def predict_on_json(predictions, annotation_path, classes, show=False, conf_threshold=None, dump_output_video=False,
path_to_output_video='output.avi', width=640, height=480, fps=30):
annotation_generator, _ = ObjectDetectorJson.json_iterator(annotation_path, classes)
annotation_data = [pickle.loads(x) for x in annotation_generator()]
output = []
if dump_output_video:
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(path_to_output_video, fourcc, fps, (int(width), int(height)))
for i, pred in enumerate(predictions):
annotation = annotation_data[i]
image_size = annotation['image_size']
img_path = annotation['image']
img_id = annotation['image_id']
det = process_image(pred[:, 1:], image_size, img_id, conf_threshold, classes)
output.extend(det)
frame = cv2.imread(img_path)
frame = cv2.resize(frame, tuple(image_size))
img = draw_detections(frame, det)
if show:
cv2.imshow('detections', img)
key = cv2.waitKey(10)
if key == 27:
break
if dump_output_video:
img_resized = cv2.resize(img, (width, height))
out.write(img_resized)
if dump_output_video:
out.release()
return output
def sample_data(json_path, num_samples, input_shape, classes, seed=666):
if num_samples == 0:
return None
data, _ = ObjectDetectorJson.json_iterator(json_path, classes)
data = [x for x in data()]
# data = ObjectDetectorJson.convert_coco_to_toolbox_format(COCO(json_path), classes)
ObjectDetectorJson.init_cache(json_path, cache_type='NONE', classes=classes)
rng = random.Random(seed)
selected_items = rng.sample(range(len(data)), num_samples)
_, transform_param = MobileNetSSD.create_transform_parameters(*input_shape[:2])
transformer = AnnotatedDataTransformer(transform_param, is_training=False)
transform_fn = lambda value: ObjectDetectorJson.transform_fn(value, transformer, add_original_image=True)
return [transform_fn(data[i]) for i in selected_items]
def predict_on_json(predictions,
annotation_path,
classes,
show=False,
conf_threshold=None,
dump_output_video=False,
path_to_output_video='output.avi',
width=640,
height=480,
fps=30):
annotation_generator, _ = ObjectDetectorJson.json_iterator(
annotation_path, classes)
annotation_data = [pickle.loads(x) for x in annotation_generator()]
output = []
if dump_output_video:
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(path_to_output_video, fourcc, fps,
(int(width), int(height)))
for i, pred in enumerate(predictions):
annotation = annotation_data[i]
image_size = annotation['image_size']
img_path = annotation['image']
img_id = annotation['image_id']
det = process_image(pred[:, 1:], image_size, img_id, conf_threshold,
classes)
output.extend(det)
frame = cv2.imread(img_path)
frame = cv2.resize(frame, tuple(image_size))
img = draw_detections(frame, det)
if show:
cv2.imshow('detections', img)
key = cv2.waitKey(10)
if key == 27:
break
if dump_output_video:
img_resized = cv2.resize(img, (width, height))
out.write(img_resized)
if dump_output_video:
out.release()
return output
