def run_eval_debug(self, results, save_dir): self.save_results(results, save_dir) save_path = "cache/fail/" coco_dets = self.coco.loadRes('{}/results.json'.format(save_dir)) coco_eval = COCOeval(self.coco, coco_dets, "bbox") p = coco_eval.params p.imgIds = list(np.unique(p.imgIds)) p.maxDets = sorted(p.maxDets) coco_eval.params = p coco_eval._prepare() catIds = [-1] computeIoU = coco_eval.computeIoU coco_eval.ious = {(imgId, catId): coco_eval.computeIoU(imgId, catId) \ for imgId in p.imgIds for catId in catIds} maxDet = p.maxDets[-1] for imgId in p.imgIds: img_path = "/home/user/home/user/Xinyuan/work/CenterNet-1/data/coco/val2017/" fullImgId = str(imgId).zfill(11) img_path = os.path.join(img_path, fullImgId + '.jpg') img = cv2.imread(img_path) for catId in catIds: # for i, areaRng in enumerate(p.areaRng): areaRng = p.areaRng[0] print("areaRng: " + areaRng) result = coco_eval.getImageFailCase(imgId, catId, areaRng, maxDet) gtIds = result['gtIds'] dtIds = result['dtIds'] gtFailIds = gtIds[result['gtfail'][0]] dtFailIds = dtIds[result['dtfail'][0]] gts = [ _ for cId in p.catIds for _ in coco_eval._gts[imgId, cId] ] dts = [ _ for cId in p.catIds for _ in coco_eval._dts[imgId, cId] ] gts = [g['bbox'] for g in gts] dts = [d['bbox'] for d in dts] pdb.set_trace() c = [255, 0, 0] for dt in dts: bbox = np.array(dt, dtype=np.int32) cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), c, 2) c = [0, 255, 0] for gt in gts: bbox = np.array(gt, dtype=np.int32) cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), c, 2) # saveImage save_path = os.path.join(save_path, fullImgId + "_" + str(i)) cv2.imwrite(save_path, img)
def evaluate(iou_type_evaluator: COCOeval) -> Tuple: """ Run per image evaluation on given images and store results (a list of dict) in self.evalImgs :return: None """ p = iou_type_evaluator.params # add backward compatibility if useSegm is specified in params if p.useSegm is not None: p.iouType = "segm" if p.useSegm == 1 else "bbox" print( f"useSegm (deprecated) is not None. Running {p.iouType} evaluation" ) # print('Evaluate annotation type *{}*'.format(p.iouType)) p.imgIds = list(numpy.unique(p.imgIds)) if p.useCats: p.catIds = list(numpy.unique(p.catIds)) p.maxDets = sorted(p.maxDets) iou_type_evaluator.params = p iou_type_evaluator._prepare() # loop through images, area range, max detection number cat_ids = p.catIds if p.useCats else [-1] compute_iou = None if p.iouType == "segm" or p.iouType == "bbox": compute_iou = iou_type_evaluator.computeIoU elif p.iouType == "keypoints": compute_iou = iou_type_evaluator.computeOks iou_type_evaluator.ious = {(imgId, catId): compute_iou(imgId, catId) for imgId in p.imgIds for catId in cat_ids} evaluate_img = iou_type_evaluator.evaluateImg max_det = p.maxDets[-1] eval_imgs = [ evaluate_img(img_id, cat_id, area_rng, max_det) for cat_id in cat_ids for area_rng in p.areaRng for img_id in p.imgIds ] eval_imgs = numpy.asarray( eval_imgs ).reshape( # this is NOT in the pycocotools code, but could be done outside len(cat_ids), len(p.areaRng), len(p.imgIds)) iou_type_evaluator._paramsEval = copy.deepcopy(iou_type_evaluator.params) return p.imgIds, eval_imgs
coco_eval = COCOeval(coco, coco_dets, "bbox") print(dir(coco_eval)) save_path = "src/cache/fail/" coco_dets = coco.loadRes('{}/results.json'.format(save_dir)) coco_eval = COCOeval(coco, coco_dets, "bbox") p = coco_eval.params p.imgIds = list(np.unique(p.imgIds)) p.maxDets = sorted(p.maxDets) coco_eval.params = p coco_eval._prepare() cat_dict = coco.cats # print(cat_dict) catIds = coco_eval.params.catIds computeIoU = coco_eval.computeIoU coco_eval.ious = {(imgId, catId): coco_eval.computeIoU(imgId, catId) \ for imgId in p.imgIds for catId in catIds} maxDet = p.maxDets[-1] for imgId in p.imgIds: img_path = "/home/user/home/user/Xinyuan/work/CenterNet-1/data/coco/val2017/" fullImgId = str(imgId).zfill(12) img_path = os.path.join(img_path, fullImgId + '.jpg') print(img_path) img = cv2.imread(img_path) for catId in catIds: cat = int(catId) txt = cat_dict[cat]['name'] font = cv2.FONT_HERSHEY_SIMPLEX cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0] areaRng = p.areaRng[0] # result = coco_eval.evaluateImg(imgId, catId, areaRng, maxDet)