def predict(pred_func, input_file):
img = cv2.imread(input_file, cv2.IMREAD_COLOR)
results = detect_one_image(img, pred_func)
final = draw_final_outputs(
img, results) # image contain boxes,labels and scores
viz = np.concatenate((img, final), axis=1)
tpviz.interactive_imshow(viz)
def _build_coco_predictor(self, idx):
graph_func = self.trainer.get_predictor(self._in_names,
self._out_names,
device=idx)
return lambda img: detect_one_image(img, graph_func)
def detect(self, img, rgb=True):
# Convert to bgr if necessary
if rgb:
img = self.rgb_to_bgr(img)
return detect_one_image(img, self.pred)
def offline_evaluate(pred_func, output_file):
df = get_eval_dataflow()
all_results = eval_coco(df, lambda img: detect_one_image(img, pred_func))
with open(output_file, 'w') as f:
json.dump(all_results, f)
print_evaluation_scores(output_file)