def accumulate()

in detic/evaluation/oideval.py [0:0]

• 77 lines of code
• 21 McCabe index (conditional complexity)

    def accumulate(self):
        """Accumulate per image evaluation results and store the result in
        self.eval.
        """
        self.logger.info("Accumulating evaluation results.")

        if not self.eval_imgs:
            self.logger.warn("Please run evaluate first.")

        if self.params.use_cats:
            cat_ids = self.params.cat_ids
        else:
            cat_ids = [-1]

        num_thrs = 1
        num_recalls = 1

        num_cats = len(cat_ids)
        num_area_rngs = 1
        num_imgs = len(self.params.img_ids)

        # -1 for absent categories
        precision = -np.ones(
            (num_thrs, num_recalls, num_cats, num_area_rngs)
        )
        recall = -np.ones((num_thrs, num_cats, num_area_rngs))

        # Initialize dt_pointers
        dt_pointers = {}
        for cat_idx in range(num_cats):
            dt_pointers[cat_idx] = {}
            for area_idx in range(num_area_rngs):
                dt_pointers[cat_idx][area_idx] = {}

        # Per category evaluation
        for cat_idx in range(num_cats):
            Nk = cat_idx * num_area_rngs * num_imgs
            for area_idx in range(num_area_rngs):
                Na = area_idx * num_imgs
                E = [
                    self.eval_imgs[Nk + Na + img_idx]
                    for img_idx in range(num_imgs)
                ]
                # Remove elements which are None
                E = [e for e in E if not e is None]
                if len(E) == 0:
                    continue

                dt_scores = np.concatenate([e["dt_scores"] for e in E], axis=0)
                dt_idx = np.argsort(-dt_scores, kind="mergesort")
                dt_scores = dt_scores[dt_idx]
                dt_m = np.concatenate([e["dt_matches"] for e in E], axis=1)[:, dt_idx]
                dt_ig = np.concatenate([e["dt_ignore"] for e in E], axis=1)[:, dt_idx]

                num_gt = sum([e['num_gt'] for e in E])
                if num_gt == 0:
                    continue

                tps = np.logical_and(dt_m, np.logical_not(dt_ig))
                fps = np.logical_and(np.logical_not(dt_m), np.logical_not(dt_ig))
                tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
                fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)

                dt_pointers[cat_idx][area_idx] = {
                    "tps": tps,
                    "fps": fps,
                }

                for iou_thr_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
                    tp = np.array(tp)
                    fp = np.array(fp)
                    num_tp = len(tp)
                    rc = tp / num_gt
                    
                    if num_tp:
                        recall[iou_thr_idx, cat_idx, area_idx] = rc[
                            -1
                        ]
                    else:
                        recall[iou_thr_idx, cat_idx, area_idx] = 0

                    # np.spacing(1) ~= eps
                    pr = tp / (fp + tp + np.spacing(1))
                    pr = pr.tolist()

                    for i in range(num_tp - 1, 0, -1):
                        if pr[i] > pr[i - 1]:
                            pr[i - 1] = pr[i]

                    mAP = compute_average_precision(
                        np.array(pr, np.float).reshape(-1), 
                        np.array(rc, np.float).reshape(-1))
                    precision[iou_thr_idx, :, cat_idx, area_idx] = mAP

        self.eval = {
            "params": self.params,
            "counts": [num_thrs, num_recalls, num_cats, num_area_rngs],
            "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "precision": precision,
            "recall": recall,
            "dt_pointers": dt_pointers,
        }