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evaluation.py
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evaluation.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Mar 18 15:19:02 2020
@author: Lim
"""
import os
import cv2
import math
import time
import torch
import evaluation
import numpy as np
from resnet_dcn import ResNet
#from dlanet_dcn import DlaNet
import matplotlib.pyplot as plt
from predict import pre_process, ctdet_decode, post_process, merge_outputs
# =============================================================================
# 推断
# =============================================================================
def process(images, return_time=False):
with torch.no_grad():
output = model(images)
hm = output['hm'].sigmoid_()
ang = output['ang'].relu_()
wh = output['wh']
reg = output['reg']
torch.cuda.synchronize()
forward_time = time.time()
dets = ctdet_decode(hm, wh, ang, reg=reg, K=100) # K 是最多保留几个目标
if return_time:
return output, dets, forward_time
else:
return output, dets
# =============================================================================
# 常规 IOU
# =============================================================================
def iou(bbox1, bbox2, center=False):
"""Compute the iou of two boxes.
Parameters
----------
bbox1, bbox2: list.
The bounding box coordinates: [xmin, ymin, xmax, ymax] or [xcenter, ycenter, w, h].
center: str, default is 'False'.
The format of coordinate.
center=False: [xmin, ymin, xmax, ymax]
center=True: [xcenter, ycenter, w, h]
Returns
-------
iou: float.
The iou of bbox1 and bbox2.
"""
if center == False:
xmin1, ymin1, xmax1, ymax1 = bbox1
xmin2, ymin2, xmax2, ymax2 = bbox2
else:
xmin1, ymin1 = bbox1[0] - bbox1[2] / 2.0, bbox1[1] - bbox1[3] / 2.0
xmax1, ymax1 = bbox1[0] + bbox1[2] / 2.0, bbox1[1] + bbox1[3] / 2.0
xmin2, ymin2 = bbox2[0] - bbox2[2] / 2.0, bbox2[1] - bbox2[3] / 2.0
xmax2, ymax2 = bbox2[0] + bbox2[2] / 2.0, bbox2[1] + bbox2[3] / 2.0
# 获取矩形框交集对应的顶点坐标(intersection)
xx1 = np.max([xmin1, xmin2])
yy1 = np.max([ymin1, ymin2])
xx2 = np.min([xmax1, xmax2])
yy2 = np.min([ymax1, ymax2])
# 计算两个矩形框面积
area1 = (xmax1 - xmin1 ) * (ymax1 - ymin1 )
area2 = (xmax2 - xmin2 ) * (ymax2 - ymin2 )
# 计算交集面积
inter_area = (np.max([0, xx2 - xx1])) * (np.max([0, yy2 - yy1]))
# 计算交并比
iou = inter_area / (area1 + area2 - inter_area + 1e-6)
return iou
#bbox1 = [1,1,2,2]
#bbox2 = [2,2,2,2]
#ret = iou(bbox1,bbox2,True)
# =============================================================================
# 旋转 IOU
# =============================================================================
def iou_rotate_calculate(boxes1, boxes2):
# print("####boxes2:", boxes1.shape)
# print("####boxes2:", boxes2.shape)
area1 = boxes1[2] * boxes1[3]
area2 = boxes2[2] * boxes2[3]
r1 = ((boxes1[0], boxes1[1]), (boxes1[2], boxes1[3]), boxes1[4])
r2 = ((boxes2[0], boxes2[1]), (boxes2[2], boxes2[3]), boxes2[4])
int_pts = cv2.rotatedRectangleIntersection(r1, r2)[1]
if int_pts is not None:
order_pts = cv2.convexHull(int_pts, returnPoints=True)
int_area = cv2.contourArea(order_pts)
# 计算出iou
ious = int_area * 1.0 / (area1 + area2 - int_area)
# print(int_area)
else:
ious=0
return ious
# 用中心点坐标、长宽、旋转角
#boxes1 = np.array([1,1,2,2,0],dtype='float32')
#boxes2 = np.array([2,2,2,2,0],dtype='float32')
#ret = iou_rotate_calculate(boxes1,boxes2)
# =============================================================================
# 获得标签信息
# =============================================================================
def get_lab_ret(xml_path):
ret = []
with open(xml_path, 'r', encoding='UTF-8') as fp:
ob = []
flag = 0
for p in fp:
key = p.split('>')[0].split('<')[1]
if key == 'cx':
ob.append(p.split('>')[1].split('<')[0])
if key == 'cy':
ob.append(p.split('>')[1].split('<')[0])
if key == 'w':
ob.append(p.split('>')[1].split('<')[0])
if key == 'h':
ob.append(p.split('>')[1].split('<')[0])
if key == 'angle':
ob.append(p.split('>')[1].split('<')[0])
flag = 1
if flag == 1:
x1 = float(ob[0])
y1 = float(ob[1])
w = float(ob[2])
h = float(ob[3])
angle = float(ob[4])*180/math.pi
angle = angle if angle < 180 else angle-180
bbox = [x1, y1, w, h, angle] # COCO 对应格式[x,y,w,h]
ret.append(bbox)
ob = []
flag = 0
return ret
def get_pre_ret(img_path, device):
image = cv2.imread(img_path)
images, meta = pre_process(image)
images = images.to(device)
output, dets, forward_time = process(images, return_time=True)
dets = post_process(dets, meta)
ret = merge_outputs(dets)
res = np.empty([1,7])
for i, c in ret.items():
tmp_s = ret[i][ret[i][:,5]>0.3]
tmp_c = np.ones(len(tmp_s)) * (i+1)
tmp = np.c_[tmp_c,tmp_s]
res = np.append(res,tmp,axis=0)
res = np.delete(res, 0, 0)
res = res.tolist()
return res
def pre_recall(root_path, device, iou=0.5):
imgs = os.listdir(root_path)
num = 0
all_pre_num = 0
all_lab_num = 0
miou = 0
mang = 0
for img in imgs:
if img.split('.')[-1] == 'jpg':
img_path = os.path.join(root_path, img)
xml_path = os.path.join(root_path, img.split('.')[0] + '.xml')
pre_ret = get_pre_ret(img_path, device)
lab_ret = get_lab_ret(xml_path)
all_pre_num += len(pre_ret)
all_lab_num += len(lab_ret)
for class_name,lx,ly,rx,ry,ang, prob in pre_ret:
pre_one = np.array([(rx+lx)/2, (ry+ly)/2, rx-lx, ry-ly, ang])
for cx, cy, w, h, ang_l in lab_ret:
lab_one = np.array([cx, cy, w, h, ang_l])
iou = iou_rotate_calculate(pre_one, lab_one)
ang_err = abs(ang - ang_l)/180
if iou > 0.5:
num += 1
miou += iou
mang += ang_err
return num/all_pre_num, num/all_lab_num, mang/num, miou/num
if __name__ == '__main__':
model = ResNet(34)
# model = DlaNet(34)
device = torch.device('cuda')
model.load_state_dict(torch.load('model\\res_dcn_34_best.pth'))
model.eval()
model.cuda()
p, r, mang, miou = pre_recall('imgs', device)
F1 = (2*p*r)/(p+r)