File size: 3,596 Bytes
07444d1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | import cv2
import math
import numpy as np
from Constants import Constants
from EnumParkingStatus import EnumParkingStatus
class StatusInferEngine():
def __init__(self, jsonEditor):
self.jsonEditor = jsonEditor
def inferUnknownStatus(self):
self._loadUnknownAnnotations()
self._computeStatus()
def _computeStatus(self):
for imgId, listSpaces in self.dictSpaces.items():
for space in listSpaces:
status = int(space[Constants.JSON_PARKING_STATUS_ID_KEY])
if (status == EnumParkingStatus.UNKNOWN.value):
spacePosition = space[Constants.JSON_SEGMENTATION_KEY]
npSpace = np.array([spacePosition])
npSpace.shape = (4,2)
centroid = self._computeCentroidSpace(npSpace)
if (imgId in self.dictSegs):
list = self.dictSegs[imgId]
idx, mindDist = self._getMinimumDistance(centroid, list)
if (cv2.pointPolygonTest(np.array([npSpace]), list[idx], measureDist = False) > 0):
space[Constants.JSON_PARKING_STATUS_ID_KEY] = EnumParkingStatus.OCCUPIED_NEED_VAL.value
else:
space[Constants.JSON_PARKING_STATUS_ID_KEY] = EnumParkingStatus.EMPTY_NEED_VAL.value
else:
space[Constants.JSON_PARKING_STATUS_ID_KEY] = EnumParkingStatus.EMPTY_NEED_VAL.value
def _loadUnknownAnnotations(self):
self.dictSpaces = {}
self.dictSegs = {}
for annot in self.jsonEditor.json[Constants.JSON_ANNOTATIONS_KEY]:
tipo = int(annot[Constants.JSON_LINK_CATEG_KEY])
imgId = annot[Constants.JSON_LINK_IMAGE_ID_KEY]
if(tipo == Constants.JSON_PARKING_SPACE_KEY):
if imgId not in self.dictSpaces:
self.dictSpaces[imgId] = []
self.dictSpaces[imgId].append(annot)
elif(tipo == Constants.JSON_VEHICLE_KEY):
if imgId not in self.dictSegs:
self.dictSegs[imgId] = []
self.dictSegs[imgId].append(self._computeCentroidVehicle(annot))
def _getMinimumDistance(self, centroidParkingSpace, listCentroids):
idx = 0
minDist = math.sqrt((listCentroids[0][0] - centroidParkingSpace[0])**2 + (listCentroids[0][1] - centroidParkingSpace[1])**2)
i = 1
while(i < len(listCentroids)):
dist = math.sqrt((listCentroids[i][0] - centroidParkingSpace[0])**2 + (listCentroids[i][1] - centroidParkingSpace[1])**2)
if(dist < minDist):
minDist = dist
idx = i
i += 1
return idx, minDist
def _computeCentroidVehicle(self, annotation):
segmentation = annotation[Constants.JSON_SEGMENTATION_KEY]
cX = 0
cY = 0
for seg in segmentation:
#force int. In case of floats, the moments assume an image and give wrong results
npArray = np.array([seg], dtype=int)
npArray.shape = (-1,2)
moments = cv2.moments(npArray)
cX += int(moments["m10"] / moments["m00"])
cY += int(moments["m01"] / moments["m00"])
numSegs = len(segmentation)
return (cX/numSegs,cY/numSegs)
def _computeCentroidSpace(self, arrayNumPy):
moments = cv2.moments(arrayNumPy)
cX = int(moments["m10"] / moments["m00"])
cY = int(moments["m01"] / moments["m00"])
return (cX, cY) |