Spaces:
Build error
Build error
| from pycocotools.coco import COCO | |
| from mrcnn.cocoeval import COCOeval | |
| from pycocotools import mask as maskUtils | |
| import time | |
| import numpy as np | |
| ############################################################ | |
| # COCO Evaluation | |
| ############################################################ | |
| def build_coco_results(dataset, image_ids, rois, class_ids, scores, masks): | |
| """Arrange resutls to match COCO specs in http://cocodataset.org/#format | |
| """ | |
| # If no results, return an empty list | |
| if rois is None: | |
| return [] | |
| results = [] | |
| for image_id in image_ids: | |
| # Loop through detections | |
| for i in range(rois.shape[0]): | |
| class_id = class_ids[i] | |
| score = scores[i] | |
| bbox = np.around(rois[i], 1) | |
| mask = masks[:, :, i] | |
| result = { | |
| "image_id": image_id, | |
| "category_id": dataset.get_source_class_id(class_id, "crowdai-mapping-challenge"), | |
| "bbox": [bbox[1], bbox[0], bbox[3] - bbox[1], bbox[2] - bbox[0]], | |
| "score": score, | |
| "segmentation": maskUtils.encode(np.asfortranarray(mask)).encode('utf-8') | |
| } | |
| results.append(result) | |
| return results | |
| def evaluate_coco(model, dataset, coco, eval_type="bbox", limit=0, image_ids=None): | |
| """Runs official COCO evaluation. | |
| dataset: A Dataset object with valiadtion data | |
| eval_type: "bbox" or "segm" for bounding box or segmentation evaluation | |
| limit: if not 0, it's the number of images to use for evaluation | |
| """ | |
| # Pick COCO images from the dataset | |
| image_ids = image_ids or dataset.image_ids | |
| # Limit to a subset | |
| if limit: | |
| image_ids = image_ids[:limit] | |
| # Get corresponding COCO image IDs. | |
| coco_image_ids = [dataset.image_info[id]["id"] for id in image_ids] | |
| t_prediction = 0 | |
| t_start = time.time() | |
| results = [] | |
| for i, image_id in enumerate(image_ids): | |
| # Load image | |
| image = dataset.load_image(image_id) | |
| # Run detection | |
| t = time.time() | |
| print("="*100) | |
| print("Image shape ", image.shape) | |
| r = model.detect([image]) | |
| r = r[0] | |
| t_prediction += (time.time() - t) | |
| print("Prediction time : ", (time.time() - t)) | |
| # Convert results to COCO format | |
| image_results = build_coco_results(dataset, coco_image_ids[i:i + 1], | |
| r["rois"], r["class_ids"], | |
| r["scores"], r["masks"]) | |
| print("Number of detections : ", len(r["rois"])) | |
| print("Classes Predicted : ", r["class_ids"]) | |
| print("Scores : ", r["scores"]) | |
| results.extend(image_results) | |
| # Load results. This modifies results with additional attributes. | |
| coco_results = coco.loadRes(results) | |
| # Evaluate | |
| cocoEval = COCOeval(coco, coco_results, eval_type) | |
| cocoEval.params.imgIds = coco_image_ids | |
| cocoEval.evaluate() | |
| cocoEval.accumulate() | |
| ap = cocoEval._summarize(ap=1, iouThr=0.5, areaRng="all", maxDets=100) | |
| ar = cocoEval._summarize(ap=0, areaRng="all", maxDets=100) | |
| print("Precision : ", ap, " Recall : ", ar) | |
| print("Prediction time: {}. Average {}/image".format( | |
| t_prediction, t_prediction / len(image_ids))) | |
| print("Total time: ", time.time() - t_start) | |