Evaluate original squeezeDetPlus model on KITTI Benchmark about squeezedet HOT 12 OPEN

def image_demo():
  """Detect image."""

  with tf.Graph().as_default():
    # Load model
    mc = kitti_squeezeDetPlus_config()
    mc.BATCH_SIZE = 1
    # model parameters will be restored from checkpoint
    mc.LOAD_PRETRAINED_MODEL = False
    model = SqueezeDetPlus(mc, FLAGS.gpu)
    saver = tf.train.Saver(model.model_params)

    #  set model path
    FLAGS.checkpoint = r'/home/project/HumanDetection/squeezeDet_github/models/squeezeDetPlus/model.ckpt-95000'
    # set test data path
    basic_image_path = r'/home/dataSet/kitti/ori_data/left_image/testing/image_2/'
    list_path = r'/home/dataSet/kitti/ori_data/left_image/testing/test_list.txt' 
    write_result_path =r'/home/dataSet/kitti/ori_data/left_image/testing/run_out/'
    
    with open(list_path,'rt') as F_read_list:
        image_list_name = [x.strip() for x in F_read_list.readlines()]


    print ('image numbers:  ', len(image_list_name) )

    count_num = 0
    pedestrian_index = int(1)  ## for pedestrian index
    keep_score = 0.05  #  
    
    # write file format
    default_str_1 = 'Pedestrian -1 -1 -10'
    default_str_2 = '-1 -1 -1 -1000 -1000 -1000 -10'

    with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
    	saver.restore(sess, FLAGS.checkpoint)

    	for file_name in image_list_name:
    		read_full_name = basic_image_path + file_name
    		im = cv2.imread(read_full_name)
    		if im is None:
    			print (file_name, ' is empty!')
    			continue
                
               # 【【【【【【【【【new codes】】】】】】】】】
                ori_height, ori_width, _ = im.shape
                x_scale = float(ori_width) / mc.IMAGE_WIDTH
                y_scale = float(ori_height) / mc.IMAGE_HEIGHT
    		
                im = im.astype(np.float32, copy=False)
    		im = cv2.resize(im, (mc.IMAGE_WIDTH, mc.IMAGE_HEIGHT))
    		input_image = im - mc.BGR_MEANS   		     
                
                # Detect 
	        det_boxes, det_probs, det_class = sess.run(
	        	[model.det_boxes, model.det_probs, model.det_class],
	        	feed_dict={model.image_input:[input_image], model.keep_prob: 1.0})
	        
                # NMS  Filter
	        final_boxes, final_probs, final_class = model.filter_prediction(
	        	det_boxes[0], det_probs[0], det_class[0])
	        
	        ##  only keep high probablity pedestrian
	        keep_idx    = [idx for idx in range(len(final_probs)) \
	        if final_probs[idx] > keep_score]

	        final_boxes = [final_boxes[idx] for idx in keep_idx]
	        final_probs = [final_probs[idx] for idx in keep_idx]
	        final_class = [final_class[idx] for idx in keep_idx]

	        # -------------- write files -----------------------
	        F_w_one_by_one = open(write_result_path + file_name.replace('png', 'txt'), 'wt')
	        rect_num = final_class.count(pedestrian_index)
	        
                print ('count: ', count_num)
        	count_num+=1
        	
	        if rect_num==0:
                        F_w_one_by_one.close()
	        	continue

        	goal_index = [idx for idx,value in enumerate(final_class) if value==pedestrian_index]

        	for kk in goal_index:
        		box = final_boxes[kk]
        		
                        xmin = box[0] - box[2]/2.0
        		ymin = box[1] - box[3]/2.0
        		xmax = box[0] + box[2]/2.0
        		ymax = box[1] + box[3]/2.0
        		
                       # 【【【【【【【【【new codes】】】】】】】】】
                        xmin*=x_scale
                        ymin*=y_scale
                        xmax*=x_scale
                        ymax*=y_scale
        		
        		line_2 = default_str_1 + ' '+ str(xmin) + ' '+ str(ymin) + ' '+ \
				str(xmax) + ' '+ str(ymax)+' '+ default_str_2 +' ' + str(final_probs[kk])+'\n'

        		F_w_one_by_one.write(line_2)
	         
	        F_w_one_by_one.close()


def main(argv=None):
    image_demo()


if __name__ == '__main__':
    tf.app.run()