【OrangePi AIpro】开箱初体验以及OAK深度相机测试

5.AI应用样例体验

       在Orangepi AIPRO板卡系统中还内置了多个有趣的AI处理案例，大大降低了学习门槛。

       在人像分割场景中，Orangepi AIPRO可以通过训练好的模型轻松实现深度学习神经网络PortraitNet，（PortraitNet是实时人像分割模型，该模型可以在移动设备上有效且高效地运行，基于轻量级的 U 形架构，在训练阶段有两个辅助损失，而在测试阶段不需要额外的成本进行肖像推理。两个辅助损失是边界损失和一致性约束损失。前者提高了边界像素的精度，后者增强了复杂光照环境下的鲁棒性）。

      5.1. 打开内置Jupyter Notebook AI样例的操作很便捷，只需要下面两条命令；

cd samples/notebooks/
./start_notebook.sh

 5.2. 运行Jupyter Notebook AI样例

    5.3.  输出对比图像

class Seg(object):"""人像分割模型推理"""def __init__(self, model_path, model_width, model_height):self._model_path = model_pathself._model_width = model_widthself._model_height = model_heightself.device_id = 0self._dvpp = Noneself._model = Nonedef init(self):"""初始化相关资源"""# Initialize dvppself._dvpp = AclLiteImageProc()# Load modelself._model = AclLiteModel(self._model_path)return const.SUCCESS@utils.display_timedef pre_process(self, image):"""图片预处理"""image_dvpp = image.copy_to_dvpp()yuv_image = self._dvpp.jpegd(image_dvpp)resized_image = self._dvpp.resize(yuv_image,self._model_width, self._model_height)return resized_image     @utils.display_timedef inference(self, input_data):"""模型推理"""return self._model.execute(input_data)@utils.display_timedef post_process(self, infer_output, image_name):"""获取分割结果"""data = infer_output[0]vals = data.flatten()mask = np.clip((vals * 255), 0, 255)mask = mask.reshape(224, 224, 2)cv2.imwrite(os.path.join(MASK_DIR, image_name), mask[:, :, 0])return mask @utils.display_time
def background_replace(bg_path, ori_path, mask_path):"""将人像分割结果与背景图片结合"""background = cv2.imread(bg_path)height, width = background.shape[:2]ori_img = cv2.imread(ori_path)mask = cv2.imread(mask_path, 0)mask = mask / 255mask_resize = cv2.resize(mask, (width, height))ori_img = cv2.resize(ori_img, (width, height))mask_bg = np.repeat(mask_resize[..., np.newaxis], 3, 2)result = np.uint8(background * mask_bg + ori_img * (1 - mask_bg))cv2.imwrite(os.path.join(OUTPUT_DIR, os.path.basename(mask_path)), result)def main():"""推理主函数"""os.makedirs(OUTPUT_DIR, exist_ok=True)os.makedirs(MASK_DIR, exist_ok=True)acl_resource = AclLiteResource()acl_resource.init()seg = Seg(MODEL_PATH, MODEL_WIDTH, MODEL_HEIGHT)ret = seg.init()utils.check_ret("seg.init ", ret)images_list = [os.path.join(IMAGE_DIR, img)for img in os.listdir(IMAGE_DIR)if os.path.splitext(img)[1] in const.IMG_EXT]for image_file in images_list:image_name = os.path.basename(image_file)if image_name != 'background.jpg':print('====' + image_name + '====')# read imageimage = AclLiteImage(image_file)# Preprocess the pictureresized_image = seg.pre_process(image)# Inferenceresult = seg.inference([resized_image, ])# Post-processingmask = seg.post_process(result, image_name)# Fusion of segmented portrait and background imagebackground_replace(os.path.join(IMAGE_DIR, 'background.jpg'), \image_file, os.path.join(MASK_DIR, image_name))

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