#!/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__',
           'ship')
 
NETS = {'vgg16': ('VGG16',
                  'VGG16_faster_rcnn_final.caffemodel'),
        'zf': ('ZF',
                  'ZF_faster_rcnn_final.caffemodel'),
        'wyx': ('wyx','vgg_cnn_m_1024_faster_rcnn_iter_1000.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
 
    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]
 
        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 vis_detections_video(im, class_name, dets, thresh=0.5):
    """Draw detected bounding boxes."""
    global lastColor,frameRate
    inds = np.where(dets[:, -1] >= thresh)[0]
    if len(inds) == 0:
        return im
 
    for i in inds:
        bbox = dets[i, :4]
        score = dets[i, -1]
        cv2.rectangle(im,(bbox[0],bbox[1]),(bbox[2],bbox[3]),(0,0,255),2)
	cv2.rectangle(im,(int(bbox[0]),int(bbox[1]-20)),(int(bbox[0]+200),int(bbox[1])),(10,10,10),-1)
	cv2.putText(im,'{:s} {:.3f}'.format(class_name, score),(int(bbox[0]),int(bbox[1]-2)),cv2.FONT_HERSHEY_SIMPLEX,.75,(255,255,255))#,cv2.CV_AA)
 
    return im
 
 
 
def demo(net, im):
    """Detect object classes in an image using pre-computed object proposals."""
    global frameRate
    # Load the demo image
    #im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
    #im = cv2.imread(im_file)
 
    # 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])
    frameRate = 1.0/timer.total_time
    print "fps: " + str(frameRate)
    # 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_video(im, cls, dets, thresh=CONF_THRESH)
        cv2.putText(im,'{:s} {:.2f}'.format("FPS:", frameRate),(1750,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255))
        cv2.imshow(videoFilePath.split('/')[len(videoFilePath.split('/'))-1],im)
        cv2.waitKey(20)
 
 
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')   
    prototxt = '/home/yexin/py-faster-rcnn/models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_end2end/test.prototxt'
#    print 'see prototxt path{}'.format(prototxt)
 
 
 #   caffemodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models',
 #                             NETS[args.demo_net][1])
    caffemodel = '/home/yexin/py-faster-rcnn/output/faster_rcnn_end2end/voc_2007_trainval/vgg_cnn_m_1024_faster_rcnn_iter_100.caffemodel'
 
 
#    print '\n\nok'
 
    if not os.path.isfile(caffemodel):
        raise IOError(('{:s} not found.\nDid you run ./data/script/'
                       'fetch_faster_rcnn_models.sh?').format(caffemodel))
    print '\n\nok'
 
    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 '\n\nLoaded 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)
 
    videoFilePath = '/home/yexin/py-faster-rcnn/data/demo/test_1-3.mp4'
    videoCapture = cv2.VideoCapture(videoFilePath) 
    #success, im = videoCapture.read()
    while True :
        success, im = videoCapture.read() 
        demo(net, im)        
        if cv2.waitKey(10) & 0xFF == ord('q'):
            break
    videoCapture.release()
    cv2.destroyAllWindows()