#!/usr/bin/env python
# encoding: utf-8
"""
@version: v1.0
@author: Jory.d
@contact: 707564875@qq.com
@site:
@software: PyCharm
@file: ncnn_basenet.py
@time: 2022/10/28 下午3:28
@desc: 用于ncnn推理的基础类
"""
from abc import ABCMeta, abstractmethod
import os, os.path as osp
import numpy as np
import ncnn
import cv2
import pickle
class NCNNBaseNet(metaclass=ABCMeta):
CLASSES = ('object1', '__backgound__',)
MODEL_ROOT = './ncnn_models/repvgg'
PARAM_PATH = f'{MODEL_ROOT}/epoch_635.deploy.pth.sim-opt-fp16.param'
BIN_PATH = f'{MODEL_ROOT}/epoch_635.deploy.pth.sim-opt-fp16.bin'
bbox_means = [0., 0., 0., 0.]
bbox_stds = [0.1, 0.1, 0.2, 0.2]
INPUT_W = 112
INPUT_H = 112
INPUT_C = 3
MEAN = [128., ] * INPUT_C
STD = [1 / 128., ] * INPUT_C
def __init__(self):
self.mean_vals = self.MEAN
self.norm_vals = self.STD
self.class_num = len(self.CLASSES)
self.use_gpu = False
self.num_threads = 4
self.init_net()
def init_net(self):
assert osp.exists(self.PARAM_PATH)
assert osp.exists(self.BIN_PATH)
self.net = ncnn.Net()
self.net.opt.use_vulkan_compute = self.use_gpu
self.net.load_param(self.PARAM_PATH)
self.net.load_model(self.BIN_PATH)
print('self.net.load_param() is done.')
print('self.net.load_model() is done.')
input_names = self.net.input_names()
output_names = self.net.output_names()
assert len(input_names) > 0 and len(output_names) > 0
print(f'input_names: {input_names}')
print(f'output_names: {output_names}')
self.input_names = input_names
self.output_names = output_names
self.class_num = len(self.CLASSES)
def __del__(self):
self.net = None
self.anchors_per_level = None
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def softmax(self, x, dim=-1):
x = np.exp(x)
sum_value = np.sum(x, axis=dim)
sum_value = np.expand_dims(sum_value, axis=dim)
return x / sum_value
def preprocess(self, img):
"""
:param img: opencv_mat
:return: ncnn:mat
"""
img_h, img_w = img.shape[:2]
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
x = img_rgb
if self.INPUT_C == 1:
x = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)
mat_in = ncnn.Mat.from_pixels_resize(
x,
ncnn.Mat.PixelType.PIXEL_GRAY if self.INPUT_C == 1 else ncnn.Mat.PixelType.PIXEL_RGB,
img_w,
img_h,
self.INPUT_W,
self.INPUT_H,
)
mat_in.substract_mean_normalize(self.MEAN, self.STD)
return mat_in
@abstractmethod
def detect(self, img, thres):
raise NotImplementedError('must be overwrite.')
pass
# 目标检测基础类
class NCNNDetectNet(NCNNBaseNet):
ANCHOR_PATH = f'./epoch_7000_anchor_per_level.pkl'
OUTPUT_NODES = [
['61', '62'], # stride 32 -- (conf, loc)
]
def __init__(self):
super(NCNNDetectNet, self).__init__()
self.load_anchor_data()
def load_anchor_data(self):
assert osp.exists(self.ANCHOR_PATH)
f = open(self.ANCHOR_PATH, 'rb')
# [level1, level2, ...]
"""
[
[
[x1,y1,x2,y2],
...
],
[
[x1,y1,x2,y2],
...
],
...
]
"""
self.anchors_per_level = pickle.load(f)
f.close()
def ssd_decode(self, priors, bbox_pred):
"""
SSD DeltaXYWHBBoxCoder 方式的box解码， 参考自 mmdetection / DeltaXYWHBBoxCoder
"""
deltas = bbox_pred
rois_ = np.asarray(priors).reshape(-1, 4)
assert rois_.shape == bbox_pred.shape, f'{rois_.shape} != {bbox_pred.shape}.'
means, stds = self.bbox_means, self.bbox_stds
means, stds = np.array([means]), np.array([stds])
denorm_deltas = deltas * stds + means
dxy = denorm_deltas[:, :2]
dwh = denorm_deltas[:, 2:]
pxy = ((rois_[:, :2] + rois_[:, 2:]) * 0.5)
pwh = (rois_[:, 2:] - rois_[:, :2])
dxy_wh = pwh * dxy
gxy = pxy + dxy_wh
gwh = pwh * np.exp(dwh)
x1y1 = gxy - (gwh * 0.5)
x2y2 = gxy + (gwh * 0.5)
bboxes = np.concatenate([x1y1, x2y2], axis=-1)
min_xy = 0
maxXY = (self.INPUT_W, self.INPUT_H)
max_xy = np.array([[*maxXY] * 2], np.float32)
bboxes = np.where(bboxes < min_xy, min_xy, bboxes)
bboxes = np.where(bboxes > max_xy, max_xy, bboxes)
return bboxes
def nms_bboxes(self, boxes, scores, nms_thres=0.6):
"""Suppress non-maximal boxes.
# Arguments
boxes: ndarray, boxes of objects.
scores: ndarray, scores of objects.
# Returns
keep: ndarray, index of effective boxes.
"""
x = boxes[:, 0]
y = boxes[:, 1]
w = boxes[:, 2] - boxes[:, 0]
h = boxes[:, 3] - boxes[:, 1]
areas = w * h
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x[i], x[order[1:]])
yy1 = np.maximum(y[i], y[order[1:]])
xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
w1 = np.maximum(0.0, xx2 - xx1)
h1 = np.maximum(0.0, yy2 - yy1)
inter = w1 * h1
ovr = inter / (areas[i] + areas[order[1:]] - inter + 1e-8)
inds = np.where(ovr <= nms_thres)[0]
order = order[inds + 1]
keep = np.array(keep)
return keep
def post_process(self, outs, thres=0.5):
mlvl_bboxes, mlvl_scores, mlvl_labels = outs
nboxes, nclasses, nscores = [], [], []
for c in range(self.class_num):
inds = np.where(mlvl_labels == c)
b = mlvl_bboxes[inds]
c = mlvl_labels[inds]
s = mlvl_scores[inds]
inds = np.where(s >= thres)[0]
b = b[inds]
c = c[inds]
s = s[inds]
if b.shape[0] == 0: continue
keep = self.nms_bboxes(b, s)
nboxes.append(b[keep])
nclasses.append(c[keep])
nscores.append(s[keep])
if len(nboxes) > 0:
bboxes = np.concatenate(nboxes) # [N,4]
classes = np.concatenate(nclasses) # [N,]
scores = np.concatenate(nscores) # [N,]
return bboxes, classes, scores
else:
return [], [], []
def detect(self, img, thres=0.7):
"""
img: opencv_mat, channel order: RGB
"""
img_h, img_w = img.shape[:2]
mat_in = self.preprocess(img)
ex = self.net.create_extractor()
ex.set_num_threads(self.num_threads)
ex.input(self.input_names[0], mat_in)
cls_score_list = []
bbox_pred_list = []
for out_names in self.OUTPUT_NODES: # stride 从小到大
assert len(out_names) == 2, '2个featmap, 代表score和location.'
ret, out_conf_stride_n = ex.extract(out_names[0])
ret, out_loc_stride_n = ex.extract(out_names[1])
out_conf_stride_n = np.array(out_conf_stride_n) # [c,h,w]
out_loc_stride_n = np.array(out_loc_stride_n)
# print(out_conf_stride_n.shape)
# print(out_loc_stride_n.shape)
# print(out_conf_stride_n[0][0][:10])
# print(out_loc_stride_n[0][0][:10])
cls_score_list.append(out_conf_stride_n)
bbox_pred_list.append(out_loc_stride_n)
mat_in.release()
###########################################################################
mlvl_priors = self.anchors_per_level
mlvl_bboxes, mlvl_scores, mlvl_labels = [], [], []
for level_idx, (cls_score, bbox_pred, priors) in \
enumerate(zip(cls_score_list, bbox_pred_list, mlvl_priors)):
cls_score = np.transpose(cls_score, axes=[1, 2, 0]).reshape(-1, self.class_num)
bboxes = np.transpose(bbox_pred, axes=[1, 2, 0]).reshape(-1, 4) #
if self.class_num == 1:
cls_score = self.sigmoid(cls_score)
else:
cls_score = self.softmax(cls_score, -1)
bboxes = self.ssd_decode(priors, bboxes)
valid_mask = np.where(cls_score > thres) # （行,列）
labels = valid_mask[1]
cls_score = cls_score[valid_mask]
bboxes = bboxes[valid_mask[0]]
mlvl_bboxes.append(bboxes)
mlvl_scores.append(cls_score)
mlvl_labels.append(labels)
mlvl_bboxes = np.concatenate(mlvl_bboxes, axis=0)
mlvl_scores = np.concatenate(mlvl_scores, axis=0)
mlvl_labels = np.concatenate(mlvl_labels, axis=0)
# print(mlvl_scores, mlvl_bboxes)
# scale detect size to src
scale_factor = np.array([[img_w / self.INPUT_W, img_h / self.INPUT_H, ] * 2])
mlvl_bboxes *= scale_factor
result = self.post_process((mlvl_bboxes, mlvl_scores, mlvl_labels), thres)
return result
# NCNN分割基础类
class NCNNSegNet(NCNNBaseNet):
OUTPUT_NODES = [
['61', '62'] # heatmap
]
def detect(self, img, thres=0.7):
mat_in = self.preprocess(img)
ex = self.net.create_extractor()
ex.set_num_threads(self.num_threads)
ex.input(self.input_names[0], mat_in)
outs = []
for node in self.OUTPUT_NODES:
ret, out_holes_seg = ex.extract(node) # [n,k,k]
out_holes_seg = np.array(out_holes_seg)
outs.append(out_holes_seg)
mat_in.release()
return outs
@abstractmethod
def post_process(self, outs, thres):
raise NotImplementedError('must be overwrite.')
pass
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