def get_G(x0, x1, y0, y1, width, height, side, data):
G = nx.Graph()
min_x, max_x, min_y, max_y = x0, x1, y0, y1
col = []
row = []
for tid in data.tid.unique():
themap = cv.CreateMat(height, width, cv.CV_16UC1)
cv.SetZero(themap)
for p in trajmap.pairwise(data[data.tid == tid].values):
x0, y0, x1, y1 = p[0][1], p[0][2], p[1][1], p[1][2]
oy = height - int((y0 - min_y) / side)
ox = int((x0 - min_x) / side)
dy = height - int((y1 - min_y) / side)
dx = int((x1 - min_x) / side)
cv.Line(themap, (ox, oy), (dx, dy), (32), 1, cv.CV_AA)
node_set = set()
for y, x in zip(*np.matrix(themap).nonzero()):
node_set.add((x, y))
a = x + (height - y) * width
for _x, _y in [(x - 1, y), (x, y - 1), (x - 1, y - 1), (x + 1, y),
(x, y + 1), (x + 1, y + 1), (x - 1, y + 1),
(x + 1, y - 1)]:
if (_x, _y) in node_set:
_a = _x + (height - _y) * width
G.add_edge(a, _a)
for tup in zip(*np.matrix(themap).nonzero()):
row.append(tup[1] + (height - tup[0]) * width)
col.append(tid)
sag = scipy.sparse.csc_matrix(([1] * len(row), (row, col)),
shape=(max(row) + 1, max(col) + 1))
return sag, G
def on_trackbar(position):
# create the image for putting in it the founded contours
contours_image = cv.CreateImage((_SIZE, _SIZE), 8, 3)
# compute the real level of display, given the current position
levels = position - 3
# initialisation
_contours = contours
if levels <= 0:
# zero or negative value
# => get to the nearest face to make it look more funny
_contours = contours.h_next().h_next().h_next()
# first, clear the image where we will draw contours
cv.SetZero(contours_image)
# draw contours in red and green
cv.DrawContours(contours_image, _contours, _red, _green, levels, 3,
cv.CV_AA, (0, 0))
# finally, show the image
cv.ShowImage("contours", contours_image)
def url_jpg_contours(url):
position = 100
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
im = cv.DecodeImage(imagefiledata, cv.CV_LOAD_IMAGE_COLOR)
col_edge = cv.CreateImage((im.width, im.height), 8, 3)
# convert to grayscale
gray_im = cv.CreateImage((im.width, im.height), 8, 1)
edge_im = cv.CreateImage((im.width, im.height), 8, 1)
cv.CvtColor(im, gray_im, cv.CV_BGR2GRAY)
cv.Canny(gray_im, edge_im, position, position * 3, 3)
cv.SetZero(col_edge)
# copy edge points
cv.Copy(im, col_edge, edge_im)
edge_im_array = np.asarray(edge_im[:])
ret, edge_im_array = cv2.threshold(edge_im_array, 127, 255,
cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(edge_im_array, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
scale = 10000.0
points = []
for contour in contours:
for i in contour:
for j in i:
lng_offset = j[0] / scale
lat_offset = j[1] / scale
points.append([lng_offset, lat_offset])
return points
def on_trackbar(position):
# create the image for putting in it the founded contours
contours_image = cv.CreateImage((_SIZE, _SIZE), 8, 3)
# compute the real level of display, given the current position
levels = position - 3
# initialisation
_contours = contours
if levels <= 0:
# zero or negative value
# => get to the nearest face to make it look more funny
_contours = contours.h_next().h_next().h_next()
# first, clear the image where we will draw contours
cv.SetZero(contours_image)
# draw contours in red and green
cv.DrawContours(
contours_image, #dest image
_contours, #input contours
_red, #color of external contour
_green, #color of internal contour
levels, #maxlevel of contours to draw
_contour_thickness,
cv.CV_AA, #line type
(0, 0)) #offset
# finally, show the image
cv.ShowImage("contours", contours_image)
def __init__(self, internalResolutionX, internalResolutionY, numCameras, configHolder):
self._internalResolutionX = internalResolutionX
self._internalResolutionY = internalResolutionY
self._videoDir = configHolder.getVideoDir()
self._selectedCameraId = 0
self._currentNumCameras = numCameras
self._miniSizeX = self._internalResolutionX / 5
self._miniSizeY = self._internalResolutionY / 5
self._numMiniRows = int(self._internalResolutionY / self._miniSizeY)
self._numMiniColumns = 1 + int(numCameras / self._numMiniRows)
self._maxImages = self._numMiniColumns * self._numMiniRows
self._miniAreaWidth = self._numMiniColumns * self._miniSizeX
self._bigAreaWidth = self._internalResolutionX - self._miniAreaWidth
self._bigAreaHeight = int((float(self._bigAreaWidth) / self._internalResolutionX) * self._internalResolutionY)
self._bigAreaTop = int((self._internalResolutionY - self._bigAreaHeight) / 2)
self._mixMat = createMat(self._internalResolutionX, self._internalResolutionY)
self._convertedMat = createMat(self._internalResolutionX, self._internalResolutionY)
cv.SetZero(self._mixMat)
self._bigRegion = cv.GetSubRect(self._mixMat, (0, self._bigAreaTop, self._bigAreaWidth, self._bigAreaHeight))
self._smallImageAreaList = []
self._cameraBaseFileNameList = []
for i in range(self._maxImages):
columnId = int(i / self._numMiniRows)
xpos = self._bigAreaWidth + (columnId * self._miniSizeX)
ypos = int(i % self._numMiniRows) * self._miniSizeY
smallRegion = cv.GetSubRect(self._mixMat, (xpos, ypos, self._miniSizeX, self._miniSizeY))
self._smallImageAreaList.append(smallRegion)
self._cameraBaseFileNameList.append("cam" + str(i) + "_")
self._debugCounter = 0
def convertto():
image=cv.LoadImageM(getpath(),cv.CV_LOAD_IMAGE_COLOR)
newimage=cv.CreateMat(image.rows,image.cols,image.type)
cv.SetZero(newimage)
cv.ConvertScale(image,newimage,2.2,50.0)
display(image,"Source")
display(newimage,"Destination")
cv.WaitKey(0)
def cal_fromcorners(self, good):
"""
:param good: Good corner positions and boards
:type good: [(corners, ChessboardInfo)]
"""
boards = [b for (_, b) in good]
ipts = self.mk_image_points(good)
opts = self.mk_object_points(boards)
npts = self.mk_point_counts(boards)
intrinsics = cv.CreateMat(3, 3, cv.CV_64FC1)
if self.calib_flags & cv2.CALIB_RATIONAL_MODEL:
distortion = cv.CreateMat(8, 1, cv.CV_64FC1) # rational polynomial
else:
distortion = cv.CreateMat(5, 1, cv.CV_64FC1) # plumb bob
cv.SetZero(intrinsics)
cv.SetZero(distortion)
# If FIX_ASPECT_RATIO flag set, enforce focal lengths have 1/1 ratio
intrinsics[0, 0] = 1.0
intrinsics[1, 1] = 1.0
cv.CalibrateCamera2(opts,
ipts,
npts,
self.size,
intrinsics,
distortion,
cv.CreateMat(len(good), 3, cv.CV_32FC1),
cv.CreateMat(len(good), 3, cv.CV_32FC1),
flags=self.calib_flags)
self.intrinsics = intrinsics
self.distortion = distortion
# R is identity matrix for monocular calibration
self.R = cv.CreateMat(3, 3, cv.CV_64FC1)
cv.SetIdentity(self.R)
self.P = cv.CreateMat(3, 4, cv.CV_64FC1)
cv.SetZero(self.P)
self.mapx = cv.CreateImage(self.size, cv.IPL_DEPTH_32F, 1)
self.mapy = cv.CreateImage(self.size, cv.IPL_DEPTH_32F, 1)
self.set_alpha(0.0)
def medianfiltering():
src = cv.LoadImageM(k, cv.CV_LOAD_IMAGE_COLOR)
dst = cv.CreateImage((src.width, src.height), 8, src.channels)
cv.SetZero(dst)
cv.NamedWindow("Median Filtering", 1)
cv.NamedWindow("After Filtering", 1)
cv.Smooth(src, dst, cv.CV_MEDIAN, 9, 9)
cv.ShowImage("Median Filtering", src)
cv.ShowImage("After Filtering", dst)
cv.WaitKey(0)
def on_trackbar(position):
cv.Smooth(gray, edge, cv.CV_BLUR, 3, 3, 0)
cv.Not(gray, edge)
# run the edge dector on gray scale
cv.Canny(gray, edge, position, position * 3, 3)
# reset
cv.SetZero(col_edge)
# copy edge points
cv.Copy(im, col_edge, edge)
# show the im
cv.ShowImage(win_name, col_edge)
def drawrandline():
rand = Random()
img = cv.CreateImage((700, 1000), 8, 3)
cv.SetZero(img)
cv.NamedWindow("RandomViewer", 1)
for i in range(100):
cv.Line(img, (rand.randrange(0, 700), rand.randrange(0, 1000)),
(300, 200), (rand.randrange(0, 256), rand.randrange(
0, 256), rand.randrange(0, 256)), 1, 8, 0)
cv.ShowImage("RandomViewer", img)
cv.WaitKey(5)
cv.PutText(img, "Hello OpenCV", (100, 200),
cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 5, 10, 0, 1, 8),
(255, 255, 255))
cv.ShowImage("RandomViewer", img)
cv.WaitKey(0)
cv.DestroyWindow("RandomViewer")
def on_trackbar(self, position):
cv.Smooth(self.source_image, self.edge, cv.CV_BLUR, 3, 3, 0)
cv.Not(self.source_image, self.edge)
# run the edge dector on gray scale
cv.Canny(self.source_image, self.edge, position, position * 3, 3)
# reset
cv.SetZero(self.col_edge)
# copy edge points
cv.Copy(self.source_color, self.col_edge, self.edge)
# show the im
cv.ShowImage(win_name, self.col_edge)
self.process_image(position)
def on_contour(position):
# compute the real level of display, given the current position
levels = position-3
# initialisation
_contours = contours
if levels <= 0:
# zero or negative value
# => get to the nearest face to make it look more funny
_contours = contours.h_next().h_next().h_next()
# first, clear the image where we will draw contours
cv.SetZero (contours_image)
# draw contours in red and green
cv.DrawContours (contours_image, _contours,_white, _green,levels, 1, cv.CV_AA,(0, 0))
# finally, show the image
cv.ShowImage ("contours", contours_image)
# three windows that will open upon execution
cv.NamedWindow("Real",0)
# blank lists to store coordinates of blue blob
blue = []
while(1):
# captures feed from video in color
color_image = cv.QueryFrame(capture)
# ??
imdraw = cv.CreateImage(cv.GetSize(frame), 8, 3)
# ??
cv.SetZero(imdraw)
cv.Flip(color_image,color_image, 1)
cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 3, 0)
# ??
imgbluethresh = getthresholdedimg(color_image)
cv.Erode(imgbluethresh, imgbluethresh, None, 3)
cv.Dilate(imgbluethresh, imgbluethresh, None, 10)
# ??
img2 = cv.CloneImage(imgbluethresh)
# ??
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(imgbluethresh, storage, cv.CV_RETR_CCOMP, cv.CV_CHAIN_APPROX_SIMPLE)
# blank list into which points for bounding rectangles around blobs are appended
points = []
def procImg(img, sideName, dispFlag):
#creates empty images of the same size
imdraw = cv.CreateImage(cv.GetSize(img), 8, 3)
#put the smoothed image here
imgSmooth = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.SetZero(imdraw)
cv.Smooth(img, imgSmooth, cv.CV_GAUSSIAN, 3, 0) #Gaussian filter the image
imgbluethresh = getthresholdedimg(
imgSmooth) #Get a color thresholed binary image
cv.Erode(imgbluethresh, imgbluethresh, None, 3)
cv.Dilate(imgbluethresh, imgbluethresh, None, 10)
#img2 = cv.CloneImage(imgbluethresh)
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(imgbluethresh, storage, cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_SIMPLE)
centroidx = 0
centroidy = 0
prevArea = 0
pt1 = (0, 0)
pt2 = (0, 0)
while contour:
#find the area of each collection of contiguous points (contour)
bound_rect = cv.BoundingRect(list(contour))
contour = contour.h_next()
#get the largest contour
area = bound_rect[2] * bound_rect[3]
if dispFlag:
print("Area= " + str(area))
if (area > prevArea and area > 3000):
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2],
bound_rect[1] + bound_rect[3])
# Draw bounding rectangle
cv.Rectangle(img, pt1, pt2, cv.CV_RGB(255, 0, 0), 3)
# calculating centroid
centroidx = cv.Round((pt1[0] + pt2[0]) / 2)
centroidy = cv.Round((pt1[1] + pt2[1]) / 2)
if (centroidx == 0 or centroidy == 0):
print("no blimp detected from " + sideName)
else:
print(sideName + " centroid x:" + str(centroidx))
print(sideName + " centroid y:" + str(centroidy))
print("")
if dispFlag:
small_thresh = cv.CreateImage(
(int(0.25 * cv.GetSize(imgbluethresh)[0]),
int(0.25 * cv.GetSize(imgbluethresh)[1])), 8, 1)
cv.Resize(imgbluethresh, small_thresh)
cv.ShowImage(sideName + "_threshold", small_thresh)
cv.WaitKey(100)
small_hsv = cv.CreateImage((int(
0.25 * cv.GetSize(imghsv)[0]), int(0.25 * cv.GetSize(imghsv)[1])),
8, 3)
cv.Resize(imghsv, small_hsv)
cv.ShowImage(sideName + "_hsv", small_hsv)
cv.WaitKey(100)
return (centroidx, centroidy)
import cv2
from cv2 import cv
from database import *
src_image = cv.LoadImageM(getpath(), cv.CV_LOAD_IMAGE_COLOR)
dst_image = cv.CreateImage((src_image.width, src_image.height), 8,
src_image.channels)
cv.SetZero(dst_image)
#src_image=cv.LoadImageM("C:\\Users\\raj\\Desktop\\image processing and computer vision\\pictures\\s4.jpg")
def display(img, name):
cv.NamedWindow(name, 1)
cv.ShowImage(name, img)
def url_jpg_contours():
url = 'http://i12.tietuku.com/05ef0b29030fa46c.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
print imagefiledata #<cvmat(type=42424000 8UC1 rows=1 cols=48230 step=48230 )>
cv.SetData(imagefiledata, filedata, len(filedata))
im = cv.DecodeImage(imagefiledata, cv.CV_LOAD_IMAGE_COLOR)
col_edge = cv.CreateImage((im.width, im.height), 8, 3)
# convert to grayscale
gray_im = cv.CreateImage((im.width, im.height), 8, 1)
edge_im = cv.CreateImage((im.width, im.height), 8, 1)
cv.CvtColor(im, gray_im, cv.CV_BGR2GRAY)
cv.Canny(gray_im, edge_im, position, position * 3, 3)
cv.SetZero(col_edge)
# copy edge points
cv.Copy(im, col_edge, edge_im)
#ret, edge_jpg = cv2.imencode('.jpg', edge_im, [int(cv.CV_IMWRITE_JPEG_QUALITY), 80])
edge_im_array = np.asarray(edge_im[:])
print type(edge_im_array)
#edge_jpg_gray = cv2.cvtColor(edge_im_array,cv2.COLOR_BGR2GRAY)
ret, edge_im_array = cv2.threshold(edge_im_array, 127, 255,
cv2.THRESH_BINARY)
print type(edge_im_array)
contours, hierarchy = cv2.findContours(
edge_im_array, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
) #压缩水平方向，垂直方向，对角线方向的元素，只保留该方向的终点坐标，例如一个矩形轮廓只需4个点来保存轮廓信息
contours_img = cv2.cvtColor(edge_im_array, cv2.COLOR_GRAY2BGR)
url_str_len_contours = str(len(contours)) #取轮廊数量
str_len_contours = str(len(contours)) #取轮廊数量
#数据处理
first_contours = contours[0] #第一条轨迹坐标集合，数据格式为numpy.ndarry
first_contours_list = first_contours.tolist()
#print contours #输出所有轨迹坐标集合
#print contours[-1] #输出最后一条轨迹坐标，数据格式为numpy.ndarry
#print contours[0][0].tolist()[0] #输出第一条轨迹起始点坐标[[375 241]]并转化成list格式[[375，241]] |**.tolist()[0] 可以省掉一个中括号输出[375，241]
#print contours[0][0].tolist()[0][0] #输出第一条轨迹起始点坐标的X坐标值。
#print contours[0][0].tolist()[0][1] #输出第一条轨迹起始点坐标的Y坐标值。
#print [i[0][0] for i in contours]
#print [i[0][0] for i in contours[0]]
scale = 1 #不缩放
contours_img = cv2.resize(contours_img, (0, 0), fx=scale, fy=scale)
print "Url_jpg_contours_num:%s" % url_str_len_contours
for cnt in contours:
color = np.random.randint(0, 255, (3)).tolist()
cv2.drawContours(contours_img, [cnt * scale], 0, color, 1)
cv2.imshow("URL_canny_img", edge_im_array)
cv2.imshow("URL_contours_img", contours_img)
#轮廊清单转文本输出
edge_im_array_pix = str(np.size(edge_im_array))
contours_img_pix = str(np.size(contours_img))
ss = open("Contours" + ".log", 'w')
ss.write("edge_im_array_pix nums:" + "%s" % edge_im_array_pix + "\n")
ss.write("contours_img_pix nums:" + "%s" % contours_img_pix + "\n")
ss.write("_url_contours num:" + "%s" % str_len_contours + "\n")
for ele in contours:
ss.write("%s" % ele)
ss.write("**" * 50 + "\n")
ss.close()
#return contours
cv2.waitKey(0)
def process_image(self, slider_pos):
"""
This function finds contours, draws them and their approximation by ellipses.
"""
use_this = self.source_image
if self.intensity == False:
cv.Smooth(self.source_image, self.edge, cv.CV_BLUR, 9, 9, 0)
cv.Not(self.source_image, self.edge)
# run the edge dector on gray scale
cv.Canny(self.source_image, self.edge, slider_pos, slider_pos * 3,
3)
# reset
cv.SetZero(self.col_edge)
# copy edge points
cv.Copy(self.source_color, self.col_edge, self.edge)
use_this = self.edge
stor = cv.CreateMemStorage()
# Create the destination images
image02 = cv.CloneImage(use_this)
cv.Zero(image02)
image04 = cv.CreateImage(cv.GetSize(self.source_image),
cv.IPL_DEPTH_8U, 3)
cv.Zero(image04)
# Threshold the source image. This needful for cv.FindContours().
cv.Threshold(use_this, image02, slider_pos, 255, cv.CV_THRESH_BINARY)
# Find all contours.
cont = cv.FindContours(image02, stor, cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE, (0, 0))
for c in contour_iterator(cont):
# Number of points must be more than or equal to 6 for cv.FitEllipse2
if len(c) >= 6:
# Copy the contour into an array of (x,y)s
PointArray2D32f = cv.CreateMat(1, len(c), cv.CV_32FC2)
for (i, (x, y)) in enumerate(c):
PointArray2D32f[0, i] = (x, y)
# Draw the current contour in gray
gray = cv.CV_RGB(100, 100, 100)
cv.DrawContours(image04, c, gray, gray, 0, 1, 8, (0, 0))
# Fits ellipse to current contour.
(center, size, angle) = cv.FitEllipse2(PointArray2D32f)
# Convert ellipse data from float to integer representation.
center = (cv.Round(center[0]), cv.Round(center[1]))
size = (cv.Round(size[0] * 0.5), cv.Round(size[1] * 0.5))
# Draw ellipse in random color
color = cv.CV_RGB(random.randrange(256), random.randrange(256),
random.randrange(256))
cv.Ellipse(image04, center, size, angle, 0, 360, color, 2,
cv.CV_AA, 0)
# Show image. HighGUI use.
cv.ShowImage("Result", image04)
#f = open(os.path.splitext(files)[0]+".svg",'w')
#f.write( startSvgTag + base64String + endSvgTag)
#print 'Converted '+ files + ' to ' + os.path.splitext(files)[0]+".svg"
#log code canny to file for progress
with open('C:\\3d-Model\\bin\\segmentation_files\\progress.txt',
'w') as myFile:
myFile.write("canny")
#CONTOUR MAKING CODE
# create the image where we want to display results
image = cv.CreateImage((_SIZE, _SIZE), 8, 1)
# start with an empty image
cv.SetZero(image)
im = cv.LoadImage("C:\\3d-Model\\bin\\segmentation_files\\pic_seg.jpg",
cv.CV_LOAD_IMAGE_COLOR)
image = cv.CreateImage((im.width, im.height), 8, 1)
cv.CvtColor(im, image, cv.CV_BGR2GRAY)
threshold = 51
colour = 255
cv.Threshold(image, image, threshold, colour, cv.CV_THRESH_BINARY)
# create the window for the contours
cv.NamedWindow("contours", cv.CV_WINDOW_NORMAL)
# create the trackbar, to enable the change of the displayed level
cv.CreateTrackbar("levels+3", "contours", 3, 7, on_contour)
cv.CvtColor(image, img_grayscale, cv.CV_RGB2GRAY)
cv.ShowImage("converted-image", img_grayscale)
img_contour = cv.CreateImage(img_size, 8, 3)
# find the contours
contours = cv.FindContours(img_grayscale, storage, cv.CV_RETR_TREE,
cv.CV_CHAIN_APPROX_SIMPLE, (0, 0))
for i in contours:
print i
contours = cv.ApproxPoly(contours, storage, cv.CV_POLY_APPROX_DP, 8, 1)
levels = 2
# first, clear the image where we will draw contours
cv.SetZero(img_contour)
# initialisation
_contours = contours
# draw contours in red and green
cv.DrawContours(
img_contour, #dest image
_contours, #input contours
_red, #color of external contour
_green, #color of internal contour
levels, #maxlevel of contours to draw
_contour_thickness,
cv.CV_AA, #line type
(0, 0)) #offset
x0 = [0.]
#print silly(x0, goodcorners)
print "initial error", silly(x0, goodcorners)
xopt = fmin(silly, x0, args=(goodcorners, ))
print "xopt", xopt
print "final error", silly(xopt, goodcorners)
d = 1.0 # - sum(xopt)
poly = numpy.poly1d(list(xopt) + [d, 0.])
print "final polynomial"
print poly
for co in goodcorners:
scrib = cv.CreateMat(480, 640, cv.CV_8UC3)
cv.SetZero(scrib)
cv.DrawChessboardCorners(scrib, (num_x_ints, num_y_ints),
[xf(pt, poly) for pt in co], True)
cv.ShowImage("snap", scrib)
cv.WaitKey()
sys.exit(0)
for (i, (img, (ok, co))) in enumerate(zip(images, corners)):
scrib = cv.CreateMat(img.rows, img.cols, cv.CV_8UC3)
cv.CvtColor(img, scrib, cv.CV_GRAY2BGR)
if ok:
cv.DrawChessboardCorners(scrib, (num_x_ints, num_y_ints), co, True)
cv.ShowImage("snap", scrib)
cv.WaitKey()
def stereoCalibrate(self):
print " rows " + str(rows)
print "columns " + str(columns)
print "points " + str(num_pts)
# nimages = 8
# num_pts = 5
# (CV_MAT_DEPTH(_imagePoints1->type) == CV_32F || CV_MAT_DEPTH(_imagePoints1->type) == CV_64F)
# && ((_imagePoints1->rows == pointsTotal && _imagePoints1->cols*cn == 2) ||
# (_imagePoints1->rows == 1 && _imagePoints1->cols == pointsTotal && cn == 2))
# CV_32FC1 == CV_32F, CV_32FC2 == CV_32FC(2) == CV_MAKETYPE(CV_32F, 2), and CV_MAKETYPE(depth, n) == ((x&7)<<3) + (n-1).
# This means that the constant type is formed from the depth, taking the lowest 3 bits, and the number of channels minus 1,
# taking the next log2(CV_CN_MAX) bits.
opts = cv.CreateMat(nimages * num_pts, 3, cv.CV_32FC1)
ipts1 = cv.CreateMat(nimages * num_pts, 2, cv.CV_32F)
ipts2 = cv.CreateMat(nimages * num_pts, 2, cv.CV_32F)
# ipts1 = cv.CreateMat(nimages * num_pts, 2, cv.CV_32F)
# ipts2 = cv.CreateMat(nimages * num_pts, 2, cv.CV_32F)
npts = cv.CreateMat(nimages, 1, cv.CV_32SC1)
for i in range(0, nimages):
npts[i, 0] = num_pts
# Create first Intrinsic Camera Matrix and Distortion Matrix
intrinsics1 = cv.CreateMat(3, 3, cv.CV_64FC1)
distortion1 = cv.CreateMat(4, 1, cv.CV_64FC1)
cv.SetZero(intrinsics1)
cv.SetZero(distortion1)
intrinsics1[0, 0] = 1.0
intrinsics1[1, 1] = 1.0
# Create second Intrinsic Camera Matrix and Distortion Matrix
intrinsics2 = cv.CreateMat(3, 3, cv.CV_64FC1)
distortion2 = cv.CreateMat(4, 1, cv.CV_64FC1)
cv.SetZero(intrinsics2)
cv.SetZero(distortion2)
# focal lengths have 1/1 ratio
intrinsics2[0, 0] = 1.0
intrinsics2[1, 1] = 1.0
# CV_64F CV_32FC1
R = cv.CreateMat(3, 3, cv.CV_64F)
T = cv.CreateMat(3, 1, cv.CV_64F)
E = cv.CreateMat(3, 3, cv.CV_64F)
F = cv.CreateMat(3, 3, cv.CV_64F)
# print type(points)
# imagePoints1 = np.asarray(points, np.uint8 , 3)
# imagePoints2 = np.asarray(points2, np.uint8, 3)
# print "HERE " + str(type(imagePoints2))
# imagePoints11 = imagePoints1.T
# imagePoints22 = imagePoints2.T
# imagePoints111 = cv.fromarray(imagePoints11)
# imagePoints222 = cv.fromarray(imagePoints22)
print np.shape(opts)
# print np.shape(points)
print np.shape(self.pointsArray1)
print type(self.pointsArray2)
for k in range(0, nimages):
for i in range(0, columns):
for j in range(0, rows):
print (j * columns + i)
opts[k * num_pts + j * columns + i, 0] = j * 10
opts[k * num_pts + j * columns + i, 1] = i * 10
opts[k * num_pts + j * columns + i, 2] = 0
ipts1[k * num_pts + j * columns + i, 0] = self.pointsArray1[k, j * columns + i][0]
ipts1[k * num_pts + j * columns + i, 1] = self.pointsArray1[k, j * columns + i][1]
ipts2[k * num_pts + j * columns + i, 0] = self.pointsArray2[k, j * columns + i][0]
ipts2[k * num_pts + j * columns + i, 1] = self.pointsArray2[k, j * columns + i][1]
# print np.shape(imagePoints111)
# cv.StereoCalibrate(objectPoints, imagePoints1, imagePoints2, pointCounts, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T, E=None, F=None, term_crit=(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 30, 1e-6), flags=CV_CALIB_FIX_INTRINSIC)
cv.StereoCalibrate(opts, ipts1, ipts2 , npts, intrinsics1, distortion1, intrinsics2, distortion2, self.size, R, T, E, F, (cv.CV_TERMCRIT_ITER + cv.CV_TERMCRIT_EPS, 30, 1e-6), cv.CV_CALIB_FIX_INTRINSIC)
size = self.size
R1 = cv.CreateMat(3, 3, cv.CV_64F)
R2 = cv.CreateMat(3, 3, cv.CV_64F)
P1 = cv.CreateMat(3, 4, cv.CV_64F)
P2 = cv.CreateMat(3, 4, cv.CV_64F)
self.Q = cv.CreateMat(4, 4, cv.CV_64F)
(roi1, roi2) = cv.StereoRectify(intrinsics1, intrinsics2, distortion1, distortion2, size, R , T, R1, R2, P1, P2, self.Q)
[mat_w, mat_h] = self.size
self.map1x = cv.CreateMat(mat_h, mat_w, cv.CV_32FC1)
self.map2x = cv.CreateMat(mat_h, mat_w, cv.CV_32FC1)
# Right maps
self.map1y = cv.CreateMat(mat_h, mat_w, cv.CV_32FC1)
self.map2y = cv.CreateMat(mat_h, mat_w, cv.CV_32FC1)
# cv.InitUndistortMap(intrinsics, distortion, mapx, mapy)
cv.InitUndistortRectifyMap(intrinsics1, distortion1, R1, P1, self.map1x, self.map1y)
cv.InitUndistortRectifyMap(intrinsics2, distortion2, R2, P2, self.map2x, self.map2y)
def tangent(self, dx, dy, Mask=None, method="cv"):
'''This function calculates the gradient orientation of each pixel
that is in Mask'''
tangent = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_8U, 1)
divSize = cv.GetSize(dx)
tangent16U = cv.CreateImage(divSize, cv.IPL_DEPTH_32F, 1)
cv.SetZero(tangent16U)
if method == "slow":
for x in range(divSize[0]):
for y in range(divSize[1]):
if Mask == None:
tang = math.atan2(dy[y, x], dx[y, x]) * self.constant
tangent16U[y, x] = int(tang)
elif Mask[y, x] > 0:
tang = math.atan2(dy[y, x], dx[y, x]) * self.constant
tangent16U[y, x] = int(tang)
elif Mask[y, x] == 0:
tangent16U[y, x] = 0
elif method == "cv":
#Calculated the arctan2 which give -pi to pi range
#I create numpy arrays then reshape them in to 1 Dimesnion.
#Next, I calculated arctan2 and change the range to 0-2pi and make it in degrees
#I reshape back to picture format and return the picture
#Numpy formatting
(width, height) = cv.GetSize(dx)
matdx = cv.CreateMat(height, width, cv.CV_16SC1)
matdy = cv.CreateMat(height, width, cv.CV_16SC1)
cv.SetZero(matdx)
cv.SetZero(matdy)
cv.Copy(dx, matdx, Mask)
cv.Copy(dy, matdy, Mask)
a = numpy.asarray(matdx)
b = numpy.asarray(matdy)
#Reshaping to one dimension
ar = numpy.reshape(a, a.size)
br = numpy.reshape(b, b.size)
#Calculating Arc Tangent with quadrant information
c = numpy.arctan2(br, ar)
#Turning it to -180 to 180 range
z = numpy.multiply(c, self.constant)
result = z.astype(numpy.int32)
result[result < 0] += 360
tang = numpy.reshape(result, (height, width))
tang = tang.astype(numpy.float32)
mat = cv.fromarray(tang)
cv.Copy(mat, tangent16U)
else:
dxTemp = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_16S, 1)
dyTemp = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_16S, 1)
zero = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_16S, 1)
cv.Add(zero, dx, dxTemp, Mask)
cv.Add(zero, dy, dyTemp, Mask)
dx = dxTemp
dy = dyTemp
for x in range(divSize[0]):
for y in range(divSize[1]):
if Mask[y, x] == 0:
tangent16U[y, x] = 0
continue
tang = math.atan2(dy[y, x], dx[y, x]) * self.constant
tangent16U[y, x] = int(tang)
if self.visualize:
#tangent2 = cv.CreateImage(cv.GetSize(dy), cv.CV_16SC1, 1)
cv.ConvertScaleAbs(tangent16U, tangent)
cv.EqualizeHist(tangent, tangent)
while True:
cv.NamedWindow("Tangent")
cv.ShowImage("Tangent", tangent)
c = cv.WaitKey(5)
if c > 0:
break
cv.DestroyAllWindows()
return tangent16U
def create_kde_with_trips(self, all_trips, cell_size, gaussian_blur):
# print ("trips path: ") + str(trips_path)
print("cell size: ") + str(cell_size)
print("gaussian blur: ") + str(gaussian_blur)
conf = ConfigurationManager()
prefix = conf.getProperty(Constants.output, Constants.prefix)
sys.stdout.write("\nFinding bounding box... ")
sys.stdout.flush()
min_lat = all_trips[0].locations[0].latitude
max_lat = all_trips[0].locations[0].latitude
min_lon = all_trips[0].locations[0].longitude
max_lon = all_trips[0].locations[0].longitude
# 寻找地图的边界，会存在temp/bounding_boxes/ 目录下
for trip in all_trips:
for location in trip.locations:
if (location.latitude < min_lat):
min_lat = location.latitude
if (location.latitude > max_lat):
max_lat = location.latitude
if (location.longitude < min_lon):
min_lon = location.longitude
if (location.longitude > max_lon):
max_lon = location.longitude
print("done.")
# find bounding box for data
min_lat -= 0.0003
max_lat += 0.0003
min_lon -= 0.0005
max_lon += 0.0005
diff_lat = max_lat - min_lat
diff_lon = max_lon - min_lon
trip_file = open(prefix + "bounding_boxes/bounding_box_1m.txt", 'w')
bound_str = str(min_lat) + " " + str(min_lon) + " " + str(
max_lat) + " " + str(max_lon)
trip_file.write(bound_str)
trip_file.close()
width = int(diff_lon * spatialfunclib.METERS_PER_DEGREE_LONGITUDE /
cell_size)
height = int(diff_lat * spatialfunclib.METERS_PER_DEGREE_LATITUDE /
cell_size)
yscale = height / diff_lat # pixels per lat
xscale = width / diff_lon # pixels per lon
# aggregate intensity map for all traces
# themap = cv.CreateMat(height,width,cv.CV_8U)
themap = cv.CreateMat(height, width, cv.CV_16UC1)
cv.SetZero(themap)
## Build an aggregate intensity map from all the edges
trip_counter = 1
for trip in all_trips:
if ((trip_counter % 10 == 0) or (trip_counter == len(all_trips))):
sys.stdout.write("\rCreating histogram (trip " +
str(trip_counter) + "/" +
str(len(all_trips)) + ")... ")
sys.stdout.flush()
trip_counter += 1
temp = cv.CreateMat(height, width, cv.CV_8UC1)
cv.SetZero(temp)
temp16 = cv.CreateMat(height, width, cv.CV_16UC1)
cv.SetZero(temp16)
for (orig, dest) in pairwise(trip.locations):
oy = height - int(yscale * (orig.latitude - min_lat))
ox = int(xscale * (orig.longitude - min_lon))
dy = height - int(yscale * (dest.latitude - min_lat))
dx = int(xscale * (dest.longitude - min_lon))
cv.Line(temp, (ox, oy), (dx, dy), (32), 1, cv.CV_AA)
# 图片 线段的第一个点 第二个点 线条颜色 线粗细 线类型 shift（点坐标中的小数位数）
# 参数解释：8（8连通线） 4（4连通线） CV_AA（抗锯齿线）
# accumulate trips into themap
cv.ConvertScale(temp, temp16, 1, 0)
# 源数组 目标数组 比例因子 将值添加到缩放后的源数组元素
# 使用可选的线性变换将一个数组转换为另外一个数组
# 用途:将一个数组复制到另外一个数组
cv.Add(themap, temp16, themap)
lines = cv.CreateMat(height, width, cv.CV_8U)
cv.SetZero(lines)
print("done.")
trip_counter = 1
for trip in all_trips:
if ((trip_counter % 10 == 0) or (trip_counter == len(all_trips))):
sys.stdout.write("\rCreating drawing (trip " +
str(trip_counter) + "/" +
str(len(all_trips)) + ")... ")
sys.stdout.flush()
trip_counter += 1
for (orig, dest) in pairwise(trip.locations):
oy = height - int(yscale * (orig.latitude - min_lat))
ox = int(xscale * (orig.longitude - min_lon))
dy = height - int(yscale * (dest.latitude - min_lat))
dx = int(xscale * (dest.longitude - min_lon))
cv.Line(lines, (ox, oy), (dx, dy), (255), 1, cv.CV_AA)
# save the lines
cv.SaveImage(prefix + "raw_data.png", lines)
print("done.")
# print "Intensity map acquired."
sys.stdout.write("Smoothing... ")
sys.stdout.flush()
# # create the mask and compute the contour
cv.Smooth(themap, themap, cv.CV_GAUSSIAN, gaussian_blur, gaussian_blur)
cv.SaveImage(prefix + "kde.png", themap)
print("done.")
print("\nKDE generation complete.")
def _wipeMix(self, wipeMode, wipeConfig, level, image1, image2, mixMat):
if((wipeMode == WipeMode.Push)):
wipeDirection = wipeConfig
if(wipeDirection < 0.25):
wipePosX = int(self._internalResolutionX * level)
sourceLeft = self._internalResolutionX-wipePosX
sourceTop = 0
sourceWidth = wipePosX
sourceHeight = self._internalResolutionY
destLeft = 0
destTop = 0
elif(wipeDirection < 0.5):
wipePosX = self._internalResolutionX - int(self._internalResolutionX * level)
sourceLeft = 0
sourceTop = 0
sourceWidth = self._internalResolutionX-wipePosX
sourceHeight = self._internalResolutionY
destLeft = self._internalResolutionX-(self._internalResolutionX-wipePosX)
destTop = 0
elif(wipeDirection < 0.75):
wipePosY = int(self._internalResolutionY * level)
sourceLeft = 0
sourceTop = self._internalResolutionY-wipePosY
sourceWidth = self._internalResolutionX
sourceHeight = wipePosY
destLeft = 0
destTop = 0
else:
wipePosY = self._internalResolutionY - int(self._internalResolutionY * level)
sourceLeft = 0
sourceTop = 0
sourceWidth = self._internalResolutionX
sourceHeight = self._internalResolutionY-wipePosY
destLeft = 0
destTop = self._internalResolutionY-(self._internalResolutionY-wipePosY)
destWidth = sourceWidth
destHeight = sourceHeight
src_region = cv.GetSubRect(image2, (sourceLeft, sourceTop, sourceWidth, sourceHeight))
if(image1 == None):
cv.SetZero(mixMat)
dst_region = cv.GetSubRect(mixMat, (destLeft, destTop, destWidth, destHeight))
return mixMat
else:
dst_region = cv.GetSubRect(mixMat, (destLeft, destTop, destWidth, destHeight))
cv.Copy(src_region, dst_region)
if(wipeDirection < 0.25):
wipePosX = int(self._internalResolutionX * level)
sourceLeft = wipePosX
sourceTop = 0
sourceWidth = self._internalResolutionX-wipePosX
sourceHeight = self._internalResolutionY
destLeft = wipePosX
destTop = 0
elif(wipeDirection < 0.5):
wipePosX = self._internalResolutionX - int(self._internalResolutionX * level)
sourceLeft = 0
sourceTop = 0
sourceWidth = wipePosX
sourceHeight = self._internalResolutionY
destLeft = 0
destTop = 0
elif(wipeDirection < 0.75):
wipePosY = int(self._internalResolutionY * level)
sourceLeft = 0
sourceTop = wipePosY
sourceWidth = self._internalResolutionX
sourceHeight = self._internalResolutionY-wipePosY
destLeft = 0
destTop = wipePosY
else:
wipePosY = self._internalResolutionY - int(self._internalResolutionY * level)
sourceLeft = 0
sourceTop = 0
sourceWidth = self._internalResolutionX
sourceHeight = wipePosY
destLeft = 0
destTop = 0
destWidth = sourceWidth
destHeight = sourceHeight
src_region = cv.GetSubRect(image1, (sourceLeft, sourceTop, sourceWidth, sourceHeight))
dst_region = cv.GetSubRect(mixMat, (destLeft, destTop, destWidth, destHeight))
cv.Copy(src_region, dst_region)
return mixMat
if(wipeMode == WipeMode.Noize):
scaleArg = wipeConfig
noizeMask = getNoizeMask(level, self._internalResolutionX, self._internalResolutionY, 1.0 + (19.0 * scaleArg))
if(image1 == None):
cv.SetZero(mixMat)
cv.Copy(image2, mixMat, noizeMask)
return mixMat
cv.Copy(image2, image1, noizeMask)
return image1
if(wipeMode == WipeMode.Zoom):
xMove, yMove = wipeConfig
xSize = int(self._internalResolutionX * level)
ySize = int(self._internalResolutionY * level)
xPos = int((self._internalResolutionX - xSize) * xMove)
yPos = int((self._internalResolutionY - ySize) * (1.0 - yMove))
cv.SetZero(mixMat)
dst_region = cv.GetSubRect(mixMat, (xPos, yPos, xSize, ySize))
cv.Resize(image2, dst_region,cv.CV_INTER_CUBIC)
if(image1 == None):
return mixMat
cv.SetZero(self._mixMixMask1)
dst_region = cv.GetSubRect(self._mixMixMask1, (xPos, yPos, xSize, ySize))
cv.Set(dst_region, 256)
cv.Copy(mixMat, image1, self._mixMixMask1)
return image1
if(wipeMode == WipeMode.Flip):
flipRotation = wipeConfig
rotation = 1.0 - level
srcPoints = ((0.0, 0.0),(0.0,self._internalResolutionY),(self._internalResolutionX, 0.0))
destPoint1 = (0.0, 0.0)
destPoint2 = (0.0, self._internalResolutionY)
destPoint3 = (self._internalResolutionX, 0.0)
if(image1 == None):
rotation = rotation / 2
if(rotation < 0.5):
flipAngle = rotation / 2
else:
flipAngle = level / 2
destPoint1 = rotatePoint(flipRotation, destPoint1[0], destPoint1[1], self._halfResolutionX, self._halfResolutionY, flipAngle)
destPoint2 = rotatePoint(flipRotation, destPoint2[0], destPoint2[1], self._halfResolutionX, self._halfResolutionY, flipAngle)
destPoint3 = rotatePoint(flipRotation, destPoint3[0], destPoint3[1], self._halfResolutionX, self._halfResolutionY, flipAngle)
dstPoints = ((destPoint1[0], destPoint1[1]),(destPoint2[0], destPoint2[1]),(destPoint3[0],destPoint3[1]))
zoomMatrix = cv.CreateMat(2,3,cv.CV_32F)
# print "DEBUG pcn: trasform points source: " + str(srcPoints) + " dest: " + str(dstPoints)
cv.GetAffineTransform(srcPoints, dstPoints, zoomMatrix)
if(rotation < 0.5):
cv.WarpAffine(image2, mixMat, zoomMatrix)
else:
cv.WarpAffine(image1, mixMat, zoomMatrix)
cv.Set(self._mixMixMask2, (255,255,255))
cv.WarpAffine(self._mixMixMask2, self._mixMixMask1, zoomMatrix)
return mixMat
return image2
def _find_corr(matches,
hom=False,
data={},
MAX_PIXEL_DEVIATION=MAX_PIXEL_DEVIATION,
FALLBACK_PIXEL_DEVIATIONS=FALLBACK_PIXEL_DEVIATIONS,
rotation_filter_only=False,
ROT_THRESHOLD_RADIANS=ROT_THRESHOLD_RADIANS):
data['success'] = False # by default
matches = list(matches)
F = cv.CreateMat(3, 3, cv.CV_64F)
cv.SetZero(F)
if not matches or (hom and len(matches) < 4):
return F, []
inliers = cv.CreateMat(1, len(matches), cv.CV_8U)
cv.SetZero(inliers)
pts_q = cv.CreateMat(len(matches), 1, cv.CV_64FC2)
pts_db = cv.CreateMat(len(matches), 1, cv.CV_64FC2)
for i, m in enumerate(matches):
cv.Set2D(pts_q, i, 0, cv.Scalar(*m['query'][:2]))
cv.Set2D(pts_db, i, 0, cv.Scalar(*m['db'][:2]))
# ransac for fundamental matrix. rotation filtering
# TODO multiple RANSAC to get smaller/larger features
if not hom:
if rotation_filter_only:
inliers = [1] * len(matches)
else:
cv.FindFundamentalMat(pts_q,
pts_db,
F,
status=inliers,
param1=MAX_PIXEL_DEVIATION,
param2=CONFIDENCE_LEVEL)
inliers = np.asarray(inliers)[0]
# assumes roll(db) == roll(query)
for i, m in enumerate(matches):
if inliers[i]:
if abs(rot_delta(m, 0)) > ROT_THRESHOLD_RADIANS:
inliers[i] = False
return F, inliers
# homography only. no rotation check
cv.FindHomography(pts_db,
pts_q,
F,
method=cv.CV_RANSAC,
ransacReprojThreshold=MAX_PIXEL_DEVIATION,
status=inliers)
### try rounds of homography calculations ###
i = 0
while i < len(FALLBACK_PIXEL_DEVIATIONS):
if not isHomographyGood(F):
cv.FindHomography(
pts_db,
pts_q,
F,
method=cv.CV_RANSAC,
ransacReprojThreshold=FALLBACK_PIXEL_DEVIATIONS[i],
status=inliers)
i += 1
else:
break
if i >= len(FALLBACK_PIXEL_DEVIATIONS):
cv.FindHomography(pts_db, pts_q, F, method=cv.CV_LMEDS, status=inliers)
if isHomographyGood(F):
data['success'] = True
else:
data['success'] = True
return F, np.asarray(inliers)[0]