def crop_outdoorRGB(image_path, image_size, margin, gpu_memory_fraction):
   # 1、获取数据
    dataset = list_all_files(image_path)

    with tf.Graph().as_default():
        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
        with sess.as_default():
            pnet, rnet, onet = detect_face.create_mtcnn(sess, None)

    minsize = 20 # minimum size of face
    threshold = [ 0.6, 0.7, 0.7 ]  # three steps's threshold
    factor = 0.709 # scale factor
    failedface_filename = 'D:/Desktop/outdoorfailedfacergb_filename.txt'

    with open(failedface_filename, "w") as text_file:
        nrof_images_total = 0
        nrof_successfully_aligned = 0

        for image_path in dataset:
            nrof_images_total += 1
            try:
                img = misc.imread(image_path)
            except (IOError, ValueError, IndexError) as e:
                errorMessage = '{}: {}'.format(image_path, e)
                print(errorMessage)
            else:
                if img.ndim<2: # 数据的维度<2 ，说明都不是一张图片
                    print('Unable to align "%s"' % image_path)
                    continue
                if img.ndim == 2: # 数据维度 = 2 ，灰度图，复制3次转化成rgb 3通道图片
                    img = function.to_rgb(img)
                img = img[:,:,0:3]

                # # 直方图均衡化
                # img = hil(img)

                # 检测
                # 人脸检测 bounding_boxes：表示边界框 形状为[n,5] 5对应x1,y1,x2,y2,score
                # _：人脸关键点坐标 形状为 [n,10]
                bounding_boxes, landmark = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
                nrof_faces = bounding_boxes.shape[0]
                # bbox = bounding_boxes.tolist()
                # bbox = [y for x in bbox for y in x]
                # landmark = landmark.tolist()
                # landmark = [y for x in landmark for y in x]
                # dict_save[image_path[len(DATAPATH):].split('.')[0]] = (bbox, landmark)\
                #                     if (nrof_faces != 0) else (None,None,None)
                if nrof_faces>0: # 检测出的框>0，说明检测到了 如果图片中只有一个人的话，nrof_faces = 1
                    # [n,4] 人脸框
                    nrof_successfully_aligned += 1
                    det = bounding_boxes[:,0:4]
                    # 保存所有人脸框
                    det_arr = []
                    img_size = np.asarray(img.shape)[0:2]
                    if nrof_faces>1:
                    #     '''
                    #     如果检测出来很多张脸，但是要选一个最大的，采用的方法如下：
                    #     用人脸框大小-偏移平方的2倍，得到的结果那个大就选哪个
                    #     即人脸框越大越好，偏移量越小越好
                    #     '''
                    #     # det [n,0:4]是矩阵
                        bounding_box_size = (det[:,2]-det[:,0])*(det[:,3]-det[:,1]) # (x2-x1)*(y2-y1) 人脸框大小
                        img_center = img_size / 2 # 原图片中心
                        offsets = np.vstack([ (det[:,0]+det[:,2])/2-img_center[1], (det[:,1]+det[:,3])/2-img_center[0] ])
                        offset_dist_squared = np.sum(np.power(offsets,2.0),0)
                        index = np.argmax(bounding_box_size-offset_dist_squared*2.0) # some extra weight on the centering
                        det_arr.append(det[index,:])
                        bounding_boxes_using = bounding_boxes[index,:]
                        landmark_using = landmark[index,:]
                    else:
                        bounding_boxes_using = bounding_boxes
                        landmark_using = landmark

                    bbox = bounding_boxes_using.tolist()
                    # bbox = [y for x in bbox for y in x]
                    landmark = landmark_using.tolist()
                    # landmark = [y for x in landmark for y in x]
                    dict_save[image_path[len(DATAPATH):].split('.')[0]] = (bbox, landmark)

                else: # 没有检测到
                    dict_save[image_path[len(DATAPATH):].split('.')[0]] = (None,None,None)
                    print('Unable to align "%s"' % image_path)
                    text_file.write('%s\n' % (image_path))
    path_save = os.path.join('D:/Desktop/Outdoor', "outdoorrgbdetect.txt")
    with open(path_save,'w') as f:
        f.write(str(dict_save))
        f.write('\n')
    print('Total number of images: %d' % nrof_images_total)
    print('Number of successfully aligned images: %d' % nrof_successfully_aligned)