(self,detect_model,image_glip, object_list,view="left")
| 425 | |
| 426 | |
| 427 | def glip_show(self,detect_model,image_glip, object_list,view="left"): |
| 428 | image_glip=cv2.cvtColor(image_glip, cv2.COLOR_BGR2RGB) |
| 429 | scores, boxes, names = detect_model.inference_on_image(image_glip, object_list) |
| 430 | print(scores, boxes, names) |
| 431 | #scores, boxes, names = self.reduce_boxes(scores, boxes, names) |
| 432 | #box_dict=self.refine_bbox_dict(boxes, names) |
| 433 | # draw output image |
| 434 | plt.figure(figsize=(10, 10)) |
| 435 | # image_rbg = cv2.cvtColor(image_glip, cv2.COLOR_BGR2RGB) |
| 436 | plt.imshow(cv2.cvtColor(image_glip, cv2.COLOR_BGR2RGB)) |
| 437 | show_predictions(scores, boxes, names) |
| 438 | plt.axis('off') |
| 439 | plt.savefig("./test_glip_"+view+".png",bbox_inches="tight", dpi=300, pad_inches=0.0) |
| 440 | # return box_dict |
| 441 | return None |
| 442 | |
| 443 | @torch.no_grad() |
| 444 | def ask_question(self,input_imgs,detect_model): |
no test coverage detected