demo.py

# --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import _init_paths
import os
import sys
import numpy as np
import argparse
import pprint
from glob import glob
import time
import cv2
import _pickle as cPickle
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim

import torchvision.transforms as transforms
import torchvision.datasets as dset
from scipy.misc import imread
from roi_data_layer.roidb import combined_roidb
from roi_data_layer.roibatchLoader import roibatchLoader
from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
from model.rpn.bbox_transform import clip_boxes
from model.roi_layers import nms
from model.rpn.bbox_transform import bbox_transform_inv
from model.utils.net_utils import save_net, load_net, vis_detections
from model.utils.blob import im_list_to_blob
from model.faster_rcnn.vgg16 import vgg16
from model.faster_rcnn.resnet import resnet
import pdb

try:
    xrange  # Python 2
except NameError:
    xrange = range  # Python 3


def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
    parser.add_argument('--dataset', dest='dataset',
                        help='training dataset',
                        default='wider_face', type=str)
    parser.add_argument('--cfg', dest='cfg_file',
                        help='optional config file',
                        default='cfgs/vgg16.yml', type=str)
    parser.add_argument('--net', dest='net',
                        help='vgg16, res50, res101, res152',
                        default='vgg16', type=str)
    parser.add_argument('--set', dest='set_cfgs',
                        help='set config keys', default=None,
                        nargs=argparse.REMAINDER)
    parser.add_argument('--load_dir', dest='load_dir',
                        help='directory to load models', default="output",
                        nargs=argparse.REMAINDER)
    parser.add_argument('--image_dir', dest='image_dir',
                        help='directory to load images for demo', default="images",
                        nargs=argparse.REMAINDER)
    parser.add_argument('--out_dir', dest='out_dir',
                        help='directory to save bboxes for demo',
                        default="/root/src/face-faster-rcnn.pytorch/data/WIDER2015/eval_tools/pred",
                        nargs=argparse.REMAINDER)
    parser.add_argument('--mGPUs', dest='mGPUs',
                        help='whether use multiple GPUs',
                        action='store_true')
    parser.add_argument('--cag', dest='class_agnostic',
                        help='whether perform class_agnostic bbox regression',
                        action='store_true')
    parser.add_argument('--parallel_type', dest='parallel_type',
                        help='which part of model to parallel, 0: all, 1: model before roi pooling',
                        default=0, type=int)
    parser.add_argument('--checksession', dest='checksession',
                        help='checksession to load model',
                        default=1, type=int)
    parser.add_argument('--checkepoch', dest='checkepoch',
                        help='checkepoch to load network',
                        default=19, type=int)
    parser.add_argument('--checkpoint', dest='checkpoint',
                        help='checkpoint to load network',
                        default=25759, type=int)
    parser.add_argument('--bs', dest='batch_size',
                        help='batch_size',
                        default=1, type=int)
    parser.add_argument('--vis', dest='vis',
                        help='visualization mode',
                        action='store_true')
    args = parser.parse_args()
    return args


lr = cfg.TRAIN.LEARNING_RATE
momentum = cfg.TRAIN.MOMENTUM
weight_decay = cfg.TRAIN.WEIGHT_DECAY


def export_bboxes(image_path, bboxes, bboxes_path):
    with open(bboxes_path, 'w') as f:
        f.write('{}\n{}\n'.format(image_path, len(bboxes)))
        for box in bboxes:
            l, t, r, b = box[:4]
            score = box[4]
            f.write('{:.4f} {:.4f} {:.4f} {:.4f} {:.4f}\n'.format(l, t, r - l, b - t, score))


def _get_image_blob(im):
    """Converts an image into a network input.
    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)
    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 = []

    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)

    # Create a blob to hold the input images
    blob = im_list_to_blob(processed_ims)

    return blob, np.array(im_scale_factors)


if __name__ == '__main__':

    args = parse_args()

    print('Called with args:')
    print(args)

    args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '20']

    if args.cfg_file is not None:
        cfg_from_file(args.cfg_file)
    if args.set_cfgs is not None:
        cfg_from_list(args.set_cfgs)

    print('Using config:')
    pprint.pprint(cfg)
    np.random.seed(cfg.RNG_SEED)

    # train set
    # -- Note: Use validation set and disable the flipped to enable faster loading.

    input_dir = args.load_dir + "/" + args.net + "/" + args.dataset
    if not os.path.exists(input_dir):
        raise Exception('There is no input directory for loading network from ' + input_dir)
    load_name = os.path.join(input_dir,
                             'faster_rcnn_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, args.checkpoint))

    pascal_classes = np.asarray(['__background__', 'face'])

    # initialize the network here.
    if args.net == 'vgg16':
        fasterRCNN = vgg16(pascal_classes, pretrained=False, class_agnostic=args.class_agnostic)
    elif args.net == 'res101':
        fasterRCNN = resnet(pascal_classes, 101, pretrained=False, class_agnostic=args.class_agnostic)
    elif args.net == 'res50':
        fasterRCNN = resnet(pascal_classes, 50, pretrained=False, class_agnostic=args.class_agnostic)
    elif args.net == 'res152':
        fasterRCNN = resnet(pascal_classes, 152, pretrained=False, class_agnostic=args.class_agnostic)
    else:
        print("network is not defined")
        pdb.set_trace()

    fasterRCNN.create_architecture()

    print("load checkpoint %s" % (load_name))
    checkpoint = torch.load(load_name)
    fasterRCNN.load_state_dict(checkpoint['model'])
    if 'pooling_mode' in checkpoint.keys():
        cfg.POOLING_MODE = checkpoint['pooling_mode']

    print('load model successfully!')

    # pdb.set_trace()

    print("load checkpoint %s" % (load_name))

    # initilize the tensor holder here.
    im_data = torch.FloatTensor(1)
    im_info = torch.FloatTensor(1)
    num_boxes = torch.LongTensor(1)
    gt_boxes = torch.FloatTensor(1)

    # ship to cuda
    im_data = im_data.cuda()
    im_info = im_info.cuda()
    num_boxes = num_boxes.cuda()
    gt_boxes = gt_boxes.cuda()
    
    im_data = Variable(im_data)
    im_info = Variable(im_info)
    num_boxes = Variable(num_boxes)
    gt_boxes = Variable(gt_boxes)

    cfg.CUDA = True
    fasterRCNN.cuda()
    fasterRCNN.eval()

    start = time.time()
    max_per_image = 100
    thresh = 0.50
    vis = True

    imglist = os.listdir(args.image_dir)
    num_images = len(imglist)

    print('Loaded Photo: {} images.'.format(num_images))

    for i in range(num_images):

        # Load the demo image
        im_file = imglist[i]
        im_in = np.array(imread(os.path.join(args.image_dir, im_file)))
        if len(im_in.shape) == 2:
            im_in = im_in[:, :, np.newaxis]
            im_in = np.concatenate((im_in, im_in, im_in), axis=2)
        # rgb -> bgr
        im = im_in[:, :, ::-1]

        blobs, im_scales = _get_image_blob(im)
        assert len(im_scales) == 1, "Only single-image batch implemented"
        im_blob = blobs
        im_info_np = np.array([[im_blob.shape[1], im_blob.shape[2], im_scales[0]]], dtype=np.float32)

        im_data_pt = torch.from_numpy(im_blob)
        im_data_pt = im_data_pt.permute(0, 3, 1, 2)
        im_info_pt = torch.from_numpy(im_info_np)

        with torch.no_grad():
            im_data.resize_(im_data_pt.size()).copy_(im_data_pt)
            im_info.resize_(im_info_pt.size()).copy_(im_info_pt)
            gt_boxes.resize_(1, 1, 5).zero_()
            num_boxes.resize_(1).zero_()

        # pdb.set_trace()
        det_tic = time.time()

        rois, cls_prob, bbox_pred, \
        rpn_loss_cls, rpn_loss_box, \
        RCNN_loss_cls, RCNN_loss_bbox, \
        rois_label = fasterRCNN(im_data, im_info, gt_boxes, num_boxes)

        scores = cls_prob.data
        boxes = rois.data[:, :, 1:5]

        if cfg.TEST.BBOX_REG:
            # Apply bounding-box regression deltas
            box_deltas = bbox_pred.data
            if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
                # Optionally normalize targets by a precomputed mean and stdev
                if args.class_agnostic:
                    box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
                                 + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
                    box_deltas = box_deltas.view(1, -1, 4)
                else:
                    box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
                                 + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
                    box_deltas = box_deltas.view(1, -1, 4 * len(pascal_classes))

            pred_boxes = bbox_transform_inv(boxes, box_deltas, 1)
            pred_boxes = clip_boxes(pred_boxes, im_info.data, 1)
        else:
            # Simply repeat the boxes, once for each class
            pred_boxes = np.tile(boxes, (1, scores.shape[1]))

        pred_boxes /= im_scales[0]

        scores = scores.squeeze()
        pred_boxes = pred_boxes.squeeze()
        det_toc = time.time()
        detect_time = det_toc - det_tic
        misc_tic = time.time()
        if vis:
            im2show = np.copy(im)
        for j in xrange(1, len(pascal_classes)):
            cls_dets = []
            inds = torch.nonzero(scores[:, j] > thresh).view(-1)
            # if there is det
            if inds.numel() > 0:
                cls_scores = scores[:, j][inds]
                _, order = torch.sort(cls_scores, 0, True)
                if args.class_agnostic:
                    cls_boxes = pred_boxes[inds, :]
                else:
                    cls_boxes = pred_boxes[inds][:, j * 4:(j + 1) * 4]

                cls_dets = torch.cat((cls_boxes, cls_scores.unsqueeze(1)), 1)
                cls_dets = cls_dets[order]
                keep = nms(cls_boxes[order, :], cls_scores[order], cfg.TEST.NMS)
                cls_dets = cls_dets[keep.view(-1).long()]
                if vis:
                    im2show = vis_detections(im2show, pascal_classes[j], cls_dets.cpu().numpy(), 0.5)
            bboxes_path = os.path.join(args.out_dir, im_file[32:-4] + '.txt')

        misc_toc = time.time()
        nms_time = misc_toc - misc_tic

        print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s'.format(i + 1, num_images, detect_time, nms_time))

        if vis:
            # cv2.imshow('test', im2show)
            # cv2.waitKey(0)
            result_path = os.path.join(args.image_dir, imglist[i][:-4] + "_det.jpg")
            cv2.imwrite(result_path, im2show)