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AutoAnalyzer.py
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AutoAnalyzer.py
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import cv2
import os
import numpy as np
import copy
import json
# SF: Sonar File Library
import file_handler as SF
import numpy as np
from time import sleep
def FAnalyze(cls, kernel = None , kernelDim = None,
startFrame = None, blurDim = None,
bgTh = None, minApp = None, maxDis = None,
searchRadius = None,
imshow = False):
# kernel = np.ones((5,5),np.uint8)
# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10,5))
if kernelDim is not None:
kernelDim = kernelDim
else:
kernelDim = (10,2)
if (kernel == "Rectangle"):
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernelDim)
elif (kernel == "Ellipse") or (kernel is None):
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, kernelDim)
elif (kernel == "Cross"):
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, kernelDim)
else:
raise ValueError("Unknown structuring element `{}`".format(kernel))
return False
if startFrame is None:
count = 1
else:
count = startFrame
if blurDim is None:
blurDim = (5,5)
if bgTh is None:
threshold = 25
else:
threshold = bgTh
if minApp is not None:
Fish.minAppear = minApp
if maxDis is not None:
Fish.maxDisappear = maxDis
if searchRadius is not None:
centroidTracker.searchArea = searchRadius
fgbg = cv2.createBackgroundSubtractorMOG2(varThreshold=threshold)
font = cv2.FONT_HERSHEY_SIMPLEX
## variables for displaying frames
# for the key presses {
# dummy: for getting key hex values,
# k : for inputing key strokes}
dummy = 0
k = 30
# for the Play|Pause operations {
# play = False --> pause,
# play = True --> playing}
play = True
# playing in desc|asce orders {
# desc = False --> playing in ascending order,
# }
desc = False
# variables for trakcers:
tracker = centroidTracker()
while (True):
# while (count<100):
# read the image from disk
readFromFile = cls.File
img = fetchFrame(count, readFromFile= readFromFile)
if(img is None):
if readFromFile:
FSaveOutput(tracker, "./", os.path.basename(cls.FFilePath).split(".")[0] + ".json")
break
else:
FSaveOutput(tracker, "./", "test.json")
break
# Blur the image to help in object detection
frameBlur = cv2.blur(img, blurDim)
# apply background subtraction to get moving objects
# the image produced has the objects and shadows
# background value #0
# shadow value #127
# objects value #255
mask = fgbg.apply(frameBlur)
# perform morphological operations to visualize objects better
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
# remove shadows
# function returns tuple, with the image mask as second arg
mask = cv2.threshold(mask, 128, 255, cv2.THRESH_BINARY)[1]
candidatesInfo = cv2.connectedComponentsWithStats(mask)
ret = candidatesInfo[0] - 1 # number of objects
labels = candidatesInfo[1] # labeled image
stats = np.delete(candidatesInfo[2],0, axis = 0) # statistics matrix of each label (deleting the first row --> backgorund)
centroids = np.delete(candidatesInfo[3], 0, axis = 0) # floating point centroid (x,y) output for each label, including the background label
# if the program can not detect any objects, continue;
if ret == 0:
if (desc):
count = count - 1
else:
count = count + 1
continue
fishes = tracker.update(stats, centroids, count)
if(imshow):
label_hue = np.uint8(179*labels/np.max(labels))
blank_ch = 255*np.ones_like(label_hue)
labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])
labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)
labeled_img[label_hue==0] = 0
colored_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR )
if (bool(fishes['objects'])):
for fish in fishes['objects'].keys():
x = int(fishes['objects'][fish].locations[-1][0])
y = int(fishes['objects'][fish].locations[-1][1])
center = (x,y)
cv2.circle(labeled_img, center, tracker.searchArea, (0,255,0), 1)
cv2.circle(colored_img, center, tracker.searchArea, (0,255,0), 1)
cv2.putText(labeled_img,"Objects: "+str(fishes["objects"].__len__()),(10,100), font, 1,(255,255,255),2,cv2.LINE_AA)
cv2.putText(labeled_img,str(count),(10,50), font, 1,(255,255,255),2,cv2.LINE_AA)
cv2.namedWindow("frames and BGS frames", cv2.WND_PROP_FULLSCREEN)
# cv2.setWindowProperty("frames and BGS frames", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
# cv2.imshow("frames and BGS frames",np.hstack((mask, img)))
cv2.imshow("frames and BGS frames",np.hstack((labeled_img, colored_img)))
k = cv2.waitKey(1 * play) & 0xff
if k == 27:
break
elif k == 0x6e:
print("right")
desc = False
count = count + 1
continue
elif k == 0x62:
print("left")
desc = True
count = count - 1
continue
# elif k == 0x52:
# print("up")
# elif k == 0x54:
# print("down")
elif k == 0x20:
print("Pause/Play")
play = not play
elif k!= dummy:
dummy = k
print(hex(k))
count = count + 1
# if count > number-1:
# count =
if readFromFile:
detectedFish = FSaveOutput(tracker, "./", os.path.basename(cls.FFilePath).split(".")[0] + ".json")
else:
detectedFish = FSaveOutput(tracker, "./", "test.json")
cv2.destroyWindow("frames and BGS frames")
return detectedFish
class centroidTracker():
# number of pixels around centroid to look at
searchArea = 30
def __init__(self):
self.objects = {
'objects' :{}
}
self.psuedoObjects = {
'objects': {}
}
self.archive = {
'objects': {}
}
def update(self, npArrayOfObjects, npArrayOfCentroids, frameIndex):
#** npArrayOfCentroids [centroids] > array of arrays with float entries
# example:
# [0:3] :[array([202.42857143,...57142857]), array([268.84, 885.72]), array([262.20689655,...65517241])]
# 0:array([202.42857143, 462.57142857])
# 1:array([268.84, 885.72])
# 2:array([262.20689655, 956.65517241])
#** npArrayOfObjects [stats] > array of arrays with float entries
# array entries description:
# 0: The leftmost (x) coordinate which is the inclusive start of the bounding box in the horizontal direction.
# 1: The topmost (y) coordinate which is the inclusive start of the bounding box in the vertical direction.
# 2: The horizontal size of the bounding box.
# 3: The vertical size of the bounding box.
# 4: The total area (in pixels) of the connected component.
# example:
# [0:3] :[array([197, 462, 12...ype=int32), array([263, 885, 12...ype=int32), array([254, 956, 17...ype=int32)]
# 0:array([197, 462, 12, 3, 21], dtype=int32)
# 1:array([263, 885, 12, 3, 25], dtype=int32)
# 2:array([254, 956, 17, 3, 29], dtype=int32)
everything = {}
# looping on all objects to register them as new objects
for i in range(npArrayOfObjects.shape[0]):
NNLocation = self.NAN(npArrayOfCentroids[i])
if not NNLocation:
newFish = Fish(npArrayOfCentroids[i], frameIndex, npArrayOfObjects[i])
self.psuedoObjects['objects'][newFish.id] = newFish
else:
self.psuedoObjects['objects'][NNLocation].updateInfo(npArrayOfCentroids[i], frameIndex, npArrayOfObjects[i])
self.delete(frameIndex)
self.archiveObject(frameIndex)
return self.objects
def NAN(self, centroid):
# (N)earest (A)ctive (N)eighbour
allObjectsLocations = np.array([0,0])
location = list()
if (bool(self.psuedoObjects['objects'])):
for key in self.psuedoObjects['objects'].keys():
location.append(key)
objectHandler = self.psuedoObjects['objects'][key]
allObjectsLocations = np.vstack((allObjectsLocations, objectHandler.getLastLocation()) )
allObjectsLocations = np.delete(allObjectsLocations, 0, axis=0)
distances = np.linalg.norm((allObjectsLocations - centroid), axis=1)
distanceToNN = np.min(distances)
if (distanceToNN<self.searchArea):
index = np.argmin(distances)
# it returns the key of the nearest neighbor in the `self.psuedoObjects['objects']`
return location[index]
else:
return False
else:
return False
def register(self):
return
def delete(self, currentFrame):
copyOfPsuedo = copy.deepcopy(self.psuedoObjects['objects'])
for fish in copyOfPsuedo.keys():
fishHandle = self.psuedoObjects['objects'][fish]
if (len(fishHandle.frames) < fishHandle.minAppear):
if (np.abs(fishHandle.frames[-1] - currentFrame) > 2*fishHandle.minAppear):
del self.psuedoObjects['objects'][fish]
if (len(fishHandle.frames) > 2*fishHandle.minAppear):
self.objects['objects'][fish] = self.psuedoObjects['objects'][fish]
return
def archiveObject(self, currentFrame):
copyOfObjects = copy.deepcopy(self.objects['objects'])
for fish in copyOfObjects.keys():
fishHandle = self.objects['objects'][fish]
if (np.abs(fishHandle.frames[-1] - currentFrame) > 2*fishHandle.maxDisappear):
self.archive['objects'][fish] = self.objects['objects'][fish]
del self.objects['objects'][fish]
return
class Fish():
ID = 0
maxDisappear = 5
minAppear = 30
def __init__(self, centroid, firstFrame, connectedLabelsWStats):
# ID given to the fish during first detection
self.id = Fish.ID
# Maximum number of frames the fish has to disappear to be deleted from the list
self.maxDisappear = Fish.maxDisappear
# Minimum number of frames the fish has to disappear to be added to the list
self.minAppear = Fish.minAppear
self.activeToggler = False
self.locations = list()
self.locations.append(centroid)
self.frames = list()
self.frames.append(firstFrame)
self.left = list()
self.left.append(connectedLabelsWStats[0])
self.top = list()
self.top.append(connectedLabelsWStats[1])
self.width = list()
self.width.append(connectedLabelsWStats[2])
self.height = list()
self.height.append(connectedLabelsWStats[3])
self.area = list()
self.area.append(connectedLabelsWStats[4])
Fish.ID += 1
return
def getLastLocation(self):
return self.locations[-1]
def updateInfo(self, centroid = None, currentFrame= None, objProps = None):
if ( (centroid is not None) and (currentFrame is not None) and (objProps is not None)):
self.locations.append(centroid)
self.frames.append(currentFrame)
self.left.append(objProps[0])
self.top.append(objProps[1])
self.width.append(objProps[2])
self.height.append(objProps[3])
self.area.append(objProps[4])
self.minAppear -= 1
return
def fetchFrame(count, readFromFile=False, filePath = False):
if not readFromFile:
# filesPath = "/home/mghobria/Pictures/SONAR_Images" ## laptop
# filesPath = "C:\\Users\\mghobria\\Downloads\\aris\\F" ## windows home PC
filesPath = "C:\\Users\\Mina Ghobrial\\Downloads\\SONAR" ## windows Laptop PC
imagesList = os.listdir(filesPath)
imagesList.sort()
# maximum number of images
number = max(enumerate(imagesList,1))[0]
imgList = list(enumerate(imagesList,1))
# get image path
try:
imgPath = os.path.join(filesPath, imgList[count][1])
img = cv2.imread(imgPath, cv2.IMREAD_GRAYSCALE)
except:
raise Exception("Could not load image from disk.\nDirectory to load from: {}\n".format(filesPath))
# read the image from disk
else:
img = readFromFile.getFrame(count-1)
return img
def FSaveOutput(cls, path, fileName):
"""
Takes the ouput of the tracking process and saves it into
the same path as the stored images.
Data saved is in the form of a JSON file.
the data has the following format:
{
"fishes": {
<fishNumber> : {
"ID": cls.id,
"locations": cls.locations,
"frames": cls.frames,
"objProps": cls.objProps
}
.
.
.
}
}
"""
print(cls.archive)
print(cls.objects)
data = dict()
for n in cls.archive['objects'].keys():
data[str(n)] = {
"ID" : cls.archive['objects'][n].id,
"locations" : tuple(map(tuple, cls.archive['objects'][n].locations)),
"frames" : cls.archive['objects'][n].frames,
"left": list(map(int, cls.archive['objects'][n].left)),
"top": list(map(int, cls.archive['objects'][n].top)),
"width": list(map(int, cls.archive['objects'][n].width)),
"height" : list(map(int, cls.archive['objects'][n].height)),
"area": list(map(int, cls.archive['objects'][n].area))
}
for n in cls.objects['objects'].keys():
if str(n) not in data:
data[str(n)] = {
"ID" : cls.objects['objects'][n].id,
"locations" : tuple(map(tuple, cls.objects['objects'][n].locations)),
"frames" : cls.objects['objects'][n].frames,
"left": list(map(int, cls.objects['objects'][n].left)),
"top": list(map(int, cls.objects['objects'][n].top)),
"width": list(map(int, cls.objects['objects'][n].width)),
"height" : list(map(int, cls.objects['objects'][n].height)),
"area": list(map(int, cls.objects['objects'][n].area))
}
path = os.path.join(path, fileName)
with open(path, 'w') as outFile:
json.dump(data, outFile)
return data