嵌入式电脑|Paddle Inference——基于python API在Jetson上部署PaddleSeg模型 paddle|python|paddlepaddle|树莓派和

文章目录

- 环境准备
- 部署
- - 主程序
  - 预测结果

环境准备

paddlepaddle-gpu
paddleseg

pip3 install numpy==1.13.3 pip3 install paddleseg --no-dependencies pip3 install filelock pyyaml visualdl>=2.0.0 yapf==0.26.0 tqdm prettytable pre-commit flake8

部署

import cv2 import numpy as np import yaml import random import os import codecs from paddle.inference import Config from paddle.inference import PrecisionType from paddle.inference import create_predictorimport paddleseg.transforms as T from paddleseg.cvlibs import manager from paddleseg.utils import get_sys_env, logger, get_image_list from paddleseg.utils.visualize import get_pseudo_color_mapclass DeployYmlConfig: def __init__(self, path): yml_path = os.path.join(path, "deploy.yaml") with codecs.open(yml_path, 'r', 'utf-8') as file: self.dic = yaml.load(file)self._transforms = self._load_transforms( self.dic['Deploy']['transforms']) self._dir = path@property def transforms(self): print("transforms") return self._transforms@property def model_file(self): return os.path.join(self._dir, self.dic['Deploy']['model'])@property def params_file(self): return os.path.join(self._dir, self.dic['Deploy']['params'])def _load_transforms(self, t_list): com = manager.TRANSFORMS transforms = [] for t in t_list: ctype = t.pop('type') transforms.append(com[ctype](**t))return T.Compose(transforms)class Paddle_Seg: def __init__(self, model_folder_dir, infer_img_size=224, use_gpu=False, gpu_memory=500, use_tensorrt=False, precision_mode="fp32"): self.model_folder_dir = model_folder_dir self.infer_img_size = infer_img_size# 模型预测的输入图像尺寸 self.use_gpu = use_gpu# 是否使用GPU，默认False self.gpu_memory = gpu_memory# GPU的显存，默认500 self.use_tensorrt = use_tensorrt# 是否使用TensorRT，默认False self.precision = precision_mode# TensorRT的precision_mode为"fp16"、"fp32"、"int8"def init(self,camera_width=640,camera_height=480): img = np.zeros(shape=(int(camera_height), int(camera_width),3),dtype="float32") # 从deploy.yml中读出模型相关信息 self.cfg = DeployYmlConfig(self.model_folder_dir) # 初始化预测模型 self.predictor = self.predict_config() self.color_list = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) for i in range(10)]def predict_config(self): # ——————————————模型配置、预测相关函数————————————————— # # 根据预测部署的实际情况，设置Config config = Config() # 读取模型文件 config.set_prog_file(self.cfg.model_file) config.set_params_file(self.cfg.params_file) precision_map = { "int8": PrecisionType.Int8, "fp16": PrecisionType.Half, "fp32": PrecisionType.Float32} if self.use_gpu == True: config.enable_use_gpu(self.gpu_memory, 0) if self.use_tensorrt == True: if self.precision == "int8": use_calib_mode = True use_static = True if self.precision == "fp16": use_calib_mode = False use_static = True else: use_calib_mode = False use_static = True config.enable_tensorrt_engine(workspace_size=1 << 30, precision_mode=precision_map[self.precision], max_batch_size=1, min_subgraph_size=50, use_static=use_static, use_calib_mode=use_calib_mode) print("----------------------------------------------") print("RUNNING CONFIG") print("----------------------------------------------") print("Model input size: {}".format([self.infer_img_size, self.infer_img_size, 3])) # 0 print("Use GPU is: {}".format(config.use_gpu())) # True print("GPU device id is: {}".format(config.gpu_device_id())) # 0 print("Init mem size is: {}".format(config.memory_pool_init_size_mb())) # 100 print("Use TensorRT: {}".format(self.use_tensorrt)) # 0 print("Precision mode: {}".format(precision_map[self.precision])) # 0 print("----------------------------------------------") # 可以设置开启IR优化、开启内存优化 config.switch_ir_optim() config.enable_memory_optim() predictor = create_predictor(config) return predictordef preprocess(self, img): data = https://www.it610.com/article/np.array([self.cfg.transforms(img)[0]]) return datadef predict(self, predictor, data): input_names = predictor.get_input_names() input_handle = predictor.get_input_handle(input_names[0]) output_names = predictor.get_output_names() output_handle = predictor.get_output_handle(output_names[0]) input_handle.reshape(data.shape) input_handle.copy_from_cpu(data) # 执行Predictor predictor.run() # 获取输出 results = [] results = output_handle.copy_to_cpu() # 获取输出 return resultsdef infer(self, img): data = self.preprocess(img) result = self.predict(self.predictor, data) return resultdef draw_img(self, res, img): res = res[0].astype("int8") mask = cv2.merge([res, res*255, res]) img = cv2.addWeighted(img, 1, mask, 0.5, 0) return imgdef save_img(self, result, save_name='res.png'): result_img = get_pseudo_color_map(result[0]) result_img.save(save_name)

主程序

if __name__ == "__main__": ################### model_folder_dir="../../../big_model/deeplab1.4" infer_img_size=512 use_gpu=True gpu_memory=500 use_tensorrt=True precision_mode="fp16" ################### paddle_seg = Paddle_Seg(model_folder_dir=model_folder_dir, infer_img_size=infer_img_size, use_gpu=use_gpu, gpu_memory=gpu_memory, use_tensorrt=use_tensorrt, precision_mode=precision_mode) paddle_seg.init(928, 800) img = cv2.imread("61.jpg")from color_dist import * color="chin_yellow_blind_path" # HSV color_lower = np.array(color_dist[color]["Lower"], np.uint8) color_upper = np.array(color_dist[color]["Upper"], np.uint8) dilate_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))# 矩形结构 erode_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))# 矩形结构# 图像处理 hsvFrame = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) color_mask = cv2.inRange(hsvFrame, color_lower, color_upper) print(color_mask.shape) mask = np.zeros((800, 928)) imgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret,binary = cv2.threshold(imgray,125,255,cv2.THRESH_BINARY)res = paddle_seg.infer(img)res = paddle_seg.draw_img(res, img) # res = res[0].astype("int8")*255 cv2.imwrite("res.jpg", res)

预测结果