def render_gen(args):
fps_counter = utils.avg_fps_counter(30)
engines, titles = utils.make_engines(args.model, DetectionEngine)
assert utils.same_input_image_sizes(engines)
engines = itertools.cycle(engines)
engine = next(engines)
labels = utils.load_labels(args.labels) if args.labels else None
filtered_labels = set(
l.strip() for l in args.filter.split(',')) if args.filter else None
get_color = make_get_color(args.color, labels)
draw_overlay = True
yield utils.input_image_size(engine)
output = None
while True:
tensor, layout, command = (yield output)
inference_rate = next(fps_counter)
if draw_overlay:
start = time.monotonic()
# Changed to detect_with_input_tensor. Res is same
# See https://coral.googlesource.com/edgetpuvision/+/refs/heads/4.14.98%5E%21/#F0
objs = engine.detect_with_input_tensor(tensor,
threshold=args.threshold,
top_k=args.top_k)
inference_time = time.monotonic() - start
objs = [convert(obj, labels) for obj in objs]
if labels and filtered_labels:
objs = [obj for obj in objs if obj.label in filtered_labels]
objs = [
obj for obj in objs
if args.min_area <= obj.bbox.area() <= args.max_area
]
if args.print:
print_results(inference_rate, objs)
autoturret_render_artifacts = controller.run(objs)
title = titles[engine]
output = overlay(title, objs, get_color, inference_time,
inference_rate, layout,
autoturret_render_artifacts)
else:
output = None
if command == 'o':
draw_overlay = not draw_overlay
elif command == 'n':
engine = next(engines)
def render_gen(self, args1):
fps_counter = utils.avg_fps_counter(30)
args = self.parser.parse_args()
engines, titles = utils.make_engines(args.model, DetectionEngine)
assert utils.same_input_image_sizes(engines)
engines = itertools.cycle(engines)
engine = next(engines)
labels = utils.load_labels(args.labels) if args.labels else None
filtered_labels = set(
l.strip() for l in args.filter.split(',')) if args.filter else None
get_color = make_get_color(args.color, labels)
draw_overlay = True
yield utils.input_image_size(engine)
output = None
while True:
tensor, layout, command = (yield output)
inference_rate = next(fps_counter)
if draw_overlay:
start = time.monotonic()
objs = engine.detect_with_input_tensor(
tensor, threshold=args.threshold, top_k=args.top_k)
inference_time = time.monotonic() - start
objs = [convert(obj, labels) for obj in objs]
if labels and filtered_labels:
objs = [
obj for obj in objs if obj.label in filtered_labels
]
objs = [
obj for obj in objs
if args.min_area <= obj.bbox.area() <= args.max_area
]
if args.print:
print_results(inference_rate, objs)
title = titles[engine]
output = overlay(title, objs, get_color, inference_time,
inference_rate, layout)
else:
output = None
if command == 'o':
draw_overlay = not draw_overlay
elif command == 'n':
engine = next(engines)
def render_gen(args):
acc = accumulator(size=args.window, top_k=args.top_k)
acc.send(None) # Initialize.
fps_counter = utils.avg_fps_counter(30)
engines, titles = utils.make_engines(args.model, ClassificationEngine)
assert utils.same_input_image_sizes(engines)
engines = itertools.cycle(engines)
engine = next(engines)
labels = utils.load_labels(args.labels)
draw_overlay = True
yield utils.input_image_size(engine)
output = None
while True:
tensor, layout, command = (yield output)
inference_rate = next(fps_counter)
if draw_overlay:
start = time.monotonic()
results = engine.classify_with_input_tensor(
tensor, threshold=args.threshold, top_k=args.top_k)
inference_time = time.monotonic() - start
results = [(labels[i], score) for i, score in results]
results = acc.send(results)
if args.print:
print_results(inference_rate, results)
title = titles[engine]
output = overlay(title, results, inference_time,
inference_rate, layout)
else:
output = None
if command == 'o':
draw_overlay = not draw_overlay
elif command == 'n':
engine = next(engines)
