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widerface_evaluate/README.md

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+# WiderFace-Evaluation
+Python Evaluation Code for [Wider Face Dataset](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/)
+
+
+## Usage
+
+
+##### before evaluating ....
+
+````
+python3 setup.py build_ext --inplace
+````
+
+##### evaluating
+
+**GroungTruth:** `wider_face_val.mat`, `wider_easy_val.mat`, `wider_medium_val.mat`,`wider_hard_val.mat`
+
+````
+python3 evaluation.py -p <your prediction dir> -g <groud truth dir>
+````
+
+## Bugs & Problems
+please issue
+
+## Acknowledgements
+
+some code borrowed from Sergey Karayev

widerface_evaluate/evaluation.py

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+"""
+WiderFace evaluation code
+author: wondervictor
+mail: tianhengcheng@gmail.com
+copyright@wondervictor
+"""
+
+import os
+import tqdm
+import pickle
+import argparse
+import numpy as np
+from scipy.io import loadmat
+from bbox import bbox_overlaps
+from IPython import embed
+
+
+def get_gt_boxes(gt_dir):
+    """ gt dir: (wider_face_val.mat, wider_easy_val.mat, wider_medium_val.mat, wider_hard_val.mat)"""
+
+    gt_mat = loadmat(os.path.join(gt_dir, 'wider_face_val.mat'))
+    hard_mat = loadmat(os.path.join(gt_dir, 'wider_hard_val.mat'))
+    medium_mat = loadmat(os.path.join(gt_dir, 'wider_medium_val.mat'))
+    easy_mat = loadmat(os.path.join(gt_dir, 'wider_easy_val.mat'))
+
+    facebox_list = gt_mat['face_bbx_list']
+    event_list = gt_mat['event_list']
+    file_list = gt_mat['file_list']
+
+    hard_gt_list = hard_mat['gt_list']
+    medium_gt_list = medium_mat['gt_list']
+    easy_gt_list = easy_mat['gt_list']
+
+    return facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list
+
+
+def get_gt_boxes_from_txt(gt_path, cache_dir):
+
+    cache_file = os.path.join(cache_dir, 'gt_cache.pkl')
+    if os.path.exists(cache_file):
+        f = open(cache_file, 'rb')
+        boxes = pickle.load(f)
+        f.close()
+        return boxes
+
+    f = open(gt_path, 'r')
+    state = 0
+    lines = f.readlines()
+    lines = list(map(lambda x: x.rstrip('\r\n'), lines))
+    boxes = {}
+    print(len(lines))
+    f.close()
+    current_boxes = []
+    current_name = None
+    for line in lines:
+        if state == 0 and '--' in line:
+            state = 1
+            current_name = line
+            continue
+        if state == 1:
+            state = 2
+            continue
+
+        if state == 2 and '--' in line:
+            state = 1
+            boxes[current_name] = np.array(current_boxes).astype('float32')
+            current_name = line
+            current_boxes = []
+            continue
+
+        if state == 2:
+            box = [float(x) for x in line.split(' ')[:4]]
+            current_boxes.append(box)
+            continue
+
+    f = open(cache_file, 'wb')
+    pickle.dump(boxes, f)
+    f.close()
+    return boxes
+
+
+def read_pred_file(filepath):
+
+    with open(filepath, 'r') as f:
+        lines = f.readlines()
+        img_file = lines[0].rstrip('\n\r')
+        lines = lines[2:]
+
+    # b = lines[0].rstrip('\r\n').split(' ')[:-1]
+    # c = float(b)
+    # a = map(lambda x: [[float(a[0]), float(a[1]), float(a[2]), float(a[3]), float(a[4])] for a in x.rstrip('\r\n').split(' ')], lines)
+    boxes = []
+    for line in lines:
+        line = line.rstrip('\r\n').split(' ')
+        if line[0] == '':
+            continue
+        # a = float(line[4])
+        boxes.append([float(line[0]), float(line[1]), float(line[2]), float(line[3]), float(line[4])])
+    boxes = np.array(boxes)
+    # boxes = np.array(list(map(lambda x: [float(a) for a in x.rstrip('\r\n').split(' ')], lines))).astype('float')
+    return img_file.split('/')[-1], boxes
+
+
+def get_preds(pred_dir):
+    events = os.listdir(pred_dir)
+    boxes = dict()
+    pbar = tqdm.tqdm(events)
+
+    for event in pbar:
+        pbar.set_description('Reading Predictions ')
+        event_dir = os.path.join(pred_dir, event)
+        event_images = os.listdir(event_dir)
+        current_event = dict()
+        for imgtxt in event_images:
+            imgname, _boxes = read_pred_file(os.path.join(event_dir, imgtxt))
+            current_event[imgname.rstrip('.jpg')] = _boxes
+        boxes[event] = current_event
+    return boxes
+
+
+def norm_score(pred):
+    """ norm score
+    pred {key: [[x1,y1,x2,y2,s]]}
+    """
+
+    max_score = 0
+    min_score = 1
+
+    for _, k in pred.items():
+        for _, v in k.items():
+            if len(v) == 0:
+                continue
+            _min = np.min(v[:, -1])
+            _max = np.max(v[:, -1])
+            max_score = max(_max, max_score)
+            min_score = min(_min, min_score)
+
+    diff = max_score - min_score
+    for _, k in pred.items():
+        for _, v in k.items():
+            if len(v) == 0:
+                continue
+            v[:, -1] = (v[:, -1] - min_score)/diff
+
+
+def image_eval(pred, gt, ignore, iou_thresh):
+    """ single image evaluation
+    pred: Nx5
+    gt: Nx4
+    ignore:
+    """
+
+    _pred = pred.copy()
+    _gt = gt.copy()
+    pred_recall = np.zeros(_pred.shape[0])
+    recall_list = np.zeros(_gt.shape[0])
+    proposal_list = np.ones(_pred.shape[0])
+
+    _pred[:, 2] = _pred[:, 2] + _pred[:, 0]
+    _pred[:, 3] = _pred[:, 3] + _pred[:, 1]
+    _gt[:, 2] = _gt[:, 2] + _gt[:, 0]
+    _gt[:, 3] = _gt[:, 3] + _gt[:, 1]
+
+    overlaps = bbox_overlaps(_pred[:, :4], _gt)
+
+    for h in range(_pred.shape[0]):
+
+        gt_overlap = overlaps[h]
+        max_overlap, max_idx = gt_overlap.max(), gt_overlap.argmax()
+        if max_overlap >= iou_thresh:
+            if ignore[max_idx] == 0:
+                recall_list[max_idx] = -1
+                proposal_list[h] = -1
+            elif recall_list[max_idx] == 0:
+                recall_list[max_idx] = 1
+
+        r_keep_index = np.where(recall_list == 1)[0]
+        pred_recall[h] = len(r_keep_index)
+    return pred_recall, proposal_list
+
+
+def img_pr_info(thresh_num, pred_info, proposal_list, pred_recall):
+    pr_info = np.zeros((thresh_num, 2)).astype('float')
+    for t in range(thresh_num):
+
+        thresh = 1 - (t+1)/thresh_num
+        r_index = np.where(pred_info[:, 4] >= thresh)[0]
+        if len(r_index) == 0:
+            pr_info[t, 0] = 0
+            pr_info[t, 1] = 0
+        else:
+            r_index = r_index[-1]
+            p_index = np.where(proposal_list[:r_index+1] == 1)[0]
+            pr_info[t, 0] = len(p_index)
+            pr_info[t, 1] = pred_recall[r_index]
+    return pr_info
+
+
+def dataset_pr_info(thresh_num, pr_curve, count_face):
+    _pr_curve = np.zeros((thresh_num, 2))
+    for i in range(thresh_num):
+        _pr_curve[i, 0] = pr_curve[i, 1] / pr_curve[i, 0]
+        _pr_curve[i, 1] = pr_curve[i, 1] / count_face
+    return _pr_curve
+
+
+def voc_ap(rec, prec):
+
+    # correct AP calculation
+    # first append sentinel values at the end
+    mrec = np.concatenate(([0.], rec, [1.]))
+    mpre = np.concatenate(([0.], prec, [0.]))
+
+    # compute the precision envelope
+    for i in range(mpre.size - 1, 0, -1):
+        mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
+
+    # to calculate area under PR curve, look for points
+    # where X axis (recall) changes value
+    i = np.where(mrec[1:] != mrec[:-1])[0]
+
+    # and sum (\Delta recall) * prec
+    ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
+    return ap
+
+
+def evaluation(pred, gt_path, iou_thresh=0.5):
+    pred = get_preds(pred)
+    norm_score(pred)
+    facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list = get_gt_boxes(gt_path)
+    event_num = len(event_list)
+    thresh_num = 1000
+    settings = ['easy', 'medium', 'hard']
+    setting_gts = [easy_gt_list, medium_gt_list, hard_gt_list]
+    aps = []
+    for setting_id in range(3):
+        # different setting
+        gt_list = setting_gts[setting_id]
+        count_face = 0
+        pr_curve = np.zeros((thresh_num, 2)).astype('float')
+        # [hard, medium, easy]
+        pbar = tqdm.tqdm(range(event_num))
+        for i in pbar:
+            pbar.set_description('Processing {}'.format(settings[setting_id]))
+            event_name = str(event_list[i][0][0])
+            img_list = file_list[i][0]
+            pred_list = pred[event_name]
+            sub_gt_list = gt_list[i][0]
+            # img_pr_info_list = np.zeros((len(img_list), thresh_num, 2))
+            gt_bbx_list = facebox_list[i][0]
+
+            for j in range(len(img_list)):
+                pred_info = pred_list[str(img_list[j][0][0])]
+
+                gt_boxes = gt_bbx_list[j][0].astype('float')
+                keep_index = sub_gt_list[j][0]
+                count_face += len(keep_index)
+
+                if len(gt_boxes) == 0 or len(pred_info) == 0:
+                    continue
+                ignore = np.zeros(gt_boxes.shape[0])
+                if len(keep_index) != 0:
+                    ignore[keep_index-1] = 1
+                pred_recall, proposal_list = image_eval(pred_info, gt_boxes, ignore, iou_thresh)
+
+                _img_pr_info = img_pr_info(thresh_num, pred_info, proposal_list, pred_recall)
+
+                pr_curve += _img_pr_info
+        pr_curve = dataset_pr_info(thresh_num, pr_curve, count_face)
+
+        propose = pr_curve[:, 0]
+        recall = pr_curve[:, 1]
+
+        ap = voc_ap(recall, propose)
+        aps.append(ap)
+
+    print("==================== Results ====================")
+    print("Easy   Val AP: {}".format(aps[0]))
+    print("Medium Val AP: {}".format(aps[1]))
+    print("Hard   Val AP: {}".format(aps[2]))
+    print("=================================================")
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-p', '--pred', default="./widerface_txt/")
+    parser.add_argument('-g', '--gt', default='./ground_truth/')
+
+    args = parser.parse_args()
+    evaluation(args.pred, args.gt)
+
+
+
+
+
+
+
+
+
+
+
+

widerface_evaluate/setup.py

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+"""
+WiderFace evaluation code
+author: wondervictor
+mail: tianhengcheng@gmail.com
+copyright@wondervictor
+"""
+
+from distutils.core import setup, Extension
+from Cython.Build import cythonize
+import numpy
+
+package = Extension('bbox', ['box_overlaps.pyx'], include_dirs=[numpy.get_include()])
+setup(ext_modules=cythonize([package]))