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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,203 @@ | ||
| import os.path | ||
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| from detectron2.data import DatasetCatalog, MetadataCatalog | ||
| from detectron2.evaluation import COCOEvaluator, inference_on_dataset, DatasetEvaluator | ||
| import matplotlib.pyplot as plt | ||
| import torch | ||
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| import numpy as np | ||
| from PIL import Image, ImageDraw | ||
| import time | ||
| from .utils import poly_to_mask, plot_mask, voc_ap, cal_one_mean_iou | ||
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| class APEvaluator: | ||
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| def __init__(self): | ||
| self.tp = [] | ||
| self.fp = [] | ||
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| self.precision = [] | ||
| self.recall = [] | ||
| self.ap = [] | ||
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| def add_tp(self): | ||
| self.tp.append(1) | ||
| self.fp.append(0) | ||
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| def add_fp(self): | ||
| self.tp.append(0) | ||
| self.fp.append(1) | ||
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| def eval(self, npos): | ||
| tp = np.array(self.tp) | ||
| fp = np.array(self.fp) | ||
| tp = np.cumsum(tp) | ||
| fp = np.cumsum(fp) | ||
| rec = tp / npos | ||
| prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) | ||
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| ap = voc_ap(rec, prec) | ||
| self.precision = prec | ||
| self.recall = rec | ||
| self.ap = ap | ||
| return ap | ||
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| class MHPDatasetEvaluator(DatasetEvaluator): | ||
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| def __init__(self, dataset_name, cfg, distributed, output_dir=None): | ||
| super().__init__() | ||
| self._cfg = cfg.clone() | ||
| self._dataset_name = dataset_name | ||
| self._distributed = distributed | ||
| self._output_dir = output_dir | ||
| self.dataset_dicts = DatasetCatalog.get(dataset_name) | ||
| self.metadata = MetadataCatalog.get(dataset_name) | ||
| self.num_classes = len(self.metadata.thing_classes) | ||
| self.ovthresh_seg = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] | ||
| # if not os.path.exists(output_dir): | ||
| try: | ||
| os.makedirs(output_dir) | ||
| except OSError: | ||
| pass | ||
|
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| def reset(self): | ||
| self.apr = {} | ||
| self.app = {} | ||
| for i in self.ovthresh_seg: | ||
| self.app[i] = APEvaluator() | ||
| self.apr[i] = APEvaluator() | ||
| self.npos = 0 | ||
| self.npart = 0 | ||
| self.total_time = 0 | ||
| self.delta_time = time.time() | ||
| self.num_images = 0 | ||
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| def process(self, inputs, outputs): | ||
| self.num_images += len(inputs) | ||
| self.total_time += (time.time() - self.delta_time) | ||
| for input, output in zip(inputs, outputs): | ||
| if len(output["instances"]) == 0: | ||
| seg_gt = self.mix_parts_of_instance(self.dataset_dicts[input['image_id']]['annotations'], (100, 100)) | ||
| self.npos += seg_gt.shape[0] | ||
| for i in range(seg_gt.shape[0]): | ||
| self.npart += len(np.unique(seg_gt[i])) | ||
| continue | ||
| w, h = output["instances"].pred_masks.size(1), output["instances"].pred_masks.size(2) | ||
| seg_gt = self.mix_parts_of_instance(self.dataset_dicts[input['image_id']]['annotations'], (w, h)) | ||
| self.npos += seg_gt.size(0) | ||
| for i in range(seg_gt.size(0)): | ||
| self.npart += len(np.unique(seg_gt[i])) | ||
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| seg_pred = output["instances"].pred_masks | ||
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| list_mious = [] | ||
| list_ious = [] | ||
| for i in range(seg_pred.size(0)): | ||
| max_miou = 0 | ||
| max_iou = [] | ||
| max_iou_id = -1 | ||
| a = seg_pred[i].clone().to('cpu') | ||
| for j in range(seg_gt.size(0)): | ||
| b = seg_gt[j].clone().to('cpu') | ||
| b[b >= self.num_classes] = 0 | ||
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| seg_iou = cal_one_mean_iou(a.numpy().astype(np.uint8), b.numpy().astype(np.uint8), 7) | ||
| # print(seg_iou) | ||
| # seg_iou = seg_iou[b.unique().cpu().numpy().astype(np.uint8)] | ||
| # seg_iou[seg_iou == 0] = np.nan | ||
| mean_seg_iou = np.nanmean(seg_iou[0:]) | ||
| # print(mean_seg_iou) | ||
| if mean_seg_iou > max_miou: | ||
| max_miou = mean_seg_iou | ||
| max_iou = seg_iou | ||
| max_iou_id = j | ||
| # print(len(max_iou)) | ||
| list_mious.append({"id": max_iou_id, "iou": max_miou, "iou_list": max_iou}) | ||
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| list_mious = sorted(list_mious, key=lambda x: x["iou"], reverse=True) | ||
| # print([f"{x['id']}:{x['iou']:.3f}" for x in list_mious]) | ||
| for j in self.ovthresh_seg: | ||
| id_list = [] | ||
| for i in list_mious: | ||
| if i['id'] not in id_list: | ||
| # print("aa", len(i['iou_list'])) | ||
| for k in range(len(i['iou_list'])): | ||
| if i['iou_list'][k] == np.nan: | ||
| continue | ||
| if i['iou_list'][k] >= j: | ||
| self.apr[j].add_tp() | ||
| else: | ||
| self.apr[j].add_fp() | ||
| if i["iou"] >= j: | ||
| id_list.append(i['id']) | ||
| self.app[j].add_tp() | ||
| else: | ||
| self.app[j].add_fp() | ||
| else: | ||
| self.app[j].add_fp() | ||
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| # plot_mask(seg_gt, self.dataset_dicts.colormap, 20, 2, os.path.join(self._output_dir, str(input['image_id']) + "_gt.png")) | ||
| # plot_mask(seg_pred, self.dataset_dicts.colormap, 20, 2, os.path.join(self._output_dir, str(input['image_id']) + "_pred.png")) | ||
| # img = input["image"].permute(1, 2, 0).cpu().numpy() | ||
| # img = (img * 255).astype(np.uint8) | ||
| # Image.fromarray(img).save(os.path.join(self._output_dir, str(input['image_id']) + ".png")) | ||
| # plt.show() | ||
| # self.evaluate() | ||
| self.delta_time = time.time() | ||
| # return self.evaluate() | ||
|
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| def mix_parts_of_instance(self, instances, size): | ||
| person_ids = set() | ||
| for i in instances: | ||
| person_ids.add(i['parent_id']) | ||
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| h, w = size | ||
| seg_mask = torch.zeros((len(person_ids), h, w)) | ||
| # print(person_ids) | ||
| for i in person_ids: | ||
| for j in instances: | ||
| if j['parent_id'] == i: | ||
| mask = poly_to_mask(j['segmentation'], w, h) | ||
| mask = torch.from_numpy(mask) | ||
| seg_mask[i] = torch.add(seg_mask[i], mask * (j['category_id'] + 0)) | ||
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| return seg_mask | ||
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| def evaluate(self): | ||
| result = {} | ||
| app = [] | ||
| apr = [] | ||
| for i in self.ovthresh_seg: | ||
|
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| result[f"APr_{i}"] = self.apr[i].eval(self.npart) | ||
| result[f"APp_{i}"] = self.app[i].eval(self.npos) | ||
| print(f"APr_{i} = {result[f'APr_{i}']:.3f}") | ||
| print(f"APp_{i} = {result[f'APp_{i}']:.3f}") | ||
| app.append(result[f"APp_{i}"]) | ||
| apr.append(result[f"APr_{i}"]) | ||
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| # tp = np.array(self.tp[i]) | ||
| # fp = np.array(self.fp[i]) | ||
| # tp = np.cumsum(tp) | ||
| # fp = np.cumsum(fp) | ||
| # rec = tp / self.npos | ||
| # prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) | ||
| # | ||
| # ap = voc_ap(rec, prec) | ||
| # print(f"APp@{i}: {ap:.3f}, {self.npos}, {tp[-1]}, {fp[-1]}") | ||
| # result[f"APp@{i}"] = ap | ||
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| #result["APpvol"] = sum(result.values()) / len(result) | ||
| result["APpvol"] = sum(app) / len(app) | ||
| result["APrvol"] = sum(apr) / len(apr) | ||
| result["total_time"] = self.total_time | ||
| result["fps"] = self.num_images / self.total_time | ||
| # print(f"APpvol: {result['APpvol']:.3f}") | ||
| print(f"total_time: {result['total_time']:.2f}") | ||
| print(f"fps: {result['fps']:.2f}") | ||
| return result |
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