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achelous.py
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achelous.py
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import colorsys
import os
import time
import pandas as pd
import copy
import numpy as np
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
from PIL import ImageDraw, ImageFont, ImageEnhance
from sklearn.preprocessing import MinMaxScaler, normalize
from nets.Achelous import Achelous, Achelous3T
from utils.utils import (cvtColor, get_classes, preprocess_input, resize_image,
show_config, preprocess_input_radar)
import torch.nn.functional as F
from PIL import Image
from utils.utils_bbox import decode_outputs, non_max_suppression
from utils_seg.utils import resize_image as resize_image_seg
import cv2
class achelous(object):
_defaults = {
# --------------------------------------------------------------------------#
# 使用自己训练好的模型进行预测一定要修改model_path和classes_path!
# model_path指向logs文件夹下的权值文件,classes_path指向model_data下的txt
#
# 训练好后logs文件夹下存在多个权值文件,选择验证集损失较低的即可。
# 验证集损失较低不代表mAP较高,仅代表该权值在验证集上泛化性能较好。
# 如果出现shape不匹配,同时要注意训练时的model_path和classes_path参数的修改
# --------------------------------------------------------------------------#
"model_path" : "model_data/mv_gdf_nano_ps_s2.pth",
"radar_root" : "E:/Big_Datasets/water_surface/benchmark_new/WaterScenes_new/radar/VOCradar320",
"radar_pc_root" : "E:/Big_Datasets/water_surface/benchmark_new/WaterScenes_new/radar/radar_0220/radar",
"classes_path" : 'model_data/waterscenes_benchmark.txt',
"export_path" : 'export_results',
# ---------------------------------------------------------------------#
# 输入图片的大小,必须为32的倍数。
# ---------------------------------------------------------------------#
"input_shape": [320, 320],
# ---------------------------------------------------------------------#
# 语义分割的类别数量
# ---------------------------------------------------------------------#
"num_classes_seg": 9,
# ---------------------------------------------------------------------#
# 所使用的Achelous的版本,'SO', 'S1', 'S2'
# ---------------------------------------------------------------------#
"phi": 'S2',
# ---------------------------------------------------------------------#
# 只有得分大于置信度的预测框会被保留下来
# ---------------------------------------------------------------------#
"confidence": 0.35,
# ---------------------------------------------------------------------#
# 非极大抑制所用到的nms_iou大小
# ---------------------------------------------------------------------#
"nms_iou": 0.35,
# ---------------------------------------------------------------------#
# 2D雷达map用了几种特征
# ---------------------------------------------------------------------#
"radar_channels": 3,
# ---------------------------------------------------------------------#
# backbone
# ---------------------------------------------------------------------#
"backbone": 'mv',
# ---------------------------------------------------------------------#
# neck
# ---------------------------------------------------------------------#
"neck": 'gdf',
# ---------------------------------------------------------------------#
# spp
# ---------------------------------------------------------------------#
"spp": True,
# ---------------------------------------------------------------------#
# detection head
# ---------------------------------------------------------------------#
"nano": True,
# ---------------------------------------------------------------------#
# radar point semantic segmentation 模型
# ---------------------------------------------------------------------#
"is_radar_seg": True,
"radar_pc": 'pn',
# ---------------------------------------------------------------------#
# radar point semantic segmentation 输入特征数量
# ---------------------------------------------------------------------#
"radar_pc_features_num": 5,
"radar_pc_seg_features": ['x', 'y', 'z', 'comp_velocity', 'rcs'],
"radar_pc_seg_labels": ['label'],
"radar_pc_align_num": 512,
# ---------------------------------------------------------------------#
# radar point semantic segmentation 类别数量
# ---------------------------------------------------------------------#
"radar_pc_classes": 8,
"radar_pc_cls_color": {0:'b', 1:'g', 2:'r', 3:'m', 4:'y', 5:'orange', 6:'violet', 7:'peru'},
# ---------------------------------------------------------------------#
# 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize,
# 在多次测试后,发现关闭letterbox_image直接resize的效果更好
# ---------------------------------------------------------------------#
"letterbox_image": True,
# -------------------------------#
# 是否使用Cuda
# 没有GPU可以设置成False
# -------------------------------#
"cuda": True,
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
# ---------------------------------------------------#
# 初始化Achelous
# ---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
self._defaults[name] = value
# ---------------------------------------------------#
# 获得种类和先验框的数量
# ---------------------------------------------------#
self.class_names, self.num_classes = get_classes(self.classes_path)
# ---------------------------------------------------#
# 画框设置不同的颜色
# ---------------------------------------------------#
hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors))
if self.num_classes_seg <= 21:
self.colors_seg = [ (0, 0, 0), (128, 0, 0), (0, 128, 0), (128, 128, 0), (0, 0, 128), (128, 0, 128), (0, 128, 128),
(128, 128, 128), (64, 0, 0), (192, 0, 0), (64, 128, 0), (192, 128, 0), (64, 0, 128), (192, 0, 128),
(64, 128, 128), (192, 128, 128), (0, 64, 0), (128, 64, 0), (0, 192, 0), (128, 192, 0), (0, 64, 128),
(128, 64, 12)]
self.colors_seg_line = list(reversed([ (0, 0, 0), (128, 0, 0), (0, 128, 0), (128, 128, 0), (0, 0, 128), (128, 0, 128), (0, 128, 128),
(128, 128, 128), (64, 0, 0), (192, 0, 0), (64, 128, 0), (192, 128, 0), (64, 0, 128), (192, 0, 128),
(64, 128, 128), (192, 128, 128), (0, 64, 0), (128, 64, 0), (0, 192, 0), (128, 192, 0), (0, 64, 128),
(128, 64, 12)]))
else:
hsv_tuples = [(x / self.num_classes_seg, 1., 1.) for x in range(self.num_classes_seg)]
self.colors_seg = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors_seg = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors_seg))
self.generate()
show_config(**self._defaults)
# ---------------------------------------------------#
# 生成模型
# ---------------------------------------------------#
def generate(self, onnx=False):
if self.is_radar_seg:
self.net = Achelous(num_det=self.num_classes, num_seg=self.num_classes_seg,
radar_channels=self.radar_channels,
backbone=self.backbone, neck=self.neck, nano_head=self.nano,
resolution=self.input_shape[0],
pc_seg=self.radar_pc, pc_classes=self.radar_pc_classes,
pc_channels=self.radar_pc_features_num, phi=self.phi, spp=self.spp)
else:
self.net = Achelous3T(num_det=self.num_classes, num_seg=self.num_classes_seg,
radar_channels=self.radar_channels,
backbone=self.backbone, neck=self.neck, nano_head=self.nano,
resolution=self.input_shape[0], phi=self.phi, spp=self.spp)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.net.load_state_dict(torch.load(self.model_path, map_location=device))
self.net = self.net.eval()
print('{} model, and classes loaded.'.format(self.model_path))
if not onnx:
if self.cuda:
self.net = nn.DataParallel(self.net)
self.net = self.net.cuda()
# ---------------------------------------------------#
# 检测图片
# ---------------------------------------------------#
def detect_image(self, image, image_id, crop=False, count=False, export_all=False):
# ---------------------------------------------------#
# 获得输入图片的高和宽
# ---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
# ---------------------------------------------------------#
# 在这里将图像转换成RGB图像,防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测,所有其它类型的图像都会转化成RGB
# ---------------------------------------------------------#
image = cvtColor(image)
old_img = copy.deepcopy(image)
orininal_h = np.array(image).shape[0]
orininal_w = np.array(image).shape[1]
# ---------------------------------------------------------#
# 给图像增加灰条,实现不失真的resize
# 也可以直接resize进行识别
# ---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
_, nw, nh = resize_image_seg(image, (self.input_shape[1], self.input_shape[0]))
# ---------------------------------------------------------#
# 添加上batch_size维度
# ---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
# ------------------------------#
# 读取雷达特征map
# ------------------------------#
radar_path = os.path.join(self.radar_root, image_id + '.npz')
radar_data = np.load(radar_path)['arr_0']
radar_data = torch.from_numpy(preprocess_input_radar(radar_data)).type(torch.FloatTensor).unsqueeze(0)
# -------------------------------- 麻烦的点云读取 ---------------------------------- #
radar_pc_file = pd.read_csv(os.path.join(self.radar_pc_root, image_id + '.csv'), index_col=0)
radar_pc_features = radar_pc_file[self.radar_pc_seg_features]
radar_pc_labels = radar_pc_file[self.radar_pc_seg_labels]
# --------------------- 投影到相机平面 ------------------------- #
radar_pc_u = radar_pc_file[['u']]
radar_pc_v = radar_pc_file[['v']]
radar_pc_power = radar_pc_file[['rcs']]
radar_pc_indexes = np.random.choice(radar_pc_features.shape[0], self.radar_pc_align_num, replace=True)
radar_pc_u = np.asarray(radar_pc_u)
radar_pc_v = np.asarray(radar_pc_v)
radar_pc_power = np.asarray(radar_pc_power)
radar_pc_u = radar_pc_u[radar_pc_indexes]
radar_pc_v = radar_pc_v[radar_pc_indexes]
radar_pc_power = radar_pc_power[radar_pc_indexes]
radar_uv = np.concatenate([radar_pc_u, radar_pc_v], axis=1)
radar_pc_features = np.asarray(radar_pc_features)
radar_pc_labels = np.asarray(radar_pc_labels)
align_radar_pc_features = radar_pc_features[radar_pc_indexes]
align_radar_pc_labels = radar_pc_labels[radar_pc_indexes]
align_radar_pc_features = normalize(X=align_radar_pc_features, axis=0)
align_radar_pc_labels = align_radar_pc_labels
align_radar_pc_features = torch.from_numpy(np.array(align_radar_pc_features, dtype=np.float32)).type(
torch.FloatTensor).unsqueeze(0).permute(0, 2, 1).cuda()
align_radar_pc_labels = torch.from_numpy(np.array(align_radar_pc_labels, dtype=np.int32)). \
type(torch.LongTensor).cuda()
# --------------------------------------------------------------------------------- #
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
# ---------------------------------------------------------#
# 将图像输入网络当中进行预测!
# ---------------------------------------------------------#
if self.is_radar_seg:
outputs, output_seg, output_seg_line, output_seg_pc = self.net(images, radar_data,
align_radar_pc_features)
outputs = decode_outputs(outputs, self.input_shape, 0)
output_seg_pc = output_seg_pc[0]
output_seg_pc_cls = torch.argmax(output_seg_pc, dim=1).unsqueeze(1)
# output_seg_pc_cls = np.array([[self.radar_pc_cls_color[key.item()]] for key in output_seg_pc_cls])
output_seg_pc_collections = np.concatenate([radar_pc_u, radar_pc_v, radar_pc_power,
output_seg_pc_cls.cpu().numpy()], axis=1)
output_seg_pc_collections = np.unique(output_seg_pc_collections, axis=0)
fig = plt.figure()
plt.scatter(x=output_seg_pc_collections[:, 0], y=output_seg_pc_collections[:, 1], s=output_seg_pc_collections[:, 2], c=output_seg_pc_collections[:, 3], alpha=0.98)
else:
outputs, output_seg, output_seg_line = self.net(images, radar_data)
outputs = decode_outputs(outputs, self.input_shape, 0)
output_seg = output_seg[0]
output_seg_line = output_seg_line[0]
# -------------------------------------------------------- #
# ---------------------------------------------------#
# 语义分割 取出每一个像素点的种类
# ---------------------------------------------------#
output_seg = F.softmax(output_seg.permute(1, 2, 0), dim=-1).cpu().numpy()
# --------------------------------------#
# 将灰条部分截取掉
# --------------------------------------#
output_seg = output_seg[int((self.input_shape[0] - nh) // 2): int((self.input_shape[0] - nh) // 2 + nh), \
int((self.input_shape[1] - nw) // 2): int((self.input_shape[1] - nw) // 2 + nw)]
# ---------------------------------------------------#
# 进行图片的resize
# ---------------------------------------------------#
output_seg = cv2.resize(output_seg, (orininal_w, orininal_h), interpolation=cv2.INTER_LINEAR)
# ---------------------------------------------------#
# 取出每一个像素点的种类
# ---------------------------------------------------#
output_seg = output_seg.argmax(axis=-1)
output_seg[(output_seg != 0) & (output_seg != 8)] = 0
# -------------------------------------------------------- #
# -------------------------------------------------------- #
# ---------------------------------------------------#
# 水岸线分割 取出每一个像素点的种类
# ---------------------------------------------------#
output_seg_line = F.softmax(output_seg_line.permute(1, 2, 0), dim=-1).cpu().numpy()
# --------------------------------------#
# 将灰条部分截取掉
# --------------------------------------#
output_seg_line = output_seg_line[int((self.input_shape[0] - nh) // 2): int((self.input_shape[0] - nh) // 2 + nh), \
int((self.input_shape[1] - nw) // 2): int((self.input_shape[1] - nw) // 2 + nw)]
# ---------------------------------------------------#
# 进行图片的resize
# ---------------------------------------------------#
output_seg_line = cv2.resize(output_seg_line, (orininal_w, orininal_h), interpolation=cv2.INTER_LINEAR)
# ---------------------------------------------------#
# 取出每一个像素点的种类
# ---------------------------------------------------#
output_seg_line = output_seg_line.argmax(axis=-1)
# -------------------------------------------------------- #
# ---------------------------------------------------------#
# 语义分割
# ---------------------------------------------------------#
seg_img = np.reshape(np.array(self.colors_seg, np.uint8)[np.reshape(output_seg, [-1])],
[orininal_h, orininal_w, -1])
seg_line_img = np.reshape(np.array(self.colors_seg_line, np.uint8)[np.reshape(output_seg_line, [-1])],
[orininal_h, orininal_w, -1])
# ------------------------------------------------#
# 将新图片转换成Image的形式
# ------------------------------------------------#
image = Image.fromarray(np.uint8(seg_img))
image_line = Image.fromarray(np.uint8(seg_line_img))
# ------------------------------------------------#
# 将新图与原图及进行混合
# ------------------------------------------------#
image = Image.blend(old_img, image, 0.45)
image = Image.blend(image, image_line, 0.3)
# contrast_enhancer = ImageEnhance.Contrast(image)
# # 传入调整系数1.2
# image = contrast_enhancer.enhance(1.1)
bright_enhancer = ImageEnhance.Brightness(image)
# 传入调整系数1.2
image = bright_enhancer.enhance(1.3)
# ---------------------------------------------------------#
# 将预测框进行堆叠,然后进行非极大抑制
# ---------------------------------------------------------#
results = non_max_suppression(outputs, self.num_classes, self.input_shape,
image_shape, self.letterbox_image, conf_thres=self.confidence,
nms_thres=self.nms_iou)
if results[0] is None:
return image
top_label = np.array(results[0][:, 6], dtype='int32')
top_conf = results[0][:, 4] * results[0][:, 5]
top_boxes = results[0][:, :4]
# ---------------------------------------------------------#
# 设置字体与边框厚度
# ---------------------------------------------------------#
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = int(max((image.size[0] + image.size[1]) // np.mean(self.input_shape), 1))
# ---------------------------------------------------------#
# 计数
# ---------------------------------------------------------#
if count:
print("top_label:", top_label)
classes_nums = np.zeros([self.num_classes])
for i in range(self.num_classes):
num = np.sum(top_label == i)
if num > 0:
print(self.class_names[i], " : ", num)
classes_nums[i] = num
print("classes_nums:", classes_nums)
# ---------------------------------------------------------#
# 是否进行目标的裁剪
# ---------------------------------------------------------#
if crop:
for i, c in list(enumerate(top_label)):
top, left, bottom, right = top_boxes[i]
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
dir_save_path = "img_crop"
if not os.path.exists(dir_save_path):
os.makedirs(dir_save_path)
crop_image = image.crop([left, top, right, bottom])
crop_image.save(os.path.join(dir_save_path, "crop_" + str(i) + ".png"), quality=95, subsampling=0)
print("save crop_" + str(i) + ".png to " + dir_save_path)
# ---------------------------------------------------------#
# 图像绘制
# ---------------------------------------------------------#
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
if predicted_class == 'sailor':
continue
box = top_boxes[i]
score = top_conf[i]
top, left, bottom, right = box
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label, top, left, bottom, right)
# if export_all is False:
# print(label, top, left, bottom, right)
# else:
# continue
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[c])
draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
draw.text(text_origin, str(label, 'UTF-8'), fill=(0, 0, 0), font=font)
del draw
if export_all is False:
plt.xticks([])
plt.yticks([])
plt.axis('off')
plt.imshow(image)
plt.savefig("export_results/" + image_id + ".jpg", dpi=300, bbox_inches='tight', pad_inches=0)
plt.show()
else:
plt.axis('off')
plt.xticks([])
plt.yticks([])
plt.imshow(image)
plt.savefig("export_results/" + image_id+".jpg", dpi=300, bbox_inches='tight', pad_inches=0)
return image
def detect_heatmap(self, image, image_id, heatmap_save_path):
import cv2
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def sigmoid(x):
y = 1.0 / (1.0 + np.exp(-x))
return y
# ---------------------------------------------------#
# 获得输入图片的高和宽
# ---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
# ---------------------------------------------------------#
# 在这里将图像转换成RGB图像,防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测,所有其它类型的图像都会转化成RGB
# ---------------------------------------------------------#
image = cvtColor(image)
# ---------------------------------------------------------#
# 给图像增加灰条,实现不失真的resize
# 也可以直接resize进行识别
# ---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
# ---------------------------------------------------------#
# 添加上batch_size维度
# ---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
# ------------------------------#
# 读取雷达特征map
# ------------------------------#
radar_path = os.path.join(self.radar_root, image_id + '.npz')
radar_data = np.load(radar_path)['arr_0']
radar_data = torch.from_numpy(radar_data).type(torch.FloatTensor).unsqueeze(0)
# -------------------------------- 麻烦的点云读取 ---------------------------------- #
radar_pc_file = pd.read_csv(os.path.join(self.radar_pc_root, image_id + '.csv'), index_col=0)
radar_pc_features = radar_pc_file[self.radar_pc_seg_features]
radar_pc_labels = radar_pc_file[self.radar_pc_seg_labels]
# --------------------- 投影到相机平面 ------------------------- #
radar_pc_u = radar_pc_file[['u']]
radar_pc_v = radar_pc_file[['v']]
radar_pc_power = radar_pc_file[['rcs']]
radar_pc_indexes = np.random.choice(radar_pc_features.shape[0], self.radar_pc_align_num, replace=True)
radar_pc_u = np.asarray(radar_pc_u)
radar_pc_v = np.asarray(radar_pc_v)
radar_pc_power = np.asarray(radar_pc_power)
radar_pc_u = radar_pc_u[radar_pc_indexes]
radar_pc_v = radar_pc_v[radar_pc_indexes]
radar_pc_power = radar_pc_power[radar_pc_indexes]
radar_uv = np.concatenate([radar_pc_u, radar_pc_v], axis=1)
radar_pc_features = np.asarray(radar_pc_features)
radar_pc_labels = np.asarray(radar_pc_labels)
align_radar_pc_features = radar_pc_features[radar_pc_indexes]
align_radar_pc_labels = radar_pc_labels[radar_pc_indexes]
align_radar_pc_features = normalize(X=align_radar_pc_features, axis=0)
align_radar_pc_labels = align_radar_pc_labels
align_radar_pc_features = torch.from_numpy(np.array(align_radar_pc_features, dtype=np.float32)).type(
torch.FloatTensor).unsqueeze(0).permute(0, 2, 1).cuda()
align_radar_pc_labels = torch.from_numpy(np.array(align_radar_pc_labels, dtype=np.int32)). \
type(torch.LongTensor).cuda()
# --------------------------------------------------------------------------------- #
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
# ---------------------------------------------------------#
# 将图像输入网络当中进行预测!
# ---------------------------------------------------------#
if self.is_radar_seg:
outputs, output_seg, output_seg_line, output_seg_pc = self.net(images, radar_data,
align_radar_pc_features)
else:
outputs, output_seg, output_seg_line = self.net(images, radar_data)
outputs = [output.cpu().numpy() for output in outputs]
plt.imshow(image, alpha=1)
plt.axis('off')
mask = np.zeros((image.size[1], image.size[0]))
for sub_output in outputs:
b, c, h, w = np.shape(sub_output)
sub_output = np.transpose(sub_output, [0, 2, 3, 1])[0]
score = np.max(sigmoid(sub_output[..., 5:]), -1) * sigmoid(sub_output[..., 4])
score = cv2.resize(score, (image.size[0], image.size[1]))
normed_score = (score * 255).astype('uint8')
mask = np.maximum(mask, normed_score)
plt.imshow(mask, alpha=0.5, interpolation='nearest', cmap="jet")
plt.axis('off')
plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
plt.margins(0, 0)
plt.savefig(heatmap_save_path, dpi=200)
print("Save to the " + heatmap_save_path)
plt.cla()
def convert_to_onnx(self, simplify, model_path):
import onnx
self.generate(onnx=True)
im = torch.zeros(1, 3, *self.input_shape).to('cpu') # image size(1, 3, 512, 512) BCHW
input_layer_names = ["images"]
output_layer_names = ["output"]
# Export the model
print(f'Starting export with onnx {onnx.__version__}.')
torch.onnx.export(self.net,
im,
f=model_path,
verbose=False,
opset_version=12,
training=torch.onnx.TrainingMode.EVAL,
do_constant_folding=True,
input_names=input_layer_names,
output_names=output_layer_names,
dynamic_axes=None)
# Checks
model_onnx = onnx.load(model_path) # load onnx model
onnx.checker.check_model(model_onnx) # check onnx model
# Simplify onnx
if simplify:
import onnxsim
print(f'Simplifying with onnx-simplifier {onnxsim.__version__}.')
model_onnx, check = onnxsim.simplify(
model_onnx,
dynamic_input_shape=False,
input_shapes=None)
assert check, 'assert check failed'
onnx.save(model_onnx, model_path)
print('Onnx model save as {}'.format(model_path))
def get_map_txt(self, image_id, image, class_names, map_out_path):
f = open(os.path.join(map_out_path, "detection-results/" + image_id + ".txt"), "w")
image_shape = np.array(np.shape(image)[0:2])
# ---------------------------------------------------------#
# 在这里将图像转换成RGB图像,防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测,所有其它类型的图像都会转化成RGB
# ---------------------------------------------------------#
image = cvtColor(image)
# ---------------------------------------------------------#
# 给图像增加灰条,实现不失真的resize
# 也可以直接resize进行识别
# ---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
# ---------------------------------------------------------#
# 添加上batch_size维度
# ---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
# ------------------------------#
# 读取雷达特征map
# ------------------------------#
radar_path = os.path.join(self.radar_root, image_id + '.npz')
radar_data = np.load(radar_path)['arr_0']
radar_data = torch.from_numpy(radar_data).type(torch.FloatTensor).unsqueeze(0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
# ---------------------------------------------------------#
# 将图像输入网络当中进行预测!
# ---------------------------------------------------------#
outputs, _ = self.net(images, radar_data)
outputs = decode_outputs(outputs, self.input_shape)
# ---------------------------------------------------------#
# 将预测框进行堆叠,然后进行非极大抑制
# ---------------------------------------------------------#
results = non_max_suppression(outputs, self.num_classes, self.input_shape,
image_shape, self.letterbox_image, conf_thres=self.confidence,
nms_thres=self.nms_iou)
if results[0] is None:
return
top_label = np.array(results[0][:, 6], dtype='int32')
top_conf = results[0][:, 4] * results[0][:, 5]
top_boxes = results[0][:, :4]
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = str(top_conf[i])
top, left, bottom, right = box
if predicted_class not in class_names:
continue
f.write("%s %s %s %s %s %s\n" % (
predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)), str(int(bottom))))
f.close()
return