py-faster-rcnn表示FDDB人の顔はFDDB上でテストするのに便利です


このプログラムはpy-faster-rcnn/tools/demoです.pyに基づいて修正された
プログラムの機能:訓練したcaffemodelを使って、FDDBの人の顔に対して表示を行って、それがFDDBの上でテストすることを容易にします
#!/usr/bin/env python 

# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

"""
Demo script showing detections in sample images.

See README.md for installation instructions before running.
"""

import _init_paths
from fast_rcnn.config import cfg
from fast_rcnn.test import im_detect
from fast_rcnn.nms_wrapper import nms
from utils.timer import Timer
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import caffe, os, sys, cv2
import argparse

#CLASSES = ('__background__',  #   +  
#           'aeroplane', 'bicycle', 'bird', 'boat',
#           'bottle', 'bus', 'car', 'cat', 'chair',
#           'cow', 'diningtable', 'dog', 'horse',
#           'motorbike', 'person', 'pottedplant',
#           'sheep', 'sofa', 'train', 'tvmonitor')

CLASSES = ('__background__','face') #    :face

NETS = {'vgg16': ('VGG16',
                  'VGG16_faster_rcnn_final.caffemodel'),
        'myvgg': ('VGG_CNN_M_1024',
                  'VGG_CNN_M_1024_faster_rcnn_final.caffemodel'),
        'zf': ('ZF',
                  'ZF_faster_rcnn_final.caffemodel'),
        'myzf': ('ZF',
                  'zf_rpn_stage1_iter_80000.caffemodel'),
}


def vis_detections(im, class_name, dets, thresh=0.5):
    """Draw detected bounding boxes."""
    inds = np.where(dets[:, -1] >= thresh)[0]
    if len(inds) == 0:
        return

    write_file.write(str(len(inds)) + '
') #add by zhipeng im = im[:, :, (2, 1, 0)] #fig, ax = plt.subplots(figsize=(12, 12)) #ax.imshow(im, aspect='equal') for i in inds: bbox = dets[i, :4] score = dets[i, -1] ########## add by zhipeng for write rectange to txt ######## write_file.write( "{} {} {} {} {}
".format(str(bbox[0]), str(bbox[1]), str(bbox[2] - bbox[0]), str(bbox[3] - bbox[1]), str(score))) #print "zhipeng, bbox:", bbox, "score:",score ########## add by zhipeng for write rectange to txt ######## '''ax.add_patch( plt.Rectangle((bbox[0], bbox[1]), bbox[2] - bbox[0], bbox[3] - bbox[1], fill=False, edgecolor='red', linewidth=3.5) ) ax.text(bbox[0], bbox[1] - 2, '{:s} {:.3f}'.format(class_name, score), bbox=dict(facecolor='blue', alpha=0.5), fontsize=14, color='white') ax.set_title(('{} detections with ' 'p({} | box) >= {:.1f}').format(class_name, class_name, thresh), fontsize=14) plt.axis('off') plt.tight_layout() plt.draw()''' def demo(net, image_name): """Detect object classes in an image using pre-computed object proposals.""" # Load the demo image #im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) im = cv2.imread(image_name) # Detect all object classes and regress object bounds timer = Timer() timer.tic() scores, boxes = im_detect(net, im) timer.toc() print ('Detection took {:.3f}s for ' '{:d} object proposals').format(timer.total_time, boxes.shape[0]) # Visualize detections for each class CONF_THRESH = 0.8 NMS_THRESH = 0.3 for cls_ind, cls in enumerate(CLASSES[1:]): cls_ind += 1 # because we skipped background cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] cls_scores = scores[:, cls_ind] dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32) keep = nms(dets, NMS_THRESH) dets = dets[keep, :] vis_detections(im, cls, dets, thresh=CONF_THRESH) def parse_args(): """Parse input arguments.""" parser = argparse.ArgumentParser(description='Faster R-CNN demo') parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]', default=0, type=int) parser.add_argument('--cpu', dest='cpu_mode', help='Use CPU mode (overrides --gpu)', action='store_true') parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]', choices=NETS.keys(), default='vgg16') args = parser.parse_args() return args if __name__ == '__main__': cfg.TEST.HAS_RPN = True # Use RPN for proposals args = parse_args() prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0], 'faster_rcnn_alt_opt', 'faster_rcnn_test.pt') caffemodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models', NETS[args.demo_net][1]) if not os.path.isfile(caffemodel): raise IOError(('{:s} not found.
Did you run ./data/script/' 'fetch_faster_rcnn_models.sh?').format(caffemodel)) if args.cpu_mode: caffe.set_mode_cpu() else: caffe.set_mode_gpu() caffe.set_device(args.gpu_id) cfg.GPU_ID = args.gpu_id net = caffe.Net(prototxt, caffemodel, caffe.TEST) print '

Loaded network {:s}'.format(caffemodel) # Warmup on a dummy image im = 128 * np.ones((300, 500, 3), dtype=np.uint8) for i in xrange(2): _, _= im_detect(net, im) '''im_names = ['000456.jpg', '000542.jpg', '001150.jpg', '001763.jpg', '004545.jpg']''' ########## add by zhipeng for write rectange to txt ######## #write_file_name = '/home/xiao/code/py-faster-rcnn-master/py-faster-rcnn/tools/detections/out.txt' #write_file = open(write_file_name, "w") ########## add by zhipeng for write rectange to txt ######## for current_file in range(1,11): #orginal range(1, 11) print 'Processing file ' + str(current_file) + ' ...' read_file_name = '/home/xiao/code/py-faster-rcnn-master/py-faster-rcnn/tools/FDDB-fold/FDDB-fold-' + str(current_file).zfill(2) + '.txt' write_file_name = '/home/xiao/code/py-faster-rcnn-master/py-faster-rcnn/tools/detections/fold-' + str(current_file).zfill(2) + '-out.txt' write_file = open(write_file_name, "w") with open(read_file_name, "r") as ins: array = [] for line in ins: array.append(line) # list of strings number_of_images = len(array) for current_image in range(number_of_images): if current_image % 10 == 0: print 'Processing image : ' + str(current_image) # load image and convert to gray read_img_name = '/home/xiao/code/py-faster-rcnn-master/py-faster-rcnn/data/FDDB/originalPics/' + array[current_image].rstrip() + '.jpg' write_file.write(array[current_image]) #add by zhipeng demo(net, read_img_name) write_file.close() '''for im_name in im_names: print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~' print 'Demo for data/demo/{}'.format(im_name) write_file.write(im_name + '
') #add by zhipeng demo(net, im_name)''' #write_file.close() # add by zhipeng,close file plt.show()