def backgroundDiff(self, img, Imask):
#cv.Zero(self.Imaskt)
cv.Zero(Imask)
cv.CvtScale(img, self.Iscratch, 1, 0)
cv.InRange(self.Iscratch, self.IlowF, self.IhiF, Imask)
cv.SubRS(Imask, 255, Imask)
cv.MorphologyEx(Imask, Imask, None, None, cv.CV_MOP_OPEN, 1)
cv.MorphologyEx(Imask, Imask, None, None, cv.CV_MOP_CLOSE, 2)
#######This stuff was for when it was 3 channel model, now only 1
#######for backprojection
#cv.Split(self.Iscratch, self.Igray1, self.Igray2, self.Igray3, None)
#cv.InRange(self.Igray1, self.Ilow1, self.Ihi1, Imask)
# cv.InRange(self.Igray2, self.Ilow2, self.Ihi2, self.Imaskt)
# cv.Or(Imask, self.Imaskt, Imask)
# cv.InRange(self.Igray3, self.Ilow3, self.Ihi3, self.Imaskt)
# cv.Or(Imask, self.Imaskt, Imask)
# cv.SubRS(Imask, 255, Imask)
#cv.SaveImage('/home/mkillpack/Desktop/mask.png', Imask)
#cv.Erode(Imask, Imask)
return Imask
def max_contrast(image):
"""Maximise the contrast of an image using top and bottom hat filters."""
size = cv.GetSize(image)
bh = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
th = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
s1 = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
s2 = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
el = cv.CreateStructuringElementEx(3, 3, 1, 1, cv.CV_SHAPE_ELLIPSE)
cv.MorphologyEx(image, th, None, el, cv.CV_MOP_TOPHAT, 1)
cv.MorphologyEx(image, bh, None, el, cv.CV_MOP_BLACKHAT, 1)
cv.Add(image, th, s1)
cv.Sub(s1, bh, s2)
return s2
def morphology(self, image):
""" remove noisy pixels by doing erode and dilate """
cv.MorphologyEx(self.th,
self.temp,
None,
self.morpher_small,
cv.CV_MOP_OPEN,
iterations=1)
cv.MorphologyEx(self.temp,
self.morphed,
None,
self.morpher,
cv.CV_MOP_CLOSE,
iterations=1)
cv.Dilate(self.morphed, self.temp, self.morpher)
cv.Copy(self.temp, self.morphed)
return self.morphed
def threshold_v2(self, frame):
cv.Smooth(frame, frame, cv.CV_BLUR, 5, 5)
cv.MorphologyEx(frame, frame, None, None, cv.CV_MOP_OPEN)
cv.MorphologyEx(frame, frame, None, None, cv.CV_MOP_CLOSE)
cv.Threshold(frame, frame, 10, 255, cv.CV_THRESH_BINARY_INV)
return frame
def find_loop(input_data, IterationClosing=6):
"""
This function detect support (or loop) and return the coordinates if there is a detection,
and -1 if not.
in : filename : string image Filename / Format accepted :
in : IterationClosing : int : Number of iteration for closing contour procedure
Out : tupple of coordiante : (string, coordinate X, coordinate Y) where string take value
'Coord' or 'No loop detected depending if loop was detected or not. If no loop was
detected coordinate X and coordinate y take the value -1.
"""
#Definition variable Global
global AIRE_MIN_REL
global AIRE_MIN
global NORM_IMG
global NiteClosing
global pointRef
#Chargement image
try:
if type(input_data) == str:
#Image filename is passed
img_ipl = cv.LoadImageM(input_data)
elif type(input_data) == np.ndarray:
img_ipl = cv.fromarray(input_data)
else:
print "ERROR : Input image could not be opened, check format or path"
return (
"ERROR : Input image could not be opened, check format or path",
-10, -10)
except:
print "ERROR : Input image could not be opened, check format or path"
return (
"ERROR : Input image could not be opened, check format or path",
-10, -10)
img_cont = img_ipl # img used for
NORM_IMG = img_ipl.width * img_ipl.height
AIRE_MIN = NORM_IMG * AIRE_MIN_REL
#traitement
#Converting input image in Grey scale image
img_gray_ini = cv.CreateImage((img_ipl.width, img_ipl.height), 8, 1)
cv.CvtColor(img_ipl, img_gray_ini, cv.CV_BGR2GRAY)
#Removing Offset from image
img_gray_resize = cv.CreateImage(
(img_ipl.width - 2 * Offset[0], img_ipl.height - 2 * Offset[1]), 8, 1)
cv.SetImageROI(img_gray_ini,
(Offset[0], Offset[1], img_ipl.width - 2 * Offset[0],
img_ipl.height - 2 * Offset[1]))
cv.Copy(img_gray_ini, img_gray_resize)
# #creat image used for treatment
img_gray = cv.CreateImage((img_gray_resize.width, img_gray_resize.height),
8, 1)
img_trait = cv.CreateImage((img_gray.width, img_gray.height), 8, 1)
# image used for treatment is the same than img_gray_resize
cv.Copy(img_gray_resize, img_gray)
#Img is smooth with asymetric kernel
cv.Smooth(img_gray, img_gray, param1=11, param2=9)
cv.Canny(img_gray, img_trait, 40, 60)
# Laplacian treatment
# Creating buffer image
img_lap_ini = cv.CreateImage((img_gray.width, img_gray.height), 32, 1)
img_lap = cv.CreateImage((img_lap_ini.width - 2 * Offset[0],
img_lap_ini.height - 2 * Offset[1]), 32, 1)
# Creating buffer img
img_lap_tmp = cv.CreateImage((img_lap.width, img_lap.height), 32, 1)
#Computing laplacian
cv.Laplace(img_gray, img_lap_ini, 5)
#Applying Offset to avoid border effect
cv.SetImageROI(img_lap_ini,
(Offset[0], Offset[1], img_lap_ini.width - 2 * Offset[0],
img_lap_ini.height - 2 * Offset[1]))
#Copying laplacian treated image to final laplacian image
cv.Copy(img_lap_ini, img_lap)
#Apply an asymetrique smoothing
cv.Smooth(img_lap, img_lap, param1=21, param2=11)
#Define the Kernel for closing algorythme
MKernel = cv.CreateStructuringElementEx(7, 3, 3, 1, cv.CV_SHAPE_RECT)
# Closing contour procedure
cv.MorphologyEx(img_lap, img_lap, img_lap_tmp, MKernel, cv.CV_MOP_CLOSE,
NiteClosing)
# Conveting img in 8bit image
img_lap8_ini = cv.CreateImage((img_lap.width, img_lap.height), 8, 1)
cv.Convert(img_lap, img_lap8_ini)
# Add white border to image
mat_bord = WhiteBorder(np.asarray(img_lap8_ini[:]), XSize, YSize)
img_lap8 = cv.CreateImage(
(img_lap.width + 2 * XSize, img_lap.height + 2 * YSize), 8, 1)
img_lap8 = cv.fromarray(mat_bord)
#Compute threshold
seuil_tmp = Seuil_var(img_lap8)
#If Seuil_tmp is not null
if seuil_tmp != 0:
seuil = seuil_tmp
#Else seuil is fixed to 20, which prevent from wrong positiv detection
else:
seuil = 20
#Compute thresholded image
img_lap_bi = cv.CreateImage((img_lap8.width, img_lap8.height), 8, 1)
img_lap_color = cv.CreateImage((img_lap8.width, img_lap8.height), 8, 3)
img_trait_lap = cv.CreateImage((img_lap8.width, img_lap8.height), 8, 1)
#Compute thresholded image
cv.Threshold(img_lap8, img_lap_bi, seuil, 255, cv.CV_THRESH_BINARY)
#Gaussian smoothing on laplacian
cv.Smooth(img_lap_bi, img_lap_bi, param1=11, param2=11)
#Convert grayscale laplacian image to binarie image using "seuil" as threshold value
cv.Threshold(img_lap_bi, img_lap_bi, 1, 255, cv.CV_THRESH_BINARY_INV)
cv.CvtColor(img_lap_bi, img_lap_color, cv.CV_GRAY2BGR)
#Compute edge in laplacian image
cv.Canny(img_lap_bi, img_trait_lap, 0, 2)
#Find contour
seqlapbi = cv.FindContours(img_trait_lap, cv.CreateMemStorage(),
cv.CV_RETR_TREE, cv.CV_CHAIN_APPROX_SIMPLE)
#contour is filtered
try:
contour_list = parcourt_contour(seqlapbi, img_lap_color)
except:
# If error is traped then there is no loop detected
return (0, 0, ("No loop detected", -1, -1))
# If there contours's list is not empty
NCont = len(contour_list)
if (NCont > 0):
# The CvSeq is inversed : X(i) became i(X)
indice = MapCont(contour_list[0], img_lap_color.width,
img_lap_color.height)
# The coordinate of target is computed in the traited image
point_shift = integreCont(indice, contour_list[0])
# The coordinate in original image are computed taken into account Offset and white bordure
point = (point_shift[0], point_shift[1] + 2 * Offset[0] - XSize,
point_shift[2] + 2 * Offset[1] - YSize)
else:
#Else no loop is detected
point = ("No loop detected", -1, -1)
Aire_Max = 0
return point
# Otsu threshold
image = loadGreyscale()
cv.Threshold(image, image, threshold, color, cv.CV_THRESH_OTSU)
showWindow("Otsu threshold")
# Dilation
image = loadGreyscale()
element_shape = cv.CV_SHAPE_RECT
pos = 1
element = cv.CreateStructuringElementEx(pos * 2 + 1, pos * 2 + 1, pos, pos,
element_shape)
cv.Dilate(image, image, element, 2)
showWindow("Dilate")
# Erosion
image = loadGreyscale()
cv.Erode(image, image, element, 2)
showWindow("Erode")
# Morphology
image = loadGreyscale()
cv.MorphologyEx(image, image, image, element, cv.CV_MOP_CLOSE, 2)
showWindow("Morphology")
# Laplace
image = loadGreyscale()
dst_16s2 = cv.CreateImage(cv.GetSize(image), cv.IPL_DEPTH_16S, 1)
cv.Laplace(image, dst_16s2)
cv.Convert(dst_16s2, image)
showWindow('Laplace')
#! /usr/bin/env python
from SimpleCV import Camera, Display, Image
import time
import matplotlib.pyplot as plt
import cv
import numpy as np
image = cv.LoadImage('/home/pi/Documents/Lab3/foto4.png',
cv.CV_LOAD_IMAGE_GRAYSCALE)
#Get edges
morphed = cv.CloneImage(image)
cv.MorphologyEx(image, morphed, None, None,
cv.CV_MOP_GRADIENT) # Apply a dilate - Erode
cv.Threshold(morphed, morphed, 30, 255, cv.CV_THRESH_BINARY_INV)
cv.ShowImage("Image", image)
cv.ShowImage("Morphed", morphed)
cv.WaitKey(0)
def back_project_hs(src, patch):
# Convert to HSV
hsv = cv.CreateImage(cv.GetSize(src), 8, 3)
cv.CvtColor(src, hsv, cv.CV_BGR2HSV)
hsv_patch= cv.CreateImage(cv.GetSize(patch), 8, 3)
cv.CvtColor(patch, hsv_patch, cv.CV_BGR2HSV)
# Extract the H and S planes
h_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
h_plane_img = cv.CreateImage(cv.GetSize(src), 8, 1)
h_plane_patch = cv.CreateMat(patch.rows, patch.cols, cv.CV_8UC1)
s_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
s_plane_img = cv.CreateImage(cv.GetSize(src), 8, 1)
s_plane_patch = cv.CreateMat(patch.rows, patch.cols, cv.CV_8UC1)
v_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
cv.Split(hsv, h_plane, s_plane, v_plane, None)
cv.Split(hsv, h_plane_img, s_plane_img, None, None)
cv.Split(hsv_patch, h_plane_patch, s_plane_patch, None, None)
#cv.Split(src, h_plane, s_plane, v_plane, None)
planes = [h_plane_patch, s_plane_patch]#, s_plane, v_plane]
# planes = [s_plane_patch]#, s_plane, v_plane]
h_bins = 30
s_bins = 32
hist_size = [h_bins, s_bins]
# hue varies from 0 (~0 deg red) to 180 (~360 deg red again */
h_ranges = [0, 180]
s_ranges = [0, 255]
# saturation varies from 0 (black-gray-white) to
# 255 (pure spectrum color)
ranges = [h_ranges, s_ranges]#, s_ranges, v_ranges]
#ranges = [s_ranges]#, s_ranges, v_ranges]
scale = 1
hist = cv.CreateHist([h_bins, s_bins], cv.CV_HIST_ARRAY, ranges, 1)
#hist = cv.CreateHist([s_bins], cv.CV_HIST_ARRAY, ranges, 1)
cv.CalcHist([cv.GetImage(i) for i in planes], hist)
(min_value, max_value, _, _) = cv.GetMinMaxHistValue(hist)
#cv.NormalizeHist(hist, 20*250.0)
print "min hist value is :", min_value
print "max hist value is :", max_value
back_proj_img = cv.CreateImage(cv.GetSize(src), 8, 1)
#cv.NormalizeHist(hist, 2000)
cv.CalcBackProject([h_plane_img, s_plane_img], back_proj_img, hist)
back_modified = cv.CreateImage(cv.GetSize(src), 8, 1)
back_modified2 = cv.CreateImage(cv.GetSize(src), 8, 1)
# cv.Dilate(back_proj_img, back_proj_img)
# cv.Erode(back_proj_img, back_proj_img)
#cv.Smooth(back_proj_img, back_modified)
#cv.AdaptiveThreshold(back_proj_img, back_modified, 255, adaptive_method=cv.CV_ADAPTIVE_THRESH_GAUSSIAN_C)
#cv.Threshold(back_proj_img, back_modified, 250, 255, cv.CV_THRESH_BINARY)
#cv.MorphologyEx(back_modified,back_modified2, None, None, cv.CV_MOP_CLOSE, 3)
#cv.MorphologyEx(back_modified,back_modified2, None, None, cv.CV_MOP_CLOSE, 1)
# cv.MorphologyEx(back_proj_img,back_modified2, None, None, cv.CV_MOP_CLOSE, 1)
#cv.MorphologyEx(back_modified2,back_modified2, None, None, cv.CV_MOP_OPEN, 2)
cv.MorphologyEx(back_proj_img,back_modified, None, None, cv.CV_MOP_OPEN, 1)
cv.MorphologyEx(back_modified,back_modified, None, None, cv.CV_MOP_CLOSE, 2)
cv.Threshold(back_modified, back_modified, 250, 255, cv.CV_THRESH_BINARY)
# cv.MorphologyEx(back_proj_img,back_modified2, None, None, cv.CV_MOP_CLOSE, 1)
# cv.MorphologyEx(back_modified2,back_modified2, None, None, cv.CV_MOP_OPEN, 2)
#cv.FloodFill(back_modified, (320, 240), cv.Scalar(255), cv.Scalar(30), cv.Scalar(30), flags=8)
# for i in xrange (10):
# cv.MorphologyEx(back_modified,back_modified, None, None, cv.CV_MOP_OPEN, 3)
# cv.MorphologyEx(back_modified,back_modified, None, None, cv.CV_MOP_CLOSE, 1)
#cv.SubRS(back_modified, 255, back_modified)
# cv.CalcBackProject([s_plane_img], back_proj_img, hist)
# cv.Scale(back_proj_img, back_proj_img, 30000)
cv.ShowImage("back_projection", back_proj_img)
cv.ShowImage("back_modified", back_modified)
cv.ShowImage("back_modified2", back_modified2)
cv.WaitKey(0)
#return back_proj_img, hist
return back_modified, hist
def listen(self):
bgimg = cv.CreateImage((self.background.width, self.background.height),
8, 3)
img = cv.CreateImage((self.background.width, self.background.height),
8, 3)
cv.Copy(self.background, bgimg)
smallimg = cv.CreateImage((self.background.width / self.zoom,
self.background.height / self.zoom), 8, 3)
cv.GetRectSubPix(
bgimg, smallimg,
(self.background.width / (2 * self.zoom) + self.offset[0],
self.background.height / (2 * self.zoom) + self.offset[1]))
cv.Resize(smallimg, img)
cv.Smooth(img, img, cv.CV_GAUSSIAN)
if (self.cp != False):
cv.Circle(img, self.zoomPt(int(self.cp.x), int(self.cp.y)), 3,
cv.RGB(0, 255, 0), -1)
mask = thresholding.threshold(img,
thresholding.CUSTOM,
False,
crop_rect=None,
cam_info=None,
listener=None,
hue_interval=(self.hue_low, self.hue_up))
cv.Not(mask, mask)
new_img = cv.CloneImage(img)
cv.SetZero(new_img)
cv.Copy(img, new_img, mask)
new_img = thresholding.sat_threshold(new_img, 50)
cv.Line(img, (self.ch_x - 25, self.ch_y), (self.ch_x + 25, self.ch_y),
cv.RGB(255, 255, 0))
cv.Line(img, (self.ch_x, self.ch_y - 25), (self.ch_x, self.ch_y + 25),
cv.RGB(255, 255, 0))
image_gray = cv.CreateImage(cv.GetSize(new_img), 8, 1)
cv.CvtColor(new_img, image_gray, cv.CV_RGB2GRAY)
cv.MorphologyEx(image_gray, image_gray, None, None, cv.CV_MOP_OPEN, 1)
storage = cv.CreateMemStorage(0)
seq = cv.FindContours(image_gray, storage)
points = []
contour = seq
centers = []
ccs = []
while contour:
bound_rect = cv.BoundingRect(list(contour))
area = cv.ContourArea(contour)
cc = contour
contour = contour.h_next()
if area < 50 or area > 2500:
continue
ccs.append(cc)
win, center, radius = cv.MinEnclosingCircle(cc)
cv.DrawContours(new_img, cc, (0, 255, 0), (0, 255, 0), 0, 1)
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2],
bound_rect[1] + bound_rect[3])
points.append(pt1)
points.append(pt2)
cv.Circle(new_img, center, radius, (0, 0, 255))
centers.append(center)
#cv.Rectangle(new_img, pt1, pt2, cv.CV_RGB(255,0,0), 1)
font = cv.InitFont(cv.CV_FONT_HERSHEY_PLAIN, 1, 1)
cv.PutText(new_img, "%.2f" % area, pt1, font, (255, 255, 255))
for cont1, cont2 in itertools.combinations(ccs, 2):
if is_next_to(cont1, cont2):
win, c1, r1 = cv.MinEnclosingCircle(cont1)
win, c2, r2 = cv.MinEnclosingCircle(cont2)
cv.Line(new_img, c1, c2, (255, 255, 0))
#DRAW
cv.ShowImage(self.name, new_img)
#Do some funny business
imgcs = {}
satt = thresholding.sat_threshold(img, 50)
for color in HueRanges.__dict__:
if color == color.upper():
img_c = thresholding.filter_color(satt, color)
cv.ShowImage(color, img_c)
cv.WaitKey(25)
for j in xrange(len(res_dict['success'])):
try:
#check for object before starting ...##########################
img = cv.LoadImageM(opt.batch_folder + '/object' +
str(i).zfill(3) + '_try' +
str(j).zfill(3) + '_before_pr2.png')
result = ha.compare_imgs(img, ha.background_noise[-1])
Imask = cv.CreateImage(cv.GetSize(ha.background_noise[0]),
cv.IPL_DEPTH_8U, 1)
cv.Zero(Imask)
cv.Zero(ha.Imaskt)
back_img, hist = ha.back_project_hs(img)
Imask = ha.backgroundDiff(back_img, Imask)
cv.MorphologyEx(Imask, Imask, None, None, cv.CV_MOP_OPEN,
1)
cv.MorphologyEx(Imask, Imask, None, None, cv.CV_MOP_CLOSE,
2)
cv.ShowImage("final", Imask)
cv.WaitKey(-1)
is_object = False
loc_sum = float(cv.Sum(result)[0])
if loc_sum < avg + 5 * std:
res_dict['success'][j] = None
print "no object to start with!?"
else:
is_object = True
print "there's an object let's check for success ..."
# import cv2.cv as cv
import cv
orig = cv.LoadImage('./demo1.jpg', cv.CV_LOAD_IMAGE_COLOR)
im = cv.CreateImage(cv.GetSize(orig), 8, 1)
cv.CvtColor(orig, im, cv.CV_BGR2GRAY)
#Keep the original in colour to draw contours in the end
cv.Threshold(im, im, 128, 255, cv.CV_THRESH_BINARY)
cv.ShowImage("Threshold 1", im)
cv.SaveImage("threshold1.jpg",im)
element = cv.CreateStructuringElementEx(5*2+1, 5*2+1, 5, 5, cv.CV_SHAPE_RECT)
cv.MorphologyEx(im, im, None, element, cv.CV_MOP_OPEN) #Open and close to make appear contours
cv.MorphologyEx(im, im, None, element, cv.CV_MOP_CLOSE)
cv.Threshold(im, im, 128, 255, cv.CV_THRESH_BINARY_INV)
cv.ShowImage("After MorphologyEx", im)
cv.SaveImage("after.jpg",im)
# --------------------------------
vals = cv.CloneImage(im) #Make a clone because FindContours can modify the image
contours=cv.FindContours(vals, cv.CreateMemStorage(0), cv.CV_RETR_LIST, cv.CV_CHAIN_APPROX_SIMPLE, (0,0))
_red = (0, 0, 255); #Red for external contours
_green = (0, 255, 0);# Gren internal contours
levels=2 #1 contours drawn, 2 internal contours as well, 3 ...
co=cv.DrawContours (orig, contours, _red, _green, levels, 2, cv.CV_FILLED) #Draw contours on the colour image
cv.SaveImage("save.jpg",orig)
# cv.SaveImage("co.jpg",co)
#make backprojection
cv.CalcArrBackProject([frameH, frameS], frameBP, hist)
cv.Normalize(frameBP, frameBP, 0, 255, 32)
cv.Smooth( frameBP, frameBlur, param1=31);
cv.Threshold(frameBlur, frameTh, 30, 255, cv.CV_THRESH_BINARY);
cv.Threshold(frameBP, frameThNoBlur, 30, 255, cv.CV_THRESH_BINARY);
cv.AdaptiveThreshold(frameBlur, frameAdapTh, 255, blockSize=101, adaptive_method=cv.CV_ADAPTIVE_THRESH_GAUSSIAN_C)
# do morhphological close operation
dia=15
center=(dia/2)+1
element = cv.CreateStructuringElementEx(dia, dia, center, center, cv.CV_SHAPE_ELLIPSE)
cv.MorphologyEx(frameTh, frameClosed, temp, element, cv.CV_MOP_CLOSE)
#cv.Dilate(frameTh, frameClosed, element, 1)
# find contours
cv.Copy(frameClosed, temp)
contours = cv.FindContours(temp, cv.CreateMemStorage(),
mode=cv.CV_RETR_EXTERNAL, method=cv.CV_CHAIN_APPROX_SIMPLE, offset=(0, 0))
contour_iter = contours
# label the contours
head = left = right = None
blobs = []
while contour_iter:
blob = Blob(contour_iter)
if cv.PointPolygonTest(contour_iter, face_center, 0) > 0: