144 lines
5.3 KiB
Python
144 lines
5.3 KiB
Python
from icraft.xir import *
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from icraft.xrt import *
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from icraft.host_backend import *
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from icraft.buyibackend import *
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import numpy as np
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import pandas as pd
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from numpy.linalg import norm
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from typing import List
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import platform
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import cv2
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import json
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import os
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from tqdm import tqdm
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import pickle
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import sys
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sys.path.append(R"../0_SFA3D-master")
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sys.path.append(R"../0_SFA3D-master/sfa")
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import argparse
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import os
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import time
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import warnings
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warnings.filterwarnings("ignore", category=UserWarning)
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from easydict import EasyDict as edict
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import torch
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from data_process.kitti_data_utils import gen_hm_radius, compute_radius, Calibration, get_filtered_lidar
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from data_process.kitti_bev_utils import makeBEVMap, drawRotatedBox, get_corners
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from data_process import transformation
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import config.kitti_config as cnf
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from utils.torch_utils import _sigmoid
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import torch.nn.functional as F
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from utils.evaluation_utils import decode, post_processing, draw_predictions, convert_det_to_real_values
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from data_process.transformation import lidar_to_camera_box
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from utils.visualization_utils import merge_rgb_to_bev, show_rgb_image_with_boxes
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def load_data(img_path, lidar_path, calib_path):
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img = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB)
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lidarData = np.fromfile(lidar_path, dtype=np.float32).reshape(-1, 4)
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calib = Calibration(calib_path)
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lidarData = get_filtered_lidar(lidarData, cnf.boundary)
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bev_map = makeBEVMap(lidarData, cnf.boundary)
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bev_map = torch.from_numpy(bev_map).unsqueeze(0)
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return img, bev_map, calib
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def process_result(result):
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outputs = []
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for out in result:
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outputs.append(torch.from_numpy(out))
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for idx, outs in enumerate(outputs):
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softmax_outs = F.softmax(outs, dim=-1)
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ret_outs = (outs * softmax_outs).sum(dim=-1)
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outputs[idx] = ret_outs
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outputs[0] = _sigmoid(outputs[0])
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outputs[1] = _sigmoid(outputs[1])
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detections = decode(outputs[0], outputs[1], outputs[2], outputs[3],
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outputs[4], K=50)
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detections = detections.cpu().numpy().astype(np.float32)
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detections = post_processing(detections, 3, 4, 0.2)
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# t2 = time_synchronized()
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detections = detections[0] # only first batch
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return detections
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def sim(network :Network, input: List[Tensor]) -> List[Tensor]:
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session = Session.Create([ HostBackend ], network.view(0), [ HostDevice.Default() ])
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session.enableTimeProfile(True)
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session.apply()
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output_tensors = session.forward( input )
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return output_tensors
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def run(network: Network, input: List[Tensor]) -> List[Tensor]:
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URL_PATH=""
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if platform.machine() == 'aarch64':
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URL_PATH = R"axi://ql100aiu?npu=0x40000000&dma=0x80000000";
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else:
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URL_PATH = Rf"socket://ql100aiu@192.168.125.{ip_octet}:9981?npu=0x40000000&dma=0x80000000"
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# Buyi_device = Device.Open(URL_PATH)
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session = Session.Create([ HostBackend], network.view(0), [HostDevice.Default()])
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# session = Session.Create([ BuyiBackend, HostBackend], network.view(0), [ Buyi_device,HostDevice.Default()])
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session.enableTimeProfile(True) #打开计时功能
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session.apply()
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output_tensors = session.forward( input ) #前向
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# 计算时间
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result = session.timeProfileResults() #获取时间，[总时间，传输时间, 硬件时间，余下时间]
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# save_time(network, result, result_table_name)
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time = np.array(list(result.values()))
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total_softtime = np.sum(time[:,1])
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total_hardtime = np.sum(time[:,2])
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# print("total_softtime:{},total_hardtime:{}(ms)",total_softtime,total_hardtime)
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return output_tensors
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ip_octet = 93
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GENERATED_JSON_FILE = "../2_compile/imodel/sfa3d/sfa3d_BY.json"
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GENERATED_RAW_FILE = "../2_compile/imodel/sfa3d/sfa3d_BY.raw"
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generated_network = Network.CreateFromJsonFile(GENERATED_JSON_FILE)
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generated_network.loadParamsFromFile(GENERATED_RAW_FILE)
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img_path = '../0_SFA3D-master/dataset/kitti/testing/image_2/000000.png'
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calib_path = '../0_SFA3D-master/dataset/kitti/testing/calib/000000.txt'
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lidar_path = '../0_SFA3D-master/dataset/kitti/testing/velodyne/000000.bin'
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img, bev_map, calib = load_data(img_path, lidar_path, calib_path)
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input_bev_maps = bev_map.float().cpu().numpy().transpose(0, 2, 3, 1)
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# print(input_bev_maps.shape)
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# input_bev_maps.tofile('input.bin')
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# input_bev_maps = np.fromfile('input.bin', dtype=np.float32).reshape((1, 608, 608, 3))
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generated_output = run(generated_network, [Tensor(input_bev_maps, Layout("NHWC"))])
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result = []
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for out in generated_output:
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result.append(np.array(out))
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# with open('result.pkl', 'wb') as f:
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# pickle.dump(output, f)
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# with open('result.pkl', 'rb') as f:
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# result = pickle.load(f)
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detections = process_result(result)
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bev_map = (bev_map.squeeze().permute(1, 2, 0).numpy() * 255).astype(np.uint8)
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bev_map = cv2.resize(bev_map, (cnf.BEV_WIDTH, cnf.BEV_HEIGHT))
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bev_map = draw_predictions(bev_map, detections.copy(), 3)
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# Rotate the bev_map
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bev_map = cv2.rotate(bev_map, cv2.ROTATE_180)
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kitti_dets = convert_det_to_real_values(detections)
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if len(kitti_dets) > 0:
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kitti_dets[:, 1:] = lidar_to_camera_box(kitti_dets[:, 1:], calib.V2C, calib.R0, calib.P2)
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img = show_rgb_image_with_boxes(img, kitti_dets, calib)
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out_img = merge_rgb_to_bev(img, bev_map, output_width=608)
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cv2.imshow(" ", out_img)
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cv2.waitKey(0) |