#以SSD的检测测试为例

def detetion(image_dir,weight,deploy,resolution=300):

    caffe.set_mode_gpu()

    net = caffe.Net(weight,deploy,caffe.TEST)

    transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})

    transformer.set_transpose('data',(2,0,1))

    transformer.set_mean('data', np.array([104, 117, 123]))  # mean pixel

    images = os.listdir(image_dir)

    target_dir = "det_results"

    if not os.path.exists(target_dir):

        os.mkdir(target_dir)

    for image in images:

        image_path = os.path.join(image_dir,image)

        target_path = os.path.join(target_dir,image)

        croped = cut(image_path,resolution)

        net.blobs['data'].reshape(1, 3, resolution, resolution)

        transformed_image = transformer.preprocess('data',croped)

        net.blobs['data'].data[...]=transformed_image

        start = time.time()

        net.forward()

        end  = time.time()

        print "Forward time is {} s.".format(int(end-start))

        out_put = net.blobs["detection_out"].data

        out_put = np.squeeze(out_put)

        # label,conf,xmin,ymin,xmax,ymax

        for box in out_put:

            conf = box[2]

            # if conf < 0.1:

            #     continue

            xmin = int(box[3]*resolution) if box[3] > 0 else 0

            ymin = int(box[4]*resolution) if box[4] > 0 else 0

            xmax = int(box[5]*resolution) if box[5] > 0 else 0

            ymax = int(box[6]*resolution) if box[6] > 0 else 0

            cv2.rectangle(croped,(xmin,ymin),(xmax,ymax),(0,255,0),1)

        cv2.imwrite(target_path,croped)

        print target_path