.path.join("test-data/img/frame-000076.png")) img2 = cv2.imread(os.path.join(dname, "frame-000076.png")) assert np.all(img1 == img2)
def test_face_vgg2_embed(self): anno = FaceAnnotator(detector=FaceDetectDlib(), embedding=FaceEmbedVgg2(), freq=4) fpobj = FrameProcessor() fpobj.load_annotator(anno) finput = FrameInput("test-data/video-clip.mp4", bsize=8) fpobj.process(finput, max_batch=2) obj_out = fpobj.collect("face") expected_keys = [ "video", "frame", "confidence", "top", "bottom", "left", "right", "embed", ] assert set(obj_out.keys()) == set(expected_keys) assert issubclass(type(obj_out["embed"]), np.ndarray) assert obj_out["embed"].shape == (8, 2048)
def main(argv): try: opts, args = getopt.getopt( argv, "hf:q:c:h:", ["file=", "quantiles=", "cutvals=", "histdiff="]) except getopt.GetoptError: print( 'dvt_to_sat.py -f <filename> -q <quantiles> -c <cutvals> -h <histdiff>' ) sys.exit(2) filename = '' quantiles = 40 cutvals = 3 histdiff = 0 for opt, arg in opts: if opt == '-h': print( 'dvt_to_sat.py -f <filename> -q <quantiles> -c <cutvals> -h <histdiff>' ) sys.exit() elif opt in ("-f", "--file"): filename = arg elif opt in ("-q", "--quantiles"): quantiles = arg elif opt in ("-c", "--cutvals"): cutvals = arg elif opt in ("-h", "--histdiff"): cutvals = arg if (filename == ''): print('missing param: filename is required') sys.exit(2) basename = os.path.basename(filename) logging.basicConfig(level='INFO') finput = FrameInput(filename, bsize=128) fpobj = FrameProcessor() fpobj.load_annotator(MetaAnnotator()) fpobj.load_annotator(DiffAnnotator(quantiles=[quantiles])) fpobj.process(finput) #fpobj.process(finput, max_batch=5) obj = fpobj.collect_all() meta = obj['meta'].todf() obj['diff'].todf().head() cagg = CutAggregator(cut_vals={ 'h40': histdiff, 'q' + str(quantiles): cutvals }) fps = meta['fps'][0] print(fps) df1 = cagg.aggregate(obj).todf() df1['seconds_start'] = df1['frame_start'] / fps df1['seconds_end'] = df1['frame_end'] / fps out = df1.to_csv() f = open(basename + ".csv", "w") f.write(out) f.close()
def test_input_pipeline_empty(self): fpobj = FrameProcessor() assert fpobj.pipeline == {}