# --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import numpy as np
try:
import cPickle as pickle
except ImportError:
import pickle
import os
import math
from utils.timer import Timer
from utils.blob import im_list_to_blob
from model.config import cfg, get_output_dir
from model.bbox_transform import clip_boxes, bbox_transform_inv
from model.nms_wrapper import nms
'''用于测试的时候调用模型进行测试,在tools/test_net.py调用'''
def _get_image_blob(im):
"""Converts an image into a network input.
将图片转换成网络的输入格式
Arguments:
im (ndarray): a color image in BGR order
Returns:
blob (ndarray): a data blob holding an image pyramid
im_scale_factors (list): list of image scales (relative to im) used
in the image pyramid
"""
im_orig = im.astype(np.float32, copy=True)
# 去均值
im_orig -= cfg.PIXEL_MEANS
# 提取最大、最小边
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 = []
for target_size in cfg.TEST.SCALES:
# 缩放比例
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)
# Create a blob to hold the input images
# 将压缩的图像转化成blob，格式为numpy矩阵
blob = im_list_to_blob(processed_ims)
return blob, np.array(im_scale_factors)
def _get_blobs(im): # 将blob封装成一个简单的方法
"""Convert an image and RoIs within that image into network inputs."""
blobs = {}
blobs['data'], im_scale_factors = _get_image_blob(im)
return blobs, im_scale_factors
def _clip_boxes(boxes, im_shape):
"""Clip boxes to image boundaries."""
# 对超出图像范围的boxes进行裁剪
# x1 >= 0
boxes[:, 0::4] = np.maximum(boxes[:, 0::4], 0)
# y1 >= 0
boxes[:, 1::4] = np.maximum(boxes[:, 1::4], 0)
# x2 < im_shape[1]
boxes[:, 2::4] = np.minimum(boxes[:, 2::4], im_shape[1] - 1)
# y2 < im_shape[0]
boxes[:, 3::4] = np.minimum(boxes[:, 3::4], im_shape[0] - 1)
return boxes
def _rescale_boxes(boxes, inds, scales):
"""Rescale boxes according to image rescaling."""
# 将图像缩放为原来的大小
for i in range(boxes.shape[0]):
boxes[i, :] = boxes[i, :] / scales[int(inds[i])]
return boxes
# 调用训练好的参数以及网络结构进行训练，返回目标检测的类别和坐标信息
def im_detect(sess, net, im):
blobs, im_scales = _get_blobs(im)
assert len(im_scales) == 1, "Only single-image batch implemented"
im_blob = blobs['data']
blobs['im_info'] = np.array([im_blob.shape[1], im_blob.shape[2], im_scales[0]], dtype=np.float32)
# 测试网络
_, scores, bbox_pred, rois = net.test_image(sess, blobs['data'], blobs['im_info'])
# boxes：rpn层预测的区域
boxes = rois[:, 1:5] / im_scales[0]
# scores：回归层的softmax值
scores = np.reshape(scores, [scores.shape[0], -1])
# bbox_pred：回归层的boxes预测坐标
bbox_pred = np.reshape(bbox_pred, [bbox_pred.shape[0], -1])
# 默认true
if cfg.TEST.BBOX_REG:
# Apply bounding-box regression deltas
box_deltas = bbox_pred
# 从偏移量映射回真实坐标 [dx,dy,dw,dh]->[xmin,ymin,xmax,ymax]
pred_boxes = bbox_transform_inv(boxes, box_deltas)
pred_boxes = _clip_boxes(pred_boxes, im.shape)
else:
# Simply repeat the boxes, once for each class
pred_boxes = np.tile(boxes, (1, scores.shape[1]))
return scores, pred_boxes
def apply_nms(all_boxes, thresh):
"""Apply non-maximum suppression to all predicted boxes output by the
test_net method.
"""
num_classes = len(all_boxes)
num_images = len(all_boxes[0])
# shape：num_classes*num_images
nms_boxes = [[[] for _ in range(num_images)] for _ in range(num_classes)]
for cls_ind in range(num_classes): # 对于每一类
for im_ind in range(num_images): # 对于每一类的每一个检测结果应用nms
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# 保证xmax > xmin , ymax > ymin
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
inds = np.where((x2 > x1) & (y2 > y1))[0]
dets = dets[inds, :]
if dets == []:
continue
keep = nms(dets, thresh)
if len(keep) == 0:
continue
nms_boxes[cls_ind][im_ind] = dets[keep, :].copy()
# nms_boxes的shape : num_classes*num_images*len(keep)*5
return nms_boxes
# 主函数
def test_net(sess, net, imdb, weights_filename, max_per_image=100, thresh=0.):
# 随机数种子
np.random.seed(cfg.RNG_SEED)
"""Test a Fast R-CNN network on an image database."""
# 图片数量：test数据集
num_images = len(imdb.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 range(num_images)]
for _ in range(imdb.num_classes)]
# 保存模型的路径
output_dir = get_output_dir(imdb, weights_filename)
# timers 定义计时器
_t = {'im_detect': Timer(), 'misc': Timer()}
for i in range(num_images):
im = cv2.imread(imdb.image_path_at(i))
_t['im_detect'].tic() # 计时开始
# 调用im_detect对图像检测，返回类别和坐标信息
scores, boxes = im_detect(sess, net, im)
_t['im_detect'].toc() # 计时结束
_t['misc'].tic()
# skip j = 0, because it's the background class
for j in range(1, imdb.num_classes):
# 返回在类别j上得分大于阈值的横坐标
inds = np.where(scores[:, j] > thresh)[0]
# 满足阈值的行的j类别的得分
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j * 4:(j + 1) * 4]
# cls_dets=[xmin,ymin,xmax,ymax,scores]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
# nms
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
all_boxes[j][i] = cls_dets # 第i个图像的第j个类的所有检测结果，都保存在cls_dets中了
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
# num_classes*num_image*xum_classes 所有类别得分
image_scores = np.hstack([all_boxes[j][i][:, -1]
for j in range(1, imdb.num_classes)])
# 保留前max_per_image个框
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.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))
# 保存
det_file = os.path.join(output_dir, 'detections.pkl')
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
# 保存结果文件，并对检测结果调用_do_python_eval()计算AP，MAP
imdb.evaluate_detections(all_boxes, output_dir)
感谢WYX同志