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567 lines
19 KiB
Python
Executable File
567 lines
19 KiB
Python
Executable File
"""
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WiderFace evaluation code
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author: wondervictor
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mail: tianhengcheng@gmail.com
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copyright@wondervictor
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"""
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from __future__ import absolute_import
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import os
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import tqdm
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import pickle
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import datetime
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import argparse
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import numpy as np
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from scipy.io import loadmat
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#from facedet.evaluation.box_utils import jaccard
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#from facedet.evaluation.bbox import bbox_overlaps
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#import torch
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#from mmdet.core.bbox import bbox_overlaps
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#def intersect(box_a, box_b):
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# A = box_a.size(0)
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# B = box_b.size(0)
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# max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2),
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# box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
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# min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2),
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# box_b[:, :2].unsqueeze(0).expand(A, B, 2))
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# inter = torch.clamp((max_xy - min_xy), min=0)
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# return inter[:, :, 0] * inter[:, :, 1]
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#
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#def jaccard(box_a, box_b):
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# inter = intersect(box_a, box_b)
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# #torch.cuda.empty_cache()
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# if not inter.is_cuda:
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# box_a_cpu = box_a.cpu()
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# box_b_cpu = box_b.cpu()
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# area_a_cpu = ((box_a_cpu[:, 2]-box_a_cpu[:, 0]) *
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# (box_a_cpu[:, 3]-box_a_cpu[:, 1])).unsqueeze(1).expand_as(inter) # [A,B]
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# area_b_cpu = ((box_b_cpu[:, 2]-box_b_cpu[:, 0]) *
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# (box_b_cpu[:, 3]-box_b_cpu[:, 1])).unsqueeze(0).expand_as(inter) # [A,B]
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# union_cpu = area_a_cpu + area_b_cpu - inter.cpu()
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# return inter / union_cpu
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# else:
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# area_a = ((box_a[:, 2]-box_a[:, 0]) *
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# (box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B]
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# area_b = ((box_b[:, 2]-box_b[:, 0]) *
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# (box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B]
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# union = area_a + area_b - inter
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#
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# return inter / union # [A,B]
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#
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def bbox_overlaps(boxes, query_boxes):
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n_ = boxes.shape[0]
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k_ = query_boxes.shape[0]
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overlaps = np.zeros((n_, k_), dtype=np.float)
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for k in range(k_):
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query_box_area = (query_boxes[k, 2] - query_boxes[k, 0] +
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1) * (query_boxes[k, 3] - query_boxes[k, 1] + 1)
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for n in range(n_):
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iw = min(boxes[n, 2], query_boxes[k, 2]) - max(
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boxes[n, 0], query_boxes[k, 0]) + 1
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if iw > 0:
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ih = min(boxes[n, 3], query_boxes[k, 3]) - max(
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boxes[n, 1], query_boxes[k, 1]) + 1
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if ih > 0:
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box_area = (boxes[n, 2] - boxes[n, 0] +
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1) * (boxes[n, 3] - boxes[n, 1] + 1)
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all_area = float(box_area + query_box_area - iw * ih)
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overlaps[n, k] = iw * ih / all_area
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return overlaps
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def bbox_overlap(a, b):
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x1 = np.maximum(a[:,0], b[0])
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y1 = np.maximum(a[:,1], b[1])
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x2 = np.minimum(a[:,2], b[2])
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y2 = np.minimum(a[:,3], b[3])
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w = x2-x1+1
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h = y2-y1+1
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inter = w*h
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aarea = (a[:,2]-a[:,0]+1) * (a[:,3]-a[:,1]+1)
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barea = (b[2]-b[0]+1) * (b[3]-b[1]+1)
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o = inter / (aarea+barea-inter)
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o[w<=0] = 0
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o[h<=0] = 0
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return o
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def __bbox_overlap(a, b):
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x1 = torch.max(a[:,0], b[0])
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y1 = torch.max(a[:,1], b[1])
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x2 = torch.min(a[:,2], b[2])
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y2 = torch.min(a[:,3], b[3])
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w = x2-x1+1
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h = y2-y1+1
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inter = w*h
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aarea = (a[:,2]-a[:,0]+1) * (a[:,3]-a[:,1]+1)
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barea = (b[2]-b[0]+1) * (b[3]-b[1]+1)
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o = inter / (aarea+barea-inter)
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o[w<=0] = 0
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o[h<=0] = 0
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return o
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def np_around(array, num_decimals=0):
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#return array
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return np.around(array, decimals=num_decimals)
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#def compute_iou(box_a, box_b):
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# x0 = np.maximum(box_a[:,0], box_b[0])
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# y0 = np.maximum(box_a[:,1], box_b[1])
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# x1 = np.minimum(box_a[:,2], box_b[2])
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# y1 = np.minimum(box_a[:,3], box_b[3])
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# #print ('x0', x0[0], x1[0], y0[0], y1[0], box_a[0], box_b[:])
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# #w = np.maximum(x1 - x0 + 1, 0)
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# w = np_around(x1 - x0 + 1)
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# #h = np.maximum(y1 - y0 + 1, 0)
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# h = np_around(y1 - y0 + 1)
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# inter = np_around(w * h)
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# area_a = (box_a[:,2] - box_a[:,0] + 1) * (box_a[:,3] - box_a[:,1] + 1)
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# area_a = np_around(area_a)
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# area_b = (box_b[2] - box_b[0] + 1) * (box_b[3] - box_b[1] + 1)
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# area_b = np_around(area_b)
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# iou = inter / (area_a + area_b - inter)
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# iou[w <= 0] = 0
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# iou[h <=0] = 0
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# return iou
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def np_round(val, decimals=4):
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return val
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#if isinstance(val, np.ndarray):
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# val = np.around(val, decimals=decimals)
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#return val
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def get_gt_boxes(gt_dir):
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""" gt dir: (wider_face_val.mat, wider_easy_val.mat, wider_medium_val.mat, wider_hard_val.mat)"""
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gt_mat = loadmat(os.path.join(gt_dir, 'wider_face_val.mat'))
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hard_mat = loadmat(os.path.join(gt_dir, 'wider_hard_val.mat'))
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medium_mat = loadmat(os.path.join(gt_dir, 'wider_medium_val.mat'))
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easy_mat = loadmat(os.path.join(gt_dir, 'wider_easy_val.mat'))
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facebox_list = gt_mat['face_bbx_list']
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event_list = gt_mat['event_list']
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file_list = gt_mat['file_list']
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hard_gt_list = hard_mat['gt_list']
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medium_gt_list = medium_mat['gt_list']
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easy_gt_list = easy_mat['gt_list']
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return facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list
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def get_gt_boxes_from_txt(gt_path, cache_dir):
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cache_file = os.path.join(cache_dir, 'gt_cache.pkl')
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if os.path.exists(cache_file):
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f = open(cache_file, 'rb')
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boxes = pickle.load(f)
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f.close()
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return boxes
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f = open(gt_path, 'r')
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state = 0
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lines = f.readlines()
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lines = list(map(lambda x: x.rstrip('\r\n'), lines))
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boxes = {}
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#print(len(lines))
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f.close()
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current_boxes = []
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current_name = None
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for line in lines:
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if state == 0 and '--' in line:
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state = 1
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current_name = line
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continue
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if state == 1:
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state = 2
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continue
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if state == 2 and '--' in line:
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state = 1
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boxes[current_name] = np.array(current_boxes).astype('float32')
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current_name = line
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current_boxes = []
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continue
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if state == 2:
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box = [float(x) for x in line.split(' ')[:4]]
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current_boxes.append(box)
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continue
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f = open(cache_file, 'wb')
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pickle.dump(boxes, f)
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f.close()
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return boxes
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def read_pred_file(filepath):
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with open(filepath, 'r') as f:
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lines = f.readlines()
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img_file = lines[0].rstrip('\n\r')
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lines = lines[2:]
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boxes = np.array(list(map(lambda x: [float(a) for a in x.rstrip('\r\n').split(' ')], lines))).astype('float')
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return img_file.split('/')[-1], boxes
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def get_preds(pred_dir):
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events = os.listdir(pred_dir)
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boxes = dict()
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pbar = tqdm.tqdm(events)
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for event in pbar:
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pbar.set_description('Reading Predictions ')
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event_dir = os.path.join(pred_dir, event)
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event_images = os.listdir(event_dir)
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current_event = dict()
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for imgtxt in event_images:
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imgname, _boxes = read_pred_file(os.path.join(event_dir, imgtxt))
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current_event[imgname.rstrip('.jpg')] = _boxes
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boxes[event] = current_event
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return boxes
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def norm_score(pred):
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""" norm score
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pred {key: [[x1,y1,x2,y2,s]]}
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"""
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max_score = -1
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min_score = 2
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for _, k in pred.items():
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for _, v in k.items():
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if len(v) == 0:
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continue
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_min = np.min(v[:, -1])
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_max = np.max(v[:, -1])
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max_score = max(_max, max_score)
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min_score = min(_min, min_score)
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diff = max_score - min_score
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for _, k in pred.items():
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for _, v in k.items():
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if len(v) == 0:
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continue
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v[:, -1] = (v[:, -1] - min_score).astype(np.float64)/diff
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return pred
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def image_eval(pred, gt, ignore, iou_thresh, mpp):
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""" single image evaluation
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pred: Nx5
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gt: Nx4
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ignore:
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"""
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_pred = pred.copy()
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_gt = gt.copy()
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pred_recall = np.zeros(_pred.shape[0])
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recall_list = np.zeros(_gt.shape[0])
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proposal_list = np.ones(_pred.shape[0])
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_pred[:, 2] = _pred[:, 2] + _pred[:, 0]
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_pred[:, 3] = _pred[:, 3] + _pred[:, 1]
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_gt[:, 2] = _gt[:, 2] + _gt[:, 0]
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_gt[:, 3] = _gt[:, 3] + _gt[:, 1]
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gt_overlap_list = mpp.starmap(bbox_overlap, zip([_gt]*_pred.shape[0],[_pred[h] for h in range(_pred.shape[0])]))
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#use_cuda = True
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#if use_cuda:
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# _pred = torch.cuda.FloatTensor(_pred[:,:4])
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# _gt = torch.cuda.FloatTensor(_gt)
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#else:
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# _pred = torch.FloatTensor(_pred[:,:4])
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# _gt = torch.FloatTensor(_gt)
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#overlaps = jaccard(_pred, _gt).cpu().numpy()
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#overlaps = compute_iou((_pred[:, :4]), (_gt))
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#overlaps = bbox_overlaps(_pred, _gt)
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#if use_cuda:
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# overlaps = overlaps.cpu().numpy()
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#else:
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# overlaps = overlaps.numpy()
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for h in range(_pred.shape[0]):
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#gt_overlap = overlaps[h]
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#gt_overlap = bbox_overlap(_gt, _pred[h])
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gt_overlap = gt_overlap_list[h]
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#if use_cuda:
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# gt_overlap = gt_overlap.cpu().numpy()
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#else:
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# gt_overlap = gt_overlap.numpy()
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#max_overlap, max_idx = gt_overlap.max(), gt_overlap.argmax()
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#gt_overlap = compute_iou(_gt, _pred[h, :4])
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#exit()
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#exit()
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#print ('overlap', gt_overlap)
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max_overlap, max_idx = gt_overlap.max(), gt_overlap.argmax()
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if max_overlap >= iou_thresh:
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if ignore[max_idx] == 0:
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recall_list[max_idx] = -1
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proposal_list[h] = -1
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elif recall_list[max_idx] == 0:
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recall_list[max_idx] = 1
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r_keep_index = np.where(recall_list == 1)[0]
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pred_recall[h] = len(r_keep_index)
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return pred_recall, proposal_list
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def img_pr_info(thresh_num, pred_info, proposal_list, pred_recall):
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pr_info = np.zeros((thresh_num, 2)).astype('float')
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fp = np.zeros((pred_info.shape[0],), dtype=np.int)
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last_info = [-1, -1]
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for t in range(thresh_num):
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thresh = 1 - (t+1)/thresh_num
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r_index = np.where(pred_info[:, 4] >= thresh)[0]
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if len(r_index) == 0:
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pr_info[t, 0] = 0
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pr_info[t, 1] = 0
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else:
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r_index = r_index[-1]
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p_index = np.where(proposal_list[:r_index+1] == 1)[0]
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pr_info[t, 0] = len(p_index) #valid pred number
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pr_info[t, 1] = pred_recall[r_index] # valid gt number
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if t>0 and pr_info[t, 0] > pr_info[t-1,0] and pr_info[t, 1]==pr_info[t-1,1]:
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fp[r_index] = 1
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#if thresh>=0.85:
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# print(thresh, t, pr_info[t])
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#print(pr_info[:10,0])
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#print(pr_info[:10,1])
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return pr_info, fp
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def dataset_pr_info(thresh_num, pr_curve, count_face):
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_pr_curve = np.zeros((thresh_num, 2))
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for i in range(thresh_num):
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#_pr_curve[i, 0] = round(pr_curve[i, 1] / pr_curve[i, 0], 4)
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#_pr_curve[i, 1] = round(pr_curve[i, 1] / count_face, 4)
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_pr_curve[i, 0] = pr_curve[i, 1] / pr_curve[i, 0]
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_pr_curve[i, 1] = pr_curve[i, 1] / count_face
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return _pr_curve
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def voc_ap(rec, prec):
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# correct AP calculation
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# first append sentinel values at the end
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#print ('rec:', rec)
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#print ('pre:', prec)
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mrec = np.concatenate(([0.], rec, [1.]))
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mpre = np.concatenate(([0.], prec, [0.]))
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# compute the precision envelope
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for i in range(mpre.size - 1, 0, -1):
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mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
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# to calculate area under PR curve, look for points
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# where X axis (recall) changes value
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i = np.where(mrec[1:] != mrec[:-1])[0]
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# and sum (\Delta recall) * prec
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ap = np_round(np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]))
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return ap
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def wider_evaluation(pred, gt_path, iou_thresh=0.5, debug=False):
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#pred = get_preds(pred)
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pred = norm_score(pred)
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thresh_num = 1000
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#thresh_num = 2000
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facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list = get_gt_boxes(gt_path)
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event_num = len(event_list)
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settings = ['easy', 'medium', 'hard']
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setting_gts = [easy_gt_list, medium_gt_list, hard_gt_list]
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from multiprocessing import Pool
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#from multiprocessing.pool import ThreadPool
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mpp = Pool(8)
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aps = [-1.0, -1.0, -1.0]
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meta = {}
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#setting_id = 2
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print('')
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for setting_id in range(3):
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#for setting_id in range(1):
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ta = datetime.datetime.now()
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# different setting
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#iou_th = 0.5 #+ 0.05 * idx
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iou_th = iou_thresh
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# different setting
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gt_list = setting_gts[setting_id]
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count_face = 0
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pr_curve = np.zeros((thresh_num, 2)).astype('float')
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# [hard, medium, easy]
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#pbar = tqdm.tqdm(range(event_num))
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#for i in pbar:
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high_score_count = 0
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high_score_fp_count = 0
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for i in range(event_num):
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#pbar.set_description('Processing {}'.format(settings[setting_id]))
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event_name = str(event_list[i][0][0])
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img_list = file_list[i][0]
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pred_list = pred[event_name]
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sub_gt_list = gt_list[i][0]
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# img_pr_info_list = np.zeros((len(img_list), thresh_num, 2))
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gt_bbx_list = facebox_list[i][0]
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for j in range(len(img_list)):
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img_name = str(img_list[j][0][0])
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pred_info = pred_list[img_name]
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gt_boxes = gt_bbx_list[j][0].astype('float')
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keep_index = sub_gt_list[j][0]
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#print ('keep_index', keep_index)
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count_face += len(keep_index)
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if len(gt_boxes) == 0 or len(pred_info) == 0:
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continue
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#ignore = np.zeros(gt_boxes.shape[0])
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#if len(keep_index) != 0:
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# ignore[keep_index-1] = 1
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#assert len(keep_index)>0
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ignore = np.zeros(gt_boxes.shape[0], dtype=np.int)
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if len(keep_index) != 0:
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ignore[keep_index-1] = 1
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pred_info = np_round(pred_info,1)
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#print('ignore:', len(ignore), len(np.where(ignore==1)[0]))
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#pred_sort_idx= np.argsort(pred_info[:,4])
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#pred_info = pred_info[pred_sort_idx][::-1]
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#print ('pred_info', pred_info[:20, 4])
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#exit()
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gt_boxes = np_round(gt_boxes)
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#ignore = np_round(ignore)
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pred_recall, proposal_list = image_eval(pred_info, gt_boxes, ignore, iou_th, mpp)
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#print(pred_recall[:10], proposal_list[:10])
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#print('1 stage', pred_recall, proposal_list)
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#print(pred_info.shape, pred_recall.shape)
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_img_pr_info, fp = img_pr_info(thresh_num, pred_info, proposal_list, pred_recall)
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#for f in range(pred_info.shape[0]):
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# _score = pred_info[f,4]
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# if _score<0.929:
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# break
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# high_score_count+=1
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# if fp[f]==1:
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# w = pred_info[f, 2]
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# h = pred_info[f, 3]
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# print('fp:', event_name, img_name, _score, w, h)
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# high_score_fp_count+=1
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pr_curve += _img_pr_info
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#print ('pr_curve', pr_curve, count_face)
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pr_curve = dataset_pr_info(thresh_num, pr_curve, count_face)
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#print(pr_curve.shape)
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propose = pr_curve[:, 0]
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recall = pr_curve[:, 1]
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#for f in range(thresh_num):
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# print('R-P:', recall[f], propose[f])
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for srecall in np.arange(0.1, 1.0001, 0.1):
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rindex = len(np.where(recall<=srecall)[0])-1
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rthresh = 1.0 - float(rindex)/thresh_num
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print('Recall-Precision-Thresh:', recall[rindex], propose[rindex], rthresh)
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ap = voc_ap(recall, propose)
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aps[setting_id] = ap
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tb = datetime.datetime.now()
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#print('high score count:', high_score_count)
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#print('high score fp count:', high_score_fp_count)
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print('%s cost %.4f seconds, ap: %.5f'%(settings[setting_id], (tb-ta).total_seconds(), ap))
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return aps
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def get_widerface_gts(gt_path):
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facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list = get_gt_boxes(gt_path)
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event_num = len(event_list)
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settings = ['easy', 'medium', 'hard']
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setting_gts = [easy_gt_list, medium_gt_list, hard_gt_list]
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all_results = []
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for setting_id in range(3):
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results = {}
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gt_list = setting_gts[setting_id]
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count_face = 0
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# [hard, medium, easy]
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#pbar = tqdm.tqdm(range(event_num))
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#for i in pbar:
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for i in range(event_num):
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#pbar.set_description('Processing {}'.format(settings[setting_id]))
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event_name = str(event_list[i][0][0])
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img_list = file_list[i][0]
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sub_gt_list = gt_list[i][0]
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# img_pr_info_list = np.zeros((len(img_list), thresh_num, 2))
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gt_bbx_list = facebox_list[i][0]
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results[event_name] = {}
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for j in range(len(img_list)):
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gt_boxes = gt_bbx_list[j][0].astype('float').copy()
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gt_boxes[:,2] += gt_boxes[:,0]
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gt_boxes[:,3] += gt_boxes[:,1]
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keep_index = sub_gt_list[j][0].copy()
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#print ('keep_index', keep_index.shape)
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count_face += len(keep_index)
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if len(gt_boxes) == 0:
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results[event_name][str(img_list[j][0][0])] = np.empty( (0,4) )
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continue
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keep_index -= 1
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keep_index = keep_index.flatten()
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#ignore = np.zeros(gt_boxes.shape[0])
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#if len(keep_index) != 0:
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# ignore[keep_index-1] = 1
|
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#assert len(keep_index)>0
|
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#ignore = np.zeros(gt_boxes.shape[0], dtype=np.int)
|
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#if len(keep_index) != 0:
|
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# ignore[keep_index-1] = 1
|
|
#print('ignore:', len(ignore), len(np.where(ignore==1)[0]))
|
|
#pred_sort_idx= np.argsort(pred_info[:,4])
|
|
#pred_info = pred_info[pred_sort_idx][::-1]
|
|
#print ('pred_info', pred_info[:20, 4])
|
|
#exit()
|
|
#if setting_id==2 and len(keep_index)<gt_boxes.shape[0]:
|
|
# print(gt_boxes.shape, keep_index.shape)
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|
|
gt_boxes = np_round(gt_boxes)[keep_index,:]
|
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|
|
results[event_name][str(img_list[j][0][0])] = gt_boxes
|
|
all_results.append(results)
|
|
return all_results
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if __name__ == '__main__':
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|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('-p', '--pred', default='')
|
|
parser.add_argument('-g', '--gt', default='./ground_truth/')
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|
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args = parser.parse_args()
|
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evaluation(args.pred, args.gt)
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