first commit

ppocr/utils/e2e_metric/Deteval.py

@@ -0,0 +1,852 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import numpy as np
+import scipy.io as io
+
+from ppocr.utils.utility import check_install
+
+from ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area
+
+
+def get_socre_A(gt_dir, pred_dict):
+    allInputs = 1
+
+    def input_reading_mod(pred_dict):
+        """This helper reads input from txt files"""
+        det = []
+        n = len(pred_dict)
+        for i in range(n):
+            points = pred_dict[i]["points"]
+            text = pred_dict[i]["texts"]
+            point = ",".join(
+                map(
+                    str,
+                    points.reshape(
+                        -1,
+                    ),
+                )
+            )
+            det.append([point, text])
+        return det
+
+    def gt_reading_mod(gt_dict):
+        """This helper reads groundtruths from mat files"""
+        gt = []
+        n = len(gt_dict)
+        for i in range(n):
+            points = gt_dict[i]["points"].tolist()
+            h = len(points)
+            text = gt_dict[i]["text"]
+            xx = [
+                np.array(["x:"], dtype="<U2"),
+                0,
+                np.array(["y:"], dtype="<U2"),
+                0,
+                np.array(["#"], dtype="<U1"),
+                np.array(["#"], dtype="<U1"),
+            ]
+            t_x, t_y = [], []
+            for j in range(h):
+                t_x.append(points[j][0])
+                t_y.append(points[j][1])
+            xx[1] = np.array([t_x], dtype="int16")
+            xx[3] = np.array([t_y], dtype="int16")
+            if text != "":
+                xx[4] = np.array([text], dtype="U{}".format(len(text)))
+                xx[5] = np.array(["c"], dtype="<U1")
+            gt.append(xx)
+        return gt
+
+    def detection_filtering(detections, groundtruths, threshold=0.5):
+        for gt_id, gt in enumerate(groundtruths):
+            if (gt[5] == "#") and (gt[1].shape[1] > 1):
+                gt_x = list(map(int, np.squeeze(gt[1])))
+                gt_y = list(map(int, np.squeeze(gt[3])))
+                for det_id, detection in enumerate(detections):
+                    detection_orig = detection
+                    detection = [float(x) for x in detection[0].split(",")]
+                    detection = list(map(int, detection))
+                    det_x = detection[0::2]
+                    det_y = detection[1::2]
+                    det_gt_iou = iod(det_x, det_y, gt_x, gt_y)
+                    if det_gt_iou > threshold:
+                        detections[det_id] = []
+
+                detections[:] = [item for item in detections if item != []]
+        return detections
+
+    def sigma_calculation(det_x, det_y, gt_x, gt_y):
+        """
+        sigma = inter_area / gt_area
+        """
+        return np.round(
+            (area_of_intersection(det_x, det_y, gt_x, gt_y) / area(gt_x, gt_y)), 2
+        )
+
+    def tau_calculation(det_x, det_y, gt_x, gt_y):
+        if area(det_x, det_y) == 0.0:
+            return 0
+        return np.round(
+            (area_of_intersection(det_x, det_y, gt_x, gt_y) / area(det_x, det_y)), 2
+        )
+
+    ##############################Initialization###################################
+    # global_sigma = []
+    # global_tau = []
+    # global_pred_str = []
+    # global_gt_str = []
+    ###############################################################################
+
+    for input_id in range(allInputs):
+        if (
+            (input_id != ".DS_Store")
+            and (input_id != "Pascal_result.txt")
+            and (input_id != "Pascal_result_curved.txt")
+            and (input_id != "Pascal_result_non_curved.txt")
+            and (input_id != "Deteval_result.txt")
+            and (input_id != "Deteval_result_curved.txt")
+            and (input_id != "Deteval_result_non_curved.txt")
+        ):
+            detections = input_reading_mod(pred_dict)
+            groundtruths = gt_reading_mod(gt_dir)
+            detections = detection_filtering(
+                detections, groundtruths
+            )  # filters detections overlapping with DC area
+            dc_id = []
+            for i in range(len(groundtruths)):
+                if groundtruths[i][5] == "#":
+                    dc_id.append(i)
+            cnt = 0
+            for a in dc_id:
+                num = a - cnt
+                del groundtruths[num]
+                cnt += 1
+
+            local_sigma_table = np.zeros((len(groundtruths), len(detections)))
+            local_tau_table = np.zeros((len(groundtruths), len(detections)))
+            local_pred_str = {}
+            local_gt_str = {}
+
+            for gt_id, gt in enumerate(groundtruths):
+                if len(detections) > 0:
+                    for det_id, detection in enumerate(detections):
+                        detection_orig = detection
+                        detection = [float(x) for x in detection[0].split(",")]
+                        detection = list(map(int, detection))
+                        pred_seq_str = detection_orig[1].strip()
+                        det_x = detection[0::2]
+                        det_y = detection[1::2]
+                        gt_x = list(map(int, np.squeeze(gt[1])))
+                        gt_y = list(map(int, np.squeeze(gt[3])))
+                        gt_seq_str = str(gt[4].tolist()[0])
+
+                        local_sigma_table[gt_id, det_id] = sigma_calculation(
+                            det_x, det_y, gt_x, gt_y
+                        )
+                        local_tau_table[gt_id, det_id] = tau_calculation(
+                            det_x, det_y, gt_x, gt_y
+                        )
+                        local_pred_str[det_id] = pred_seq_str
+                        local_gt_str[gt_id] = gt_seq_str
+
+            global_sigma = local_sigma_table
+            global_tau = local_tau_table
+            global_pred_str = local_pred_str
+            global_gt_str = local_gt_str
+
+    single_data = {}
+    single_data["sigma"] = global_sigma
+    single_data["global_tau"] = global_tau
+    single_data["global_pred_str"] = global_pred_str
+    single_data["global_gt_str"] = global_gt_str
+    return single_data
+
+
+def get_socre_B(gt_dir, img_id, pred_dict):
+    allInputs = 1
+
+    def input_reading_mod(pred_dict):
+        """This helper reads input from txt files"""
+        det = []
+        n = len(pred_dict)
+        for i in range(n):
+            points = pred_dict[i]["points"]
+            text = pred_dict[i]["texts"]
+            point = ",".join(
+                map(
+                    str,
+                    points.reshape(
+                        -1,
+                    ),
+                )
+            )
+            det.append([point, text])
+        return det
+
+    def gt_reading_mod(gt_dir, gt_id):
+        gt = io.loadmat("%s/poly_gt_img%s.mat" % (gt_dir, gt_id))
+        gt = gt["polygt"]
+        return gt
+
+    def detection_filtering(detections, groundtruths, threshold=0.5):
+        for gt_id, gt in enumerate(groundtruths):
+            if (gt[5] == "#") and (gt[1].shape[1] > 1):
+                gt_x = list(map(int, np.squeeze(gt[1])))
+                gt_y = list(map(int, np.squeeze(gt[3])))
+                for det_id, detection in enumerate(detections):
+                    detection_orig = detection
+                    detection = [float(x) for x in detection[0].split(",")]
+                    detection = list(map(int, detection))
+                    det_x = detection[0::2]
+                    det_y = detection[1::2]
+                    det_gt_iou = iod(det_x, det_y, gt_x, gt_y)
+                    if det_gt_iou > threshold:
+                        detections[det_id] = []
+
+                detections[:] = [item for item in detections if item != []]
+        return detections
+
+    def sigma_calculation(det_x, det_y, gt_x, gt_y):
+        """
+        sigma = inter_area / gt_area
+        """
+        return np.round(
+            (area_of_intersection(det_x, det_y, gt_x, gt_y) / area(gt_x, gt_y)), 2
+        )
+
+    def tau_calculation(det_x, det_y, gt_x, gt_y):
+        if area(det_x, det_y) == 0.0:
+            return 0
+        return np.round(
+            (area_of_intersection(det_x, det_y, gt_x, gt_y) / area(det_x, det_y)), 2
+        )
+
+    ##############################Initialization###################################
+    # global_sigma = []
+    # global_tau = []
+    # global_pred_str = []
+    # global_gt_str = []
+    ###############################################################################
+
+    for input_id in range(allInputs):
+        if (
+            (input_id != ".DS_Store")
+            and (input_id != "Pascal_result.txt")
+            and (input_id != "Pascal_result_curved.txt")
+            and (input_id != "Pascal_result_non_curved.txt")
+            and (input_id != "Deteval_result.txt")
+            and (input_id != "Deteval_result_curved.txt")
+            and (input_id != "Deteval_result_non_curved.txt")
+        ):
+            detections = input_reading_mod(pred_dict)
+            groundtruths = gt_reading_mod(gt_dir, img_id).tolist()
+            detections = detection_filtering(
+                detections, groundtruths
+            )  # filters detections overlapping with DC area
+            dc_id = []
+            for i in range(len(groundtruths)):
+                if groundtruths[i][5] == "#":
+                    dc_id.append(i)
+            cnt = 0
+            for a in dc_id:
+                num = a - cnt
+                del groundtruths[num]
+                cnt += 1
+
+            local_sigma_table = np.zeros((len(groundtruths), len(detections)))
+            local_tau_table = np.zeros((len(groundtruths), len(detections)))
+            local_pred_str = {}
+            local_gt_str = {}
+
+            for gt_id, gt in enumerate(groundtruths):
+                if len(detections) > 0:
+                    for det_id, detection in enumerate(detections):
+                        detection_orig = detection
+                        detection = [float(x) for x in detection[0].split(",")]
+                        detection = list(map(int, detection))
+                        pred_seq_str = detection_orig[1].strip()
+                        det_x = detection[0::2]
+                        det_y = detection[1::2]
+                        gt_x = list(map(int, np.squeeze(gt[1])))
+                        gt_y = list(map(int, np.squeeze(gt[3])))
+                        gt_seq_str = str(gt[4].tolist()[0])
+
+                        local_sigma_table[gt_id, det_id] = sigma_calculation(
+                            det_x, det_y, gt_x, gt_y
+                        )
+                        local_tau_table[gt_id, det_id] = tau_calculation(
+                            det_x, det_y, gt_x, gt_y
+                        )
+                        local_pred_str[det_id] = pred_seq_str
+                        local_gt_str[gt_id] = gt_seq_str
+
+            global_sigma = local_sigma_table
+            global_tau = local_tau_table
+            global_pred_str = local_pred_str
+            global_gt_str = local_gt_str
+
+    single_data = {}
+    single_data["sigma"] = global_sigma
+    single_data["global_tau"] = global_tau
+    single_data["global_pred_str"] = global_pred_str
+    single_data["global_gt_str"] = global_gt_str
+    return single_data
+
+
+def get_score_C(gt_label, text, pred_bboxes):
+    """
+    get score for CentripetalText (CT) prediction.
+    """
+    check_install("Polygon", "Polygon3")
+    import Polygon as plg
+
+    def gt_reading_mod(gt_label, text):
+        """This helper reads groundtruths from mat files"""
+        groundtruths = []
+        nbox = len(gt_label)
+        for i in range(nbox):
+            label = {"transcription": text[i][0], "points": gt_label[i].numpy()}
+            groundtruths.append(label)
+
+        return groundtruths
+
+    def get_union(pD, pG):
+        areaA = pD.area()
+        areaB = pG.area()
+        return areaA + areaB - get_intersection(pD, pG)
+
+    def get_intersection(pD, pG):
+        pInt = pD & pG
+        if len(pInt) == 0:
+            return 0
+        return pInt.area()
+
+    def detection_filtering(detections, groundtruths, threshold=0.5):
+        for gt in groundtruths:
+            point_num = gt["points"].shape[1] // 2
+            if gt["transcription"] == "###" and (point_num > 1):
+                gt_p = np.array(gt["points"]).reshape(point_num, 2).astype("int32")
+                gt_p = plg.Polygon(gt_p)
+
+                for det_id, detection in enumerate(detections):
+                    det_y = detection[0::2]
+                    det_x = detection[1::2]
+
+                    det_p = np.concatenate((np.array(det_x), np.array(det_y)))
+                    det_p = det_p.reshape(2, -1).transpose()
+                    det_p = plg.Polygon(det_p)
+
+                    try:
+                        det_gt_iou = get_intersection(det_p, gt_p) / det_p.area()
+                    except:
+                        print(det_x, det_y, gt_p)
+                    if det_gt_iou > threshold:
+                        detections[det_id] = []
+
+                detections[:] = [item for item in detections if item != []]
+        return detections
+
+    def sigma_calculation(det_p, gt_p):
+        """
+        sigma = inter_area / gt_area
+        """
+        if gt_p.area() == 0.0:
+            return 0
+        return get_intersection(det_p, gt_p) / gt_p.area()
+
+    def tau_calculation(det_p, gt_p):
+        """
+        tau = inter_area / det_area
+        """
+        if det_p.area() == 0.0:
+            return 0
+        return get_intersection(det_p, gt_p) / det_p.area()
+
+    detections = []
+
+    for item in pred_bboxes:
+        detections.append(item[:, ::-1].reshape(-1))
+
+    groundtruths = gt_reading_mod(gt_label, text)
+
+    detections = detection_filtering(
+        detections, groundtruths
+    )  # filters detections overlapping with DC area
+
+    for idx in range(len(groundtruths) - 1, -1, -1):
+        # NOTE: source code use 'orin' to indicate '#', here we use 'anno',
+        # which may cause slight drop in fscore, about 0.12
+        if groundtruths[idx]["transcription"] == "###":
+            groundtruths.pop(idx)
+
+    local_sigma_table = np.zeros((len(groundtruths), len(detections)))
+    local_tau_table = np.zeros((len(groundtruths), len(detections)))
+
+    for gt_id, gt in enumerate(groundtruths):
+        if len(detections) > 0:
+            for det_id, detection in enumerate(detections):
+                point_num = gt["points"].shape[1] // 2
+
+                gt_p = np.array(gt["points"]).reshape(point_num, 2).astype("int32")
+                gt_p = plg.Polygon(gt_p)
+
+                det_y = detection[0::2]
+                det_x = detection[1::2]
+
+                det_p = np.concatenate((np.array(det_x), np.array(det_y)))
+
+                det_p = det_p.reshape(2, -1).transpose()
+                det_p = plg.Polygon(det_p)
+
+                local_sigma_table[gt_id, det_id] = sigma_calculation(det_p, gt_p)
+                local_tau_table[gt_id, det_id] = tau_calculation(det_p, gt_p)
+
+    data = {}
+    data["sigma"] = local_sigma_table
+    data["global_tau"] = local_tau_table
+    data["global_pred_str"] = ""
+    data["global_gt_str"] = ""
+    return data
+
+
+def combine_results(all_data, rec_flag=True):
+    tr = 0.7
+    tp = 0.6
+    fsc_k = 0.8
+    k = 2
+    global_sigma = []
+    global_tau = []
+    global_pred_str = []
+    global_gt_str = []
+
+    for data in all_data:
+        global_sigma.append(data["sigma"])
+        global_tau.append(data["global_tau"])
+        global_pred_str.append(data["global_pred_str"])
+        global_gt_str.append(data["global_gt_str"])
+
+    global_accumulative_recall = 0
+    global_accumulative_precision = 0
+    total_num_gt = 0
+    total_num_det = 0
+    hit_str_count = 0
+    hit_count = 0
+
+    def one_to_one(
+        local_sigma_table,
+        local_tau_table,
+        local_accumulative_recall,
+        local_accumulative_precision,
+        global_accumulative_recall,
+        global_accumulative_precision,
+        gt_flag,
+        det_flag,
+        idy,
+        rec_flag,
+    ):
+        hit_str_num = 0
+        for gt_id in range(num_gt):
+            gt_matching_qualified_sigma_candidates = np.where(
+                local_sigma_table[gt_id, :] > tr
+            )
+            gt_matching_num_qualified_sigma_candidates = (
+                gt_matching_qualified_sigma_candidates[0].shape[0]
+            )
+            gt_matching_qualified_tau_candidates = np.where(
+                local_tau_table[gt_id, :] > tp
+            )
+            gt_matching_num_qualified_tau_candidates = (
+                gt_matching_qualified_tau_candidates[0].shape[0]
+            )
+
+            det_matching_qualified_sigma_candidates = np.where(
+                local_sigma_table[:, gt_matching_qualified_sigma_candidates[0]] > tr
+            )
+            det_matching_num_qualified_sigma_candidates = (
+                det_matching_qualified_sigma_candidates[0].shape[0]
+            )
+            det_matching_qualified_tau_candidates = np.where(
+                local_tau_table[:, gt_matching_qualified_tau_candidates[0]] > tp
+            )
+            det_matching_num_qualified_tau_candidates = (
+                det_matching_qualified_tau_candidates[0].shape[0]
+            )
+
+            if (
+                (gt_matching_num_qualified_sigma_candidates == 1)
+                and (gt_matching_num_qualified_tau_candidates == 1)
+                and (det_matching_num_qualified_sigma_candidates == 1)
+                and (det_matching_num_qualified_tau_candidates == 1)
+            ):
+                global_accumulative_recall = global_accumulative_recall + 1.0
+                global_accumulative_precision = global_accumulative_precision + 1.0
+                local_accumulative_recall = local_accumulative_recall + 1.0
+                local_accumulative_precision = local_accumulative_precision + 1.0
+
+                gt_flag[0, gt_id] = 1
+                matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
+                # recg start
+                if rec_flag:
+                    gt_str_cur = global_gt_str[idy][gt_id]
+                    pred_str_cur = global_pred_str[idy][matched_det_id[0].tolist()[0]]
+                    if pred_str_cur == gt_str_cur:
+                        hit_str_num += 1
+                    else:
+                        if pred_str_cur.lower() == gt_str_cur.lower():
+                            hit_str_num += 1
+                # recg end
+                det_flag[0, matched_det_id] = 1
+        return (
+            local_accumulative_recall,
+            local_accumulative_precision,
+            global_accumulative_recall,
+            global_accumulative_precision,
+            gt_flag,
+            det_flag,
+            hit_str_num,
+        )
+
+    def one_to_many(
+        local_sigma_table,
+        local_tau_table,
+        local_accumulative_recall,
+        local_accumulative_precision,
+        global_accumulative_recall,
+        global_accumulative_precision,
+        gt_flag,
+        det_flag,
+        idy,
+        rec_flag,
+    ):
+        hit_str_num = 0
+        for gt_id in range(num_gt):
+            # skip the following if the groundtruth was matched
+            if gt_flag[0, gt_id] > 0:
+                continue
+
+            non_zero_in_sigma = np.where(local_sigma_table[gt_id, :] > 0)
+            num_non_zero_in_sigma = non_zero_in_sigma[0].shape[0]
+
+            if num_non_zero_in_sigma >= k:
+                ####search for all detections that overlaps with this groundtruth
+                qualified_tau_candidates = np.where(
+                    (local_tau_table[gt_id, :] >= tp) & (det_flag[0, :] == 0)
+                )
+                num_qualified_tau_candidates = qualified_tau_candidates[0].shape[0]
+
+                if num_qualified_tau_candidates == 1:
+                    if (local_tau_table[gt_id, qualified_tau_candidates] >= tp) and (
+                        local_sigma_table[gt_id, qualified_tau_candidates] >= tr
+                    ):
+                        # became an one-to-one case
+                        global_accumulative_recall = global_accumulative_recall + 1.0
+                        global_accumulative_precision = (
+                            global_accumulative_precision + 1.0
+                        )
+                        local_accumulative_recall = local_accumulative_recall + 1.0
+                        local_accumulative_precision = (
+                            local_accumulative_precision + 1.0
+                        )
+
+                        gt_flag[0, gt_id] = 1
+                        det_flag[0, qualified_tau_candidates] = 1
+                        # recg start
+                        if rec_flag:
+                            gt_str_cur = global_gt_str[idy][gt_id]
+                            pred_str_cur = global_pred_str[idy][
+                                qualified_tau_candidates[0].tolist()[0]
+                            ]
+                            if pred_str_cur == gt_str_cur:
+                                hit_str_num += 1
+                            else:
+                                if pred_str_cur.lower() == gt_str_cur.lower():
+                                    hit_str_num += 1
+                        # recg end
+                elif np.sum(local_sigma_table[gt_id, qualified_tau_candidates]) >= tr:
+                    gt_flag[0, gt_id] = 1
+                    det_flag[0, qualified_tau_candidates] = 1
+                    # recg start
+                    if rec_flag:
+                        gt_str_cur = global_gt_str[idy][gt_id]
+                        pred_str_cur = global_pred_str[idy][
+                            qualified_tau_candidates[0].tolist()[0]
+                        ]
+                        if pred_str_cur == gt_str_cur:
+                            hit_str_num += 1
+                        else:
+                            if pred_str_cur.lower() == gt_str_cur.lower():
+                                hit_str_num += 1
+                    # recg end
+
+                    global_accumulative_recall = global_accumulative_recall + fsc_k
+                    global_accumulative_precision = (
+                        global_accumulative_precision
+                        + num_qualified_tau_candidates * fsc_k
+                    )
+
+                    local_accumulative_recall = local_accumulative_recall + fsc_k
+                    local_accumulative_precision = (
+                        local_accumulative_precision
+                        + num_qualified_tau_candidates * fsc_k
+                    )
+
+        return (
+            local_accumulative_recall,
+            local_accumulative_precision,
+            global_accumulative_recall,
+            global_accumulative_precision,
+            gt_flag,
+            det_flag,
+            hit_str_num,
+        )
+
+    def many_to_one(
+        local_sigma_table,
+        local_tau_table,
+        local_accumulative_recall,
+        local_accumulative_precision,
+        global_accumulative_recall,
+        global_accumulative_precision,
+        gt_flag,
+        det_flag,
+        idy,
+        rec_flag,
+    ):
+        hit_str_num = 0
+        for det_id in range(num_det):
+            # skip the following if the detection was matched
+            if det_flag[0, det_id] > 0:
+                continue
+
+            non_zero_in_tau = np.where(local_tau_table[:, det_id] > 0)
+            num_non_zero_in_tau = non_zero_in_tau[0].shape[0]
+
+            if num_non_zero_in_tau >= k:
+                ####search for all detections that overlaps with this groundtruth
+                qualified_sigma_candidates = np.where(
+                    (local_sigma_table[:, det_id] >= tp) & (gt_flag[0, :] == 0)
+                )
+                num_qualified_sigma_candidates = qualified_sigma_candidates[0].shape[0]
+
+                if num_qualified_sigma_candidates == 1:
+                    if (local_tau_table[qualified_sigma_candidates, det_id] >= tp) and (
+                        local_sigma_table[qualified_sigma_candidates, det_id] >= tr
+                    ):
+                        # became an one-to-one case
+                        global_accumulative_recall = global_accumulative_recall + 1.0
+                        global_accumulative_precision = (
+                            global_accumulative_precision + 1.0
+                        )
+                        local_accumulative_recall = local_accumulative_recall + 1.0
+                        local_accumulative_precision = (
+                            local_accumulative_precision + 1.0
+                        )
+
+                        gt_flag[0, qualified_sigma_candidates] = 1
+                        det_flag[0, det_id] = 1
+                        # recg start
+                        if rec_flag:
+                            pred_str_cur = global_pred_str[idy][det_id]
+                            gt_len = len(qualified_sigma_candidates[0])
+                            for idx in range(gt_len):
+                                ele_gt_id = qualified_sigma_candidates[0].tolist()[idx]
+                                if ele_gt_id not in global_gt_str[idy]:
+                                    continue
+                                gt_str_cur = global_gt_str[idy][ele_gt_id]
+                                if pred_str_cur == gt_str_cur:
+                                    hit_str_num += 1
+                                    break
+                                else:
+                                    if pred_str_cur.lower() == gt_str_cur.lower():
+                                        hit_str_num += 1
+                                    break
+                        # recg end
+                elif np.sum(local_tau_table[qualified_sigma_candidates, det_id]) >= tp:
+                    det_flag[0, det_id] = 1
+                    gt_flag[0, qualified_sigma_candidates] = 1
+                    # recg start
+                    if rec_flag:
+                        pred_str_cur = global_pred_str[idy][det_id]
+                        gt_len = len(qualified_sigma_candidates[0])
+                        for idx in range(gt_len):
+                            ele_gt_id = qualified_sigma_candidates[0].tolist()[idx]
+                            if ele_gt_id not in global_gt_str[idy]:
+                                continue
+                            gt_str_cur = global_gt_str[idy][ele_gt_id]
+                            if pred_str_cur == gt_str_cur:
+                                hit_str_num += 1
+                                break
+                            else:
+                                if pred_str_cur.lower() == gt_str_cur.lower():
+                                    hit_str_num += 1
+                                    break
+                    # recg end
+
+                    global_accumulative_recall = (
+                        global_accumulative_recall
+                        + num_qualified_sigma_candidates * fsc_k
+                    )
+                    global_accumulative_precision = (
+                        global_accumulative_precision + fsc_k
+                    )
+
+                    local_accumulative_recall = (
+                        local_accumulative_recall
+                        + num_qualified_sigma_candidates * fsc_k
+                    )
+                    local_accumulative_precision = local_accumulative_precision + fsc_k
+        return (
+            local_accumulative_recall,
+            local_accumulative_precision,
+            global_accumulative_recall,
+            global_accumulative_precision,
+            gt_flag,
+            det_flag,
+            hit_str_num,
+        )
+
+    for idx in range(len(global_sigma)):
+        local_sigma_table = np.array(global_sigma[idx])
+        local_tau_table = global_tau[idx]
+
+        num_gt = local_sigma_table.shape[0]
+        num_det = local_sigma_table.shape[1]
+
+        total_num_gt = total_num_gt + num_gt
+        total_num_det = total_num_det + num_det
+
+        local_accumulative_recall = 0
+        local_accumulative_precision = 0
+        gt_flag = np.zeros((1, num_gt))
+        det_flag = np.zeros((1, num_det))
+
+        #######first check for one-to-one case##########
+        (
+            local_accumulative_recall,
+            local_accumulative_precision,
+            global_accumulative_recall,
+            global_accumulative_precision,
+            gt_flag,
+            det_flag,
+            hit_str_num,
+        ) = one_to_one(
+            local_sigma_table,
+            local_tau_table,
+            local_accumulative_recall,
+            local_accumulative_precision,
+            global_accumulative_recall,
+            global_accumulative_precision,
+            gt_flag,
+            det_flag,
+            idx,
+            rec_flag,
+        )
+
+        hit_str_count += hit_str_num
+        #######then check for one-to-many case##########
+        (
+            local_accumulative_recall,
+            local_accumulative_precision,
+            global_accumulative_recall,
+            global_accumulative_precision,
+            gt_flag,
+            det_flag,
+            hit_str_num,
+        ) = one_to_many(
+            local_sigma_table,
+            local_tau_table,
+            local_accumulative_recall,
+            local_accumulative_precision,
+            global_accumulative_recall,
+            global_accumulative_precision,
+            gt_flag,
+            det_flag,
+            idx,
+            rec_flag,
+        )
+        hit_str_count += hit_str_num
+        #######then check for many-to-one case##########
+        (
+            local_accumulative_recall,
+            local_accumulative_precision,
+            global_accumulative_recall,
+            global_accumulative_precision,
+            gt_flag,
+            det_flag,
+            hit_str_num,
+        ) = many_to_one(
+            local_sigma_table,
+            local_tau_table,
+            local_accumulative_recall,
+            local_accumulative_precision,
+            global_accumulative_recall,
+            global_accumulative_precision,
+            gt_flag,
+            det_flag,
+            idx,
+            rec_flag,
+        )
+        hit_str_count += hit_str_num
+
+    try:
+        recall = global_accumulative_recall / total_num_gt
+    except ZeroDivisionError:
+        recall = 0
+
+    try:
+        precision = global_accumulative_precision / total_num_det
+    except ZeroDivisionError:
+        precision = 0
+
+    try:
+        f_score = 2 * precision * recall / (precision + recall)
+    except ZeroDivisionError:
+        f_score = 0
+
+    try:
+        seqerr = 1 - float(hit_str_count) / global_accumulative_recall
+    except ZeroDivisionError:
+        seqerr = 1
+
+    try:
+        recall_e2e = float(hit_str_count) / total_num_gt
+    except ZeroDivisionError:
+        recall_e2e = 0
+
+    try:
+        precision_e2e = float(hit_str_count) / total_num_det
+    except ZeroDivisionError:
+        precision_e2e = 0
+
+    try:
+        f_score_e2e = 2 * precision_e2e * recall_e2e / (precision_e2e + recall_e2e)
+    except ZeroDivisionError:
+        f_score_e2e = 0
+
+    final = {
+        "total_num_gt": total_num_gt,
+        "total_num_det": total_num_det,
+        "global_accumulative_recall": global_accumulative_recall,
+        "hit_str_count": hit_str_count,
+        "recall": recall,
+        "precision": precision,
+        "f_score": f_score,
+        "seqerr": seqerr,
+        "recall_e2e": recall_e2e,
+        "precision_e2e": precision_e2e,
+        "f_score_e2e": f_score_e2e,
+    }
+    return final

ppocr/utils/e2e_metric/polygon_fast.py

@@ -0,0 +1,84 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import numpy as np
+from shapely.geometry import Polygon
+
+"""
+:param det_x: [1, N] Xs of detection's vertices
+:param det_y: [1, N] Ys of detection's vertices
+:param gt_x: [1, N] Xs of groundtruth's vertices
+:param gt_y: [1, N] Ys of groundtruth's vertices
+
+##############
+All the calculation of 'AREA' in this script is handled by:
+1) First generating a binary mask with the polygon area filled up with 1's
+2) Summing up all the 1's
+"""
+
+
+def area(x, y):
+    polygon = Polygon(np.stack([x, y], axis=1))
+    return float(polygon.area)
+
+
+def approx_area_of_intersection(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine if both polygons are intersecting with each others with an approximation method.
+    Area of intersection represented by the minimum bounding rectangular [xmin, ymin, xmax, ymax]
+    """
+    det_ymax = np.max(det_y)
+    det_xmax = np.max(det_x)
+    det_ymin = np.min(det_y)
+    det_xmin = np.min(det_x)
+
+    gt_ymax = np.max(gt_y)
+    gt_xmax = np.max(gt_x)
+    gt_ymin = np.min(gt_y)
+    gt_xmin = np.min(gt_x)
+
+    all_min_ymax = np.minimum(det_ymax, gt_ymax)
+    all_max_ymin = np.maximum(det_ymin, gt_ymin)
+
+    intersect_heights = np.maximum(0.0, (all_min_ymax - all_max_ymin))
+
+    all_min_xmax = np.minimum(det_xmax, gt_xmax)
+    all_max_xmin = np.maximum(det_xmin, gt_xmin)
+    intersect_widths = np.maximum(0.0, (all_min_xmax - all_max_xmin))
+
+    return intersect_heights * intersect_widths
+
+
+def area_of_intersection(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.intersection(p2).area)
+
+
+def area_of_union(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.union(p2).area)
+
+
+def iou(det_x, det_y, gt_x, gt_y):
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
+        area_of_union(det_x, det_y, gt_x, gt_y) + 1.0
+    )
+
+
+def iod(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine the fraction of intersection area over detection area
+    """
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (area(det_x, det_y) + 1.0)