def getGoalData(imgDir, filenames, dbDir):
topLeftRatios, leftRightRatios, goalLineAngleRanges = [], [], []
for i in range(2):
import os
filename = filenames[i]
prevDir = os.getcwd()
img = cv2.imread(imgDir + filename, 1)
frameNum = filename.split('.')[0]
db = connectDB(dbDir)
playerData, ballData, goalData = getFrameData(db, frameNum)
disconnectDB(db)
bBox = goalData[0]
try:
import pickle
file_Name = "Pickle_File_color_data"
fileObject = open(file_Name,'rb')
color_ranges = pickle.load(fileObject)
ground_color_range=color_ranges['ground_color']
rangeH=ground_color_range[dir_num-1][0]
rangeS=ground_color_range[dir_num-1][1]
rangeV=ground_color_range[dir_num-1][2]
except:
rangeH, rangeS, rangeV = getGroundColor(img)
# print(rangeH, rangeS, rangeV)
rangeH, rangeS, rangeV = [0.2777777777777778, 0.36666666666666664], [0.28627450980392155, 0.6235294117647059], [0.22745098039215686, 0.592156862745098]
ground_mask = rangeToMask(img, [rangeH],[rangeS],[rangeV])
# cv2.imshow('hmm', ground_mask*255)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
playerMask = np.array(getPlayersMask(playerData, ballData))
white_thres = getLineRange(img, ground_mask, playerMask)
topLeftRatio, leftRightRatio, goalLineAngleRange = goalDataCollector(img, bBox, white_thres)
topLeftRatios.append(topLeftRatio)
leftRightRatios.append(leftRightRatio)
goalLineAngleRanges.append(goalLineAngleRange)
return topLeftRatios, leftRightRatios, goalLineAngleRanges
if __name__ == '__main__':
import os
import pickle
goalDict = {}
for i in range(1,11):
print(i)
dir_num = i
imgDir = './goalImgs/imgs'+str(dir_num)+'/'
filenames = sorted(os.listdir(imgDir))
dbDir = '../DB/data{0}.db'.format(dir_num)
topLeftRatios, leftRightRatios, goalLineAngleRanges = getGoalData(imgDir, filenames, dbDir)
goalDict[dir_num] = [topLeftRatios, leftRightRatios, goalLineAngleRanges]
f = open('goalData.dat', 'wb')
pickle.dump(goalDict, f)
f.close()
else:
return np.array([0,
0]), np.array([0,
0]), np.array([0, 0
]), np.array([0, 0])
if __name__ == '__main__':
imgDir = '../'
filename = '169.jpg'
img = cv2.imread(imgDir + filename, 1)
frameNum = filename.split('.')[0]
dbDir = '../DB/data1.db'
db = connectDB(dbDir)
playerData, ballData, goalData = getFrameData(db, frameNum)
disconnectDB(db)
bBox = goalData[0]
import pickle
f = open('goalData.dat', 'rb')
goalDict = pickle.load(f)
f.close()
goalData = goalDict[1]
# rangeH, rangeS, rangeV = getGroundColor(img)
# print(rangeH, rangeS, rangeV)
# rangeH, rangeS, rangeV = [0.28888888888888886, 0.3888888888888889], [0.3843137254901961, 0.6549019607843137], [0.5176470588235295, 0.7411764705882353]
rangeH, rangeS, rangeV = [0.2777777777777778, 0.36666666666666664
], [0.28627450980392155, 0.6235294117647059
], [0.22745098039215686, 0.592156862745098]
ground_mask = rangeToMask(img, [rangeH], [rangeS], [rangeV])
playerMask = np.array(getPlayersMask(playerData, ballData))
def getPointsForDict(img, dbDir):
db = connectDB(dbDir)
playerData, ballData, goalData = getFrameData(db, frameNum)
disconnectDB(db)
bBox = goalData[0]
# rangeH, rangeS, rangeV = getGroundColor(img)
# print(rangeH, rangeS, rangeV)
rangeH, rangeS, rangeV = [0.25555555555555554, 0.37222222222222223
], [0.27450980392156865, 0.6705882352941176
], [0.1568627450980392, 0.5176470588235295]
ground_mask = rangeToMask(img, [rangeH], [rangeS], [rangeV])
player_mask = np.array(getPlayersMask(playerData, ballData))
white_thres = getLineRange(img, ground_mask, player_mask)
[top1, top2, bottom1,
bottom2] = getGoalLine(img, bBox, white_thres,
[1.3885779892102874, 1.42697065429588],
[1.049079754601227, 0.9644970414201184],
[[-19.38738787344459, -9.387387873444592],
[8.677776114838897, 18.6777761148389]])
stands_mask = getStandMask(ground_mask, player_mask)
upper_bound, lower_bound = findOuterBoundaries(stands_mask, player_mask)
upper_bound, status = improveCorner(img, goalData, upper_bound,
white_thres)
print(upper_bound, lower_bound)
case_in, para_lines, para_lines1, _, _ = find_inner_boundaries(
upper_bound, lower_bound)
lines_img = getLineMask(img, ground_mask, player_mask, white_thres)
cv2.imshow('hmm', ground_mask * 255)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imshow('hmm', lines_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imshow('hmm', stands_mask * 255)
cv2.waitKey(0)
cv2.destroyAllWindows()
coords = getEndPoints(lines_img)
coords, lines = lineMerge(coords)
center_line_idx, label, mag = lines_and_mag(lines, coords, case_in,
para_lines, para_lines1)
pLine = coords[label]
pLine[0] = pLine[0][::-1]
pLine[1] = pLine[1][::-1]
pLineTop = pLine[0] if pLine[0][1] < pLine[1][1] else pLine[1]
cv2.line(img, tuple(pLine[0]), tuple(pLine[1]), (0, 255, 0), 3)
cv2.circle(img, tuple(pLineTop), 5, (0, 0, 255), -5)
[lPt, cPt, rPt] = upper_bound.T
def absSlope(pt1, pt2):
return abs((pt2[1] - pt1[1]) / (pt2[0] - pt1[0]))
if absSlope(lPt, cPt) < absSlope(cPt, rPt):
ub1 = lPt
ub2 = cPt
alpha = 1
else:
ub1 = cPt
ub2 = rPt
alpha = -1
extendedPt = line_intersection(pLine, (ub1, ub2))
extendedPt = extendedPt.astype(np.int_)
cv2.circle(img, tuple(extendedPt), 5, (0, 0, 255), -5)
interPt = cPt
if np.linalg.norm(interPt - cPt) < 500:
status = 1
else:
interPt = cPt
status = 0
interPt = interPt.astype(np.int_)
lastPoint = (interPt - extendedPt)
cv2.circle(
img,
tuple(
line_intersection(((lastPoint + pLineTop), pLineTop),
(bottom1, bottom2)).astype(np.int_)), 5,
(255, 0, 255), -5)
alpha = alpha * np.pi / 180
rotMat = np.array([[np.cos(alpha), np.sin(alpha)],
[-1 * np.sin(alpha), np.cos(alpha)]])
lastPoint = np.matmul(rotMat, lastPoint)
lastPoint = pLineTop + lastPoint
lastPoint = line_intersection((lastPoint, pLineTop), (bottom1, bottom2))
lastPoint = lastPoint.astype(np.int_)
cv2.circle(img, tuple(lastPoint), 5, (0, 0, 255), -5)
cv2.circle(img, tuple(bottom1), 5, (0, 255, 0), -5)
cv2.circle(img, tuple(bottom2), 5, (0, 255, 0), -5)
cv2.circle(img, tuple(ub1), 5, (255, 0, 0), -5)
cv2.circle(img, tuple(ub2), 5, (255, 0, 0), -5)
print(extendedPt, cPt, lastPoint, pLineTop)
return interPt, status