版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/majinlei121/article/details/78927418
由于Faster-rcnn里的计算mAP程序里面有很多嵌套，移植到自己的卷积网络框架下很麻烦，所以把这些嵌套都整合起来方便使用，整合之后的程序只包括test_net.py和voc_eval.py
下面是test_net.py
import _init_paths
from config import cfg
import caffe
import time, os, sys
from caffeWrapper.timer import Timer
import cv2
import numpy as np
from datasets.bbox_transform import clip_boxes, bbox_transform_inv ##这两个函数需要自己import进来
from nms.nms_wrapper import nms
import cPickle
import uuid
#import get_voc_results_file_template, im_detect
from voc_eval import voc_eval
import datetime
def get_voc_results_file_template(cls):##这个函数也改了一下
#comp_id = ('comp4' + '_' + str(uuid.uuid4()))
date = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')##这里把原来的编码名称改为日期（年-月-日-时-分-秒），方便查看
filename = date + '_det_' + 'test' + cls + '.txt'
path = os.path.join(save_prob_path, filename)
return path
def im_detect(net, im):
"""Detect object classes in an image given object proposals.
Arguments:
net (caffe.Net): Fast R-CNN network to use
im (ndarray): color image to test (in BGR order)
Returns:
scores (ndarray): R x K array of object class scores (K includes
background as object category 0)
boxes (ndarray): R x (4*K) array of predicted bounding boxes
"""
blobs = {'data' : None, 'rois' : None}
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
im_shape = im_orig.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
processed_ims = []
im_scale_factors = []
##这里图片都是一样大小
# for target_size in cfg.TEST.SCALES:
# im_scale = float(target_size) / float(im_size_min)
# # Prevent the biggest axis from being more than MAX_SIZE
# if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
# im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
# im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
# interpolation=cv2.INTER_LINEAR)
# im_scale_factors.append(im_scale)
# processed_ims.append(im)
im_scale = 1.0
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
im_scale_factors.append(im_scale)
processed_ims.append(im)
max_shape = np.array([imn.shape for imn in processed_ims]).max(axis=0)
num_images = len(processed_ims)
blob = np.zeros((num_images, max_shape[0], max_shape[1], 3),
dtype=np.float32)
for i in xrange(num_images):
imn = processed_ims[i]
blob[i, 0:imn.shape[0], 0:imn.shape[1], :] = imn
# Move channels (axis 3) to axis 1
# Axis order will become: (batch elem, channel, height, width)
channel_swap = (0, 3, 1, 2)
blob = blob.transpose(channel_swap)
blobs['data'] = blob
im_scales = np.array(im_scale_factors)
im_blob = blobs['data']
blobs['im_info'] = np.array([[im_blob.shape[2], im_blob.shape[3], im_scales[0]]],dtype=np.float32)
# reshape network inputs
net.blobs['data'].reshape(*(blobs['data'].shape))
net.blobs['im_info'].reshape(*(blobs['im_info'].shape))
# do forward
forward_kwargs = {'data': blobs['data'].astype(np.float32, copy=False)}
forward_kwargs['im_info'] = blobs['im_info'].astype(np.float32, copy=False)
blobs_out = net.forward(**forward_kwargs)
assert len(im_scales) == 1, "Only single-image batch implemented"
rois = net.blobs['rois'].data.copy()
# unscale back to raw image space
boxes = rois[:, 1:5] / im_scales[0]
scores = blobs_out['cls_prob']
box_deltas = blobs_out['bbox_pred']
pred_boxes = bbox_transform_inv(boxes, box_deltas)
pred_boxes = clip_boxes(pred_boxes, im.shape)
return scores, pred_boxes
file_path = 'VOC2007'
test_file = '/dataset/test.txt'
file_path_img = 'VOC2007/JPEGImages'
save_prob_path = 'VOC2007/output' ##生成的结果文件都保存在output里，包括detections.pkl，class_pr.pkl，和txt文件
test_prototxt = 'test.prototxt'
weight = 'vgg16.caffemodel'
thresh = 0.05
max_per_image = 100
num_classes = 2
Classes = ('__background__', 'ship')##这是二分类
with open(test_file) as f:
image_index = [x.strip() for x in f.readlines()]
caffe.set_mode_gpu()
caffe.set_device(0)
net = caffe.Net(test_prototxt, weight, caffe.TEST)
net.name = os.path.splitext(os.path.basename(weight))[0]
num_images = len(image_index)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(num_classes)]
# timers
_t = {'im_detect' : Timer(), 'misc' : Timer()}
for i in xrange(num_images):
image_path = os.path.join(file_path_img, image_index[i] + '.jpg')
im = cv2.imread(image_path)
_t['im_detect'].tic()
scores, boxes = im_detect(net, im)
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the background class
for j in xrange(1, num_classes):
inds = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j*4:(j+1)*4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
all_boxes[j][i] = cls_dets
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
image_scores = np.hstack([all_boxes[j][i][:, -1]
for j in xrange(1, num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in xrange(1, num_classes):
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
_t['misc'].toc()
print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time)
if not os.path.exists(save_prob_path):
os.mkdir(save_prob_path)
det_file = os.path.join(save_prob_path, 'detections.pkl')
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
for cls_ind, cls in enumerate(Classes):
if cls == '__background__':
continue
print 'Writing {} VOC results file'.format(cls)
filename = get_voc_results_file_template(cls)
if not os.path.exists(filename):
os.mknod(filename)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in xrange(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index, dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
annopath = os.path.join(file_path, 'Annotations', '{:s}.xml')
imagesetfile = os.path.join(file_path, 'ImageSets', 'Main', 'test.txt')
cachedir = os.path.join(save_prob_path)
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = True #True
print 'VOC07 metric? ' + ('Yes' if use_07_metric else 'No')
for i, cls in enumerate(Classes):
if cls == '__background__':
continue
#filename = get_voc_results_file_template(cls)
rec, prec, ap = voc_eval(
filename, annopath, imagesetfile, cls, cachedir, ovthresh = 0.5,
use_07_metric = use_07_metric)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
with open(os.path.join(save_prob_path, cls + '_pr.pkl'), 'w') as f:
cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')接下来是voc_eval.py
# --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Bharath Hariharan
# --------------------------------------------------------
import xml.etree.ElementTree as ET
import os
import cPickle
import numpy as np
def parse_rec(filename):
""" Parse a PASCAL VOC xml file """
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = obj.find('name').text
#obj_struct['pose'] = obj.find('pose').text
#obj_struct['truncated'] = int(obj.find('truncated').text)
obj_struct['difficult'] = int(obj.find('difficult').text)
bbox = obj.find('bndbox')
obj_struct['bbox'] = [int(bbox.find('xmin').text),
int(bbox.find('ymin').text),
int(bbox.find('xmax').text),
int(bbox.find('ymax').text)]
objects.append(obj_struct)
return objects
def voc_ap(rec, prec, use_07_metric=False):
""" ap = voc_ap(rec, prec, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:False).
"""
if use_07_metric:
# 11 point metric
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap = ap + p / 11.
else:
# 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 voc_eval(detpath,
annopath,
imagesetfile,
classname,
cachedir,
ovthresh=0.5,
use_07_metric=False):
"""rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
classname,
[ovthresh],
[use_07_metric])
Top level function that does the PASCAL VOC evaluation.
detpath: Path to detections
detpath.format(classname) should produce the detection results file.
annopath: Path to annotations
annopath.format(imagename) should be the xml annotations file.
imagesetfile: Text file containing the list of images, one image per line.
classname: Category name (duh)
cachedir: Directory for caching the annotations
[ovthresh]: Overlap threshold (default = 0.5)
[use_07_metric]: Whether to use VOC07's 11 point AP computation
(default False)
"""
# assumes detections are in detpath.format(classname)
# assumes annotations are in annopath.format(imagename)
# assumes imagesetfile is a text file with each line an image name
# cachedir caches the annotations in a pickle file
# first load gt
if not os.path.isdir(cachedir):
os.mkdir(cachedir)
cachefile = os.path.join(cachedir, 'annots.pkl')
# read list of images
with open(imagesetfile, 'r') as f:
lines = f.readlines()
imagenames = [x.strip() for x in lines]
if not os.path.isfile(cachefile):
# load annots
recs = {}
for i, imagename in enumerate(imagenames):
recs[imagename] = parse_rec(annopath.format(imagename))
if i % 100 == 0:
print 'Reading annotation for {:d}/{:d}'.format(
i + 1, len(imagenames))
# save
print 'Saving cached annotations to {:s}'.format(cachefile)
with open(cachefile, 'w') as f:
cPickle.dump(recs, f)
else:
# load
with open(cachefile, 'r') as f:
recs = cPickle.load(f)
# extract gt objects for this class
class_recs = {}
npos = 0
for imagename in imagenames:
R = [obj for obj in recs[imagename] if obj['name'] == classname]
bbox = np.array([x['bbox'] for x in R])
difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
det = [False] * len(R)
npos = npos + sum(~difficult)
class_recs[imagename] = {'bbox': bbox,
'difficult': difficult,
'det': det}
# read dets
detfile = detpath
with open(detfile, 'r') as f:
lines = f.readlines()
splitlines = [x.strip().split(' ') for x in lines]
image_ids = [x[0] for x in splitlines]
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
# sort by confidence
sorted_ind = np.argsort(-confidence)
sorted_scores = np.sort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
# go down dets and mark TPs and FPs
nd = len(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
for d in range(nd):
R = class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
ovmax = -np.inf
BBGT = R['bbox'].astype(float)
if BBGT.size > 0:
# compute overlaps
# intersection
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(BBGT[:, 2] - BBGT[:, 0] + 1.) *
(BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
overlaps = inters / uni
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if ovmax > ovthresh:
if not R['difficult'][jmax]:
if not R['det'][jmax]:
tp[d] = 1.
R['det'][jmax] = 1
else:
fp[d] = 1.
else:
fp[d] = 1.
# compute precision recall
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(npos)
# avoid divide by zero in case the first detection matches a difficult
# ground truth
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = voc_ap(rec, prec, use_07_metric)
return rec, prec, ap