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inferEngine.py
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inferEngine.py
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import cv2
import numpy as np
import fastdeploy as fd
from PIL import Image
from collections import Counter
def FastdeployOption(device=0):
option = fd.RuntimeOption()
if device == 0:
option.use_gpu()
else:
# 使用OpenVino推理
option.use_openvino_backend()
option.use_cpu()
return option
class SegModel(object):
def __init__(self, device=0) -> None:
self.segModel = fd.vision.segmentation.ppseg.PaddleSegModel(
model_file = 'inference/ppliteseg/model.pdmodel',
params_file = 'inference/ppliteseg/model.pdiparams',
config_file = 'inference/ppliteseg/deploy.yaml',
runtime_option=FastdeployOption(device)
)
def predict(self, img):
segResult = self.segModel.predict(img)
result = self.postprocess(segResult)
visImg = fd.vision.vis_segmentation(img, segResult)
return result, visImg
def postprocess(self, result):
resultShape = result.shape
labelmap = result.label_map
labelmapCount = dict(Counter(labelmap))
pixelTotal = int(resultShape[0] * resultShape[1])
# 统计建筑率和绿地率
buildingRate, greenRate = 0, 0
if 8 in labelmapCount:
buildingRate = round(labelmapCount[8] / pixelTotal* 100, 3)
if 9 in labelmapCount:
greenRate = round(labelmapCount[9] / pixelTotal * 100 , 3)
return {"building": buildingRate, "green": greenRate}
class DetModel(object):
def __init__(self, device=0) -> None:
self.detModel = fd.vision.detection.PPYOLO(
model_file = 'inference/ppyolo/model.pdmodel',
params_file = 'inference/ppyolo/model.pdiparams',
config_file = 'inference/ppyolo/infer_cfg.yml',
runtime_option=FastdeployOption(device)
)
# 阈值设置
self.threshold = 0.3
def predict(self, img):
detResult = self.detModel.predict(img.copy())
result = self.postprocess(detResult)
visImg = fd.vision.vis_detection(img, detResult, self.threshold, 2)
return result, visImg
def postprocess(self, result):
# 得到结果
detIds = result.label_ids
detScores = result.scores
# 统计数量
humanNum, CarNum = 0, 0
for i in range(len(detIds)):
if detIds[i] == 0 and detScores[i] >= self.threshold:
humanNum += 1
if detIds[i] == 2 and detScores[i] >= self.threshold:
CarNum += 1
return {"human": humanNum, "car": CarNum}