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fasterMultiprocessing2.py
496 lines (404 loc) · 22 KB
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fasterMultiprocessing2.py
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import cv2
import numpy as np
import math
import zebrazoom.videoFormatConversion.zzVideoReading as zzVideoReading
from zebrazoom.code.extractParameters import extractParameters
from zebrazoom.code.preprocessImage import preprocessImage
from ._base import register_tracking_method
from ._fasterMultiprocessingBase import BaseFasterMultiprocessing
from ._tailExtremityTracking import TailTrackingExtremityDetectMixin
class FasterMultiprocessing2(BaseFasterMultiprocessing, TailTrackingExtremityDetectMixin):
def __init__(self, videoPath, wellPositions, hyperparameters):
super().__init__(videoPath, wellPositions, hyperparameters)
# if self._hyperparameters["eyeTracking"]:
# self._trackingEyesAllAnimalsList = []
# else:
# self._trackingEyesAllAnimals = 0
# for wellNumber in range(0, self._hyperparameters["nbWells"]):
# if self._hyperparameters["eyeTracking"]:
# self._trackingHeadingAllAnimalsList.append(np.zeros((self._hyperparameters["nbAnimalsPerWell"], self._lastFrame-self._firstFrame+1, 8)))
# self._trackingProbabilityOfGoodDetectionList.append(np.zeros((self._hyperparameters["nbAnimalsPerWell"], self._lastFrame-self._firstFrame+1)))
# if not(self._hyperparameters["nbAnimalsPerWell"] > 1) and not(self._hyperparameters["headEmbeded"]) and (self._hyperparameters["findHeadPositionByUserInput"] == 0) and (self._hyperparameters["takeTheHeadClosestToTheCenter"] == 0):
# self._trackingProbabilityOfGoodDetectionList = []
# else:
# self._trackingProbabilityOfGoodDetectionList = 0
# self._trackingProbabilityOfGoodDetectionList = []
def _adjustParameters(self, i, back, frame, initialCurFrame, widgets):
return None
def _findTheTwoSides(self, headPosition, bodyContour, curFrame, bestAngle):
# Finding the 'mouth' of the fish
unitVector = np.array([math.cos(bestAngle), math.sin(bestAngle)])
factor = 1
headPos = np.array(headPosition)
testBorder = headPos + factor * unitVector
testBorder = testBorder.astype(int)
while (cv2.pointPolygonTest(bodyContour, (float(testBorder[0]), float(testBorder[1])), True) > 0) and (factor < 100) and (testBorder[0] >= 0) and (testBorder[1] >= 0) and (testBorder[0] < len(curFrame[0])) and (testBorder[1] < len(curFrame)):
factor = factor + 1
testBorder = headPos + factor * unitVector
# Finding the indexes of the two "border points" along the contour (these are the two points that are the closest from the 'mouth' of fish)
xOtherBorder = testBorder[0]
yOtherBorder = testBorder[1]
minDist1 = 1000000000000
minDist2 = 1000000000000
indMin1 = 0
indMin2 = 0
for i in range(0, len(bodyContour)):
Pt = bodyContour[i][0]
dist = math.sqrt((Pt[0] - xOtherBorder)**2 + (Pt[1] - yOtherBorder)**2)
if (dist < minDist1):
minDist2 = minDist1
indMin2 = indMin1
minDist1 = dist
indMin1 = i
else:
if (dist < minDist2):
minDist2 = dist
indMin2 = i
return indMin1, indMin2
def _computeHeading(self, initialContour, lenX, lenY, headPosition):
xmin = lenX
ymin = lenY
xmax = 0
ymax = 0
for pt in initialContour:
if pt[0][0] < xmin:
xmin = pt[0][0]
if pt[0][1] < ymin:
ymin = pt[0][1]
if pt[0][0] > xmax:
xmax = pt[0][0]
if pt[0][1] > ymax:
ymax = pt[0][1]
for pt in initialContour:
pt[0][0] = pt[0][0] - xmin
pt[0][1] = pt[0][1] - ymin
headPosition = [headPosition[0] - xmin, headPosition[1] - ymin]
image = np.zeros((ymax - ymin, xmax - xmin))
image[:, :] = 255
image = image.astype(np.uint8)
kernel = np.ones((3, 3), np.uint8)
if type(initialContour) != int:
cv2.fillPoly(image, pts =[initialContour], color=(0))
image[:,0] = 255
image[0,:] = 255
image[:, len(image[0])-1] = 255
image[len(image)-1, :] = 255
originalShape = 255 - image
# Heading calculation: first approximation
minWhitePixel = 1000000000
bestAngle = 0
nTries = 50
for i in range(0, nTries):
angleOption = i * ((2 * math.pi) / nTries)
startPoint = (int(headPosition[0]), int(headPosition[1]))
endPoint = (int(headPosition[0] + 100000 * math.cos(angleOption)), int(headPosition[1] + 100000 * math.sin(angleOption)))
testImage = originalShape.copy()
testImage = cv2.line(testImage, startPoint, endPoint, (0), 1)
nbWhitePixels = cv2.countNonZero(testImage)
if nbWhitePixels < minWhitePixel:
minWhitePixel = nbWhitePixels
bestAngle = angleOption
bestAngleAfterFirstStep = bestAngle
# Heading calculation: second (and refined) approximation
# Searching for the optimal value of iterationsForErodeImageForHeadingCalculation
countTries = 0
nbIterations2nbWhitePixels = {}
if "iterationsForErodeImageForHeadingCalculation" in self._hyperparameters:
iterationsForErodeImageForHeadingCalculation = self._hyperparameters["iterationsForErodeImageForHeadingCalculation"]
else:
iterationsForErodeImageForHeadingCalculation = 4
kernel = np.ones((3, 3), np.uint8)
nbWhitePixelsMax = 0.3 * cv2.contourArea(initialContour)
while (iterationsForErodeImageForHeadingCalculation > 0) and (countTries < 50) and not(iterationsForErodeImageForHeadingCalculation in nbIterations2nbWhitePixels):
testImage2 = cv2.erode(testImage, kernel, iterations = iterationsForErodeImageForHeadingCalculation)
nbWhitePixels = cv2.countNonZero(testImage2)
nbIterations2nbWhitePixels[iterationsForErodeImageForHeadingCalculation] = nbWhitePixels
if nbWhitePixels < nbWhitePixelsMax:
iterationsForErodeImageForHeadingCalculation = iterationsForErodeImageForHeadingCalculation - 1
if nbWhitePixels >= nbWhitePixelsMax:
iterationsForErodeImageForHeadingCalculation = iterationsForErodeImageForHeadingCalculation + 1
countTries = countTries + 1
best_iterations = 0
minDist = 10000000000000
for iterations in nbIterations2nbWhitePixels:
nbWhitePixels = nbIterations2nbWhitePixels[iterations]
dist = abs(nbWhitePixels - nbWhitePixelsMax)
if dist < minDist:
minDist = dist
best_iterations = iterations
iterationsForErodeImageForHeadingCalculation = best_iterations
self._hyperparameters["iterationsForErodeImageForHeadingCalculation"] = iterationsForErodeImageForHeadingCalculation
testImage2 = cv2.erode(originalShape.copy(), kernel, iterations = iterationsForErodeImageForHeadingCalculation)
maxDist = -1
for i in range(0, nTries):
angleOption = bestAngleAfterFirstStep - (math.pi / 5) + i * ((2 * (math.pi / 5)) / nTries)
startPoint = (int(headPosition[0]), int(headPosition[1]))
endPoint = (int(headPosition[0] + 100000 * math.cos(angleOption)), int(headPosition[1] + 100000 * math.sin(angleOption)))
testImage = testImage2.copy()
testImage = cv2.line(testImage, startPoint, endPoint, (0), 1)
nbWhitePixels = cv2.countNonZero(testImage)
if nbWhitePixels < minWhitePixel:
minWhitePixel = nbWhitePixels
bestAngle = angleOption
theta = bestAngle
if self._hyperparameters["debugHeadingCalculation"]:
img2 = image.copy()
img2 = cv2.cvtColor(img2, cv2.COLOR_GRAY2RGB)
cv2.line(img2, (int(len(img2[0])/2), int(len(img2)/2)), (int(len(img2[0])/2 + 20 * math.cos(theta)), int(len(img2)/2 + 20 * math.sin(theta))), (255,0,255), 1)
self._debugFrame(img2, title='imgForHeadingCalculation')
return theta + math.pi
# def computeHeading(self, thresh1, x, y):
# videoWidth = self._hyperparameters["videoWidth"]
# videoHeight = self._hyperparameters["videoHeight"]
# headSize = self._hyperparameters["headSize"]
# ymin = y - headSize - 10 if y - headSize >= 0 else 0
# ymax = y + headSize + 10 if y + headSize < len(thresh1) else len(thresh1) - 1
# xmin = x - headSize - 10 if x - headSize >= 0 else 0
# xmax = x + headSize + 10 if x + headSize < len(thresh1[0]) else len(thresh1[0]) - 1
# img = thresh1[int(ymin):int(ymax), int(xmin):int(xmax)]
# img[0,:] = 255
# img[len(img)-1,:] = 255
# img[:,0] = 255
# img[:,len(img[0])-1] = 255
# y2, x2 = np.nonzero(img)
# x2 = x2 - np.mean(x2)
# y2 = y2 - np.mean(y2)
# coords = np.vstack([x2, y2])
# cov = np.cov(coords)
# evals, evecs = np.linalg.eig(cov)
# sort_indices = np.argsort(evals)[::-1]
# x_v1, y_v1 = evecs[:, sort_indices[0]] # Eigenvector with largest eigenvalue
# x_v2, y_v2 = evecs[:, sort_indices[1]]
# scale = 20
# theta = self._calculateAngle(0, 0, x_v1, y_v1)
# theta = (theta - math.pi/2) % (2 * math.pi)
# if False:
# width = len(img[0])
# height = len(img)
# option1X = x + width * math.cos(theta)
# option1Y = y + height * math.sin(theta)
# option2X = x - width * math.cos(theta)
# option2Y = y - height * math.sin(theta)
# if math.sqrt((option1X - width)**2 + (option1Y - height)**2) < math.sqrt((option2X - width)**2 + (option2Y - height)**2):
# theta += theta + math.pi
# if self._hyperparameters["debugHeadingCalculation"]:
# img2 = img.copy()
# img2 = cv2.cvtColor(img2, cv2.COLOR_GRAY2BGR)
# cv2.line(img2, (int(len(img2[0])/2), int(len(img2)/2)), (int(len(img[0])/2 + 20 * math.cos(theta)), int(len(img)/2 + 20 * math.sin(theta))), (255,0,0), 1)
# self._debugFrame(img2, title='imgForHeadingCalculation')
# return theta
def _findOptimalIdCorrespondance(self, wellNumber, i):
from scipy.optimize import linear_sum_assignment
if i > self._firstFrame:
costMatrix = np.zeros((len(self._trackingHeadTailAllAnimalsList[wellNumber]), len(self._trackingHeadTailAllAnimalsList[wellNumber])))
for animalIdPrev in range(0, len(self._trackingHeadTailAllAnimalsList[wellNumber])):
for animalIdCur in range(0, len(self._trackingHeadTailAllAnimalsList[wellNumber])):
coordPrevX = self._trackingHeadTailAllAnimalsList[wellNumber][animalIdPrev, i-self._firstFrame-1][0][0]
coordPrevY = self._trackingHeadTailAllAnimalsList[wellNumber][animalIdPrev, i-self._firstFrame-1][0][1]
coordCurX = self._trackingHeadTailAllAnimalsList[wellNumber][animalIdCur, i-self._firstFrame][0][0]
coordCurY = self._trackingHeadTailAllAnimalsList[wellNumber][animalIdCur, i-self._firstFrame][0][1]
# TO DO: add some very high cost for (0, 0) coordinates
costMatrix[animalIdPrev, animalIdCur] = math.sqrt((coordCurX - coordPrevX)**2 + (coordCurY - coordPrevY)**2)
row_ind, col_ind = linear_sum_assignment(costMatrix)
return col_ind
else:
return np.array([k for k in range(0, len(self._trackingHeadTailAllAnimalsList[wellNumber]))])
def _switchIdentities(self, correspondance, wellNumber, i):
trackingHeadTailAllAnimalsListWellNumberOriginal = self._trackingHeadTailAllAnimalsList[wellNumber][:, i-self._firstFrame].copy()
trackingHeadingAllAnimalsListWellNumberOriginal = self._trackingHeadingAllAnimalsList[wellNumber][:, i-self._firstFrame].copy()
for previousId, newId in enumerate(correspondance):
self._trackingHeadTailAllAnimalsList[wellNumber][previousId, i-self._firstFrame] = trackingHeadTailAllAnimalsListWellNumberOriginal[newId]
for previousId, newId in enumerate(correspondance):
self._trackingHeadingAllAnimalsList[wellNumber][previousId, i-self._firstFrame] = trackingHeadingAllAnimalsListWellNumberOriginal[newId]
def _findCenterByIterativelyDilating(self, initialContour, lenX, lenY):
x = 0
y = 0
xmin = lenX
ymin = lenY
xmax = 0
ymax = 0
for pt in initialContour:
if pt[0][0] < xmin:
xmin = pt[0][0]
if pt[0][1] < ymin:
ymin = pt[0][1]
if pt[0][0] > xmax:
xmax = pt[0][0]
if pt[0][1] > ymax:
ymax = pt[0][1]
for pt in initialContour:
pt[0][0] = pt[0][0] - xmin
pt[0][1] = pt[0][1] - ymin
image = np.zeros((ymax - ymin, xmax - xmin))
image[:, :] = 255
image = image.astype(np.uint8)
kernel = np.ones((3, 3), np.uint8)
if type(initialContour) != int:
cv2.fillPoly(image, pts =[initialContour], color=(0))
image[:,0] = 255
image[0,:] = 255
image[:, len(image[0])-1] = 255
image[len(image)-1, :] = 255
nbBlackPixels = 1
dilateIter = 0
while nbBlackPixels > 0:
dilateIter = dilateIter + 1
dilatedImage = cv2.dilate(image, kernel, iterations=dilateIter)
nbBlackPixels = cv2.countNonZero(255-dilatedImage)
dilateIter = dilateIter - 1
dilatedImage = cv2.dilate(image, kernel, iterations=dilateIter)
dilatedImage[:,0] = 255
dilatedImage[0,:] = 255
dilatedImage[:, len(dilatedImage[0])-1] = 255
dilatedImage[len(dilatedImage)-1, :] = 255
contours, hierarchy = cv2.findContours(dilatedImage, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
maxContour = 0
maxContourArea = 0
for contour in contours:
contourArea = cv2.contourArea(contour)
if contourArea < int((xmax - xmin) * (ymax - ymin) * 0.8):
if contourArea > maxContourArea:
maxContourArea = contourArea
maxContour = contour
M = cv2.moments(maxContour)
if M['m00']:
x = int(M['m10']/M['m00'])
y = int(M['m01']/M['m00'])
return [x + xmin, y + ymin]
def _formatOutput(self):
# if self._hyperparameters["postProcessMultipleTrajectories"]:
# self._postProcessMultipleTrajectories(self._trackingHeadTailAllAnimalsList[wellNumber], self._trackingProbabilityOfGoodDetectionList[wellNumber])
if self._auDessusPerAnimalIdList == None:
return {wellNumber: extractParameters([self._trackingHeadTailAllAnimalsList[wellNumber], self._trackingHeadingAllAnimalsList[wellNumber], [], 0, 0, 0], wellNumber, self._hyperparameters, self._videoPath, self._wellPositions, self._background)
for wellNumber in range(self._firstWell, self._lastWell + 1)}
else:
return {wellNumber: extractParameters([self._trackingHeadTailAllAnimalsList[wellNumber], self._trackingHeadingAllAnimalsList[wellNumber], [], 0, 0, self._auDessusPerAnimalIdList[wellNumber]], wellNumber, self._hyperparameters, self._videoPath, self._wellPositions, self._background)
for wellNumber in range(self._firstWell, self._lastWell + 1)}
def run(self):
self._background = self.getBackground()
cap = zzVideoReading.VideoCapture(self._videoPath)
if (cap.isOpened()== False):
print("Error opening video stream or file")
lastFrameRememberedForBackgroundExtract = 0
# if self._hyperparameters["backgroundSubtractorKNN"]:
# fgbg = cv2.createBackgroundSubtractorKNN()
# for i in range(0, min(self._lastFrame - 1, 500), int(min(self._lastFrame - 1, 500) / 10)):
# cap.set(1, min(self._lastFrame - 1, 500) - i)
# ret, frame = cap.read()
# fgmask = fgbg.apply(frame)
# cap.release()
# cap = zzVideoReading.VideoCapture(videoPath)
i = self._firstFrame
if self._firstFrame:
cap.set(1, self._firstFrame)
previousFrames = None
widgets = None
while (i < self._lastFrame + 1):
if (self._hyperparameters["freqAlgoPosFollow"] != 0) and (i % self._hyperparameters["freqAlgoPosFollow"] == 0):
print("Tracking: frame:",i)
if self._hyperparameters["popUpAlgoFollow"]:
from zebrazoom.code.popUpAlgoFollow import prepend
prepend("Tracking: frame:" + str(i))
if self._hyperparameters["debugTracking"]:
print("frame:",i)
ret, frame = cap.read()
if ret:
if self._hyperparameters["invertBlackWhiteOnImages"]:
frame = 255 - frame
if self._hyperparameters["imagePreProcessMethod"]:
frame = preprocessImage(frame, self._hyperparameters)
# if self._hyperparameters["backgroundSubtractorKNN"]:
# frame = fgbg.apply(frame)
# frame = 255 - frame
for wellNumber in range(self._firstWell, self._lastWell + 1):
minPixelDiffForBackExtract = self._hyperparameters["minPixelDiffForBackExtract"]
xtop = self._wellPositions[wellNumber]['topLeftX']
ytop = self._wellPositions[wellNumber]['topLeftY']
lenX = self._wellPositions[wellNumber]['lengthX']
lenY = self._wellPositions[wellNumber]['lengthY']
# if self._hyperparameters["backgroundSubtractorKNN"]:
# grey = frame
# else:
grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
curFrame = grey[ytop:ytop+lenY, xtop:xtop+lenX].copy()
initialCurFrame = curFrame.copy()
# if not(self._hyperparameters["backgroundSubtractorKNN"]):
back = self._background[ytop:ytop+lenY, xtop:xtop+lenX]
putToWhite = ( curFrame.astype('int32') >= (back.astype('int32') - minPixelDiffForBackExtract) )
curFrame[putToWhite] = 255
# else:
# self._hyperparameters["paramGaussianBlur"] = int(math.sqrt(cv2.countNonZero(255 - curFrame) / self._hyperparameters["nbAnimalsPerWell"]) / 2) * 2 + 1
# if self._hyperparameters["paramGaussianBlur"]:
# blur = cv2.GaussianBlur(curFrame, (self._hyperparameters["paramGaussianBlur"], self._hyperparameters["paramGaussianBlur"]),0)
# else:
# blur = curFrame
headPositionFirstFrame = 0
ret, thresh1 = cv2.threshold(curFrame.copy(), 254, 255, cv2.THRESH_BINARY)
thresh1[:,0] = 255
thresh1[0,:] = 255
thresh1[:, len(thresh1[0])-1] = 255
thresh1[len(thresh1)-1, :] = 255
contours, hierarchy = cv2.findContours(thresh1,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
areas = np.array([cv2.contourArea(contour) for contour in contours])
maxIndexes = []
for numFish in range(0, self._hyperparameters["nbAnimalsPerWell"]):
maxArea = -1
maxInd = -1
for idx, area in enumerate(areas):
if area > maxArea and area > 0.7 * self._hyperparameters["minAreaBody"] and area < 1.3 * self._hyperparameters["maxAreaBody"]:
maxArea = area
maxInd = idx
areas[maxInd] = -1
if maxInd != -1:
maxIndexes.append(maxInd)
for animal_Id, idx in enumerate(maxIndexes):
bodyContour = contours[idx]
M = cv2.moments(bodyContour)
if M['m00']:
# x = int(M['m10']/M['m00'])
# y = int(M['m01']/M['m00'])
# headPosition = [x, y]
headPosition = self._findCenterByIterativelyDilating(bodyContour.copy(), len(curFrame[0]), len(curFrame))
self._trackingHeadTailAllAnimalsList[wellNumber][animal_Id, i-self._firstFrame][0][0] = headPosition[0]
self._trackingHeadTailAllAnimalsList[wellNumber][animal_Id, i-self._firstFrame][0][1] = headPosition[1]
heading = self._computeHeading(bodyContour.copy(), len(curFrame[0]), len(curFrame), headPosition)
self._trackingHeadingAllAnimalsList[wellNumber][animal_Id, i-self._firstFrame] = heading
if self._hyperparameters["trackTail"] == 1 :
res = self._findTheTwoSides(headPosition, bodyContour, curFrame, heading)
# Finding tail extremity
rotatedContour = bodyContour.copy()
rotatedContour = self._rotate(rotatedContour,int(headPosition[0]),int(headPosition[1]),heading)
debugAdv = False
[MostCurvyIndex, distance2] = self._findTailExtremete(rotatedContour, bodyContour, headPosition[0], int(res[0]), int(res[1]), debugAdv, curFrame, self._hyperparameters["tailExtremityMaxJugeDecreaseCoeff"])
# Getting Midline
if self._hyperparameters["detectMouthInsteadOfHeadTwoSides"] == 0:
tail = self._getMidline(int(res[0]), int(res[1]), int(MostCurvyIndex), bodyContour, curFrame, self._nbTailPoints-1, distance2, debugAdv)
else:
tail = self._getMidline(int(res[0]), int(res[1]), int(MostCurvyIndex), bodyContour, curFrame, self._nbTailPoints, distance2, debugAdv)
tail = np.array([tail[0][1:len(tail[0])]])
tail = np.insert(tail, 0, headPosition, axis=1)
self._trackingHeadTailAllAnimalsList[wellNumber][animal_Id, i-self._firstFrame] = tail
# Eye tracking for frame i
# if self._hyperparameters["eyeTracking"]:
# self._eyeTracking(animalId, i, frame, thresh1, self._trackingHeadingAllAnimalsList[wellNumber], self._trackingHeadTailAllAnimalsList[wellNumber], self._trackingHeadingAllAnimalsList[wellNumber])
correspondance = self._findOptimalIdCorrespondance(wellNumber, i)
self._switchIdentities(correspondance, wellNumber, i)
self._debugTracking(i, self._trackingHeadTailAllAnimalsList[wellNumber], self._trackingHeadingAllAnimalsList[wellNumber], curFrame)
if self._hyperparameters["updateBackgroundAtInterval"]:
self._updateBackgroundAtInterval(i, wellNumber, initialCurFrame, self._trackingHeadTailAllAnimalsList[wellNumber], initialCurFrame)
if ("updateBackgroundAtIntervalRememberLastFrame" in self._hyperparameters) and (self._hyperparameters["updateBackgroundAtIntervalRememberLastFrame"]):
lastFrameRememberedForBackgroundExtract = self._updateBackgroundAtIntervalRememberLastFrame(i, wellNumber, grey, lastFrameRememberedForBackgroundExtract)
if self._hyperparameters["freqAlgoPosFollow"]:
if i % self._hyperparameters["freqAlgoPosFollow"] == 0:
print("Tracking at frame", i)
if self._hyperparameters["detectMovementWithRawVideoInsideTracking"]:
previousFrames = self._detectMovementWithRawVideoInsideTracking(i, grey, previousFrames)
paramsAdjusted = self._adjustParameters(i, back, frame, initialCurFrame, widgets)
if paramsAdjusted is not None:
i, widgets = paramsAdjusted
cap.set(1, i)
else:
i = i + 1
cap.release()
return self._formatOutput()
register_tracking_method('fasterMultiprocessing2', FasterMultiprocessing2)