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)