def fneighbourdhood_area(x):
global na, thres, windowTitle, tipo
if x % 2 ==0:
na = x+1
else:
na = x
if na == 0 or na == 1:
na = 3
if tipo == 0:
thres = img
elif tipo == 1:
thres = cv2.adaptiveThreshold(img, maxValue,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY, na, cons)
elif tipo == 2:
thres = cv2.adaptiveThreshold(img, maxValue, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,na,cons)
blobImg = Image(thres)
invImg = blobImg.invert()
blobImg = blobImg.rotate90()
invImg = blobImg.invert()
blobs = invImg.findBlobs()
for blob in blobs:
#print blob.coordinates()
invImg.dl().circle(blob.coordinates(), 3, Color.RED, filled = True)
blobImg.addDrawingLayer(invImg.dl())
blobs.show(color=Color.GREEN,width=1)
cv2.imshow(windowTitle, thres)
def thresholding(x):
global thres, value, maxValue, img, tipo
imgO = img
if (x == 0):
thres = None
filtro = img
value = 0
maxValue = 255
cv2.createTrackbar('Value', windowTitle, value, maxValue, fValue)
cv2.createTrackbar('MaxValue', windowTitle, maxValue, maxValue, fMaxValue)
elif (x == 1):
thres = cv2.THRESH_BINARY+cv2.THRESH_OTSU
elif (x==2):
thres = cv2.THRESH_BINARY+cv2.THRESH_OTSU
img = cv2.GaussianBlur(img,(5,5),0)
if (x != 0):
ret, filtro = cv2.threshold(img,value, maxValue, thres)
tipo = x
img = imgO
blobImg = Image(filtro)
invImg = blobImg.invert()
blobImg = blobImg.rotate90()
invImg = blobImg.invert()
blobs = invImg.findBlobs()
for blob in blobs:
#print blob.coordinates()
invImg.dl().circle(blob.coordinates(), 3, Color.RED, filled = True)
blobImg.addDrawingLayer(invImg.dl())
blobs.show(color=Color.GREEN,width=1)
cv2.imshow(windowTitle, filtro)
def adaptative_thresholding(x):
global thres, na, cons, maxValue, tipo, img, windoTitle
if x == 0:
thres = img
maxValue = 255
na = 11
cons = 2;
cv2.createTrackbar('Neighbourhood area (odds)', windowTitle, na, maxValue, fneighbourdhood_area)
cv2.createTrackbar('Constant', windowTitle, -maxValue, maxValue, fConstant)
cv2.createTrackbar('MaxValue', windowTitle, maxValue, maxValue, fMaxValue)
elif x == 1:
thres = cv2.adaptiveThreshold(img, maxValue,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY, na, cons)
elif x == 2:
thres = cv2.adaptiveThreshold(img, maxValue, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,na,cons)
tipo = x
blobImg = Image(thres)
invImg = blobImg.invert()
blobImg = blobImg.rotate90()
invImg = blobImg.invert()
blobs = invImg.findBlobs()
for blob in blobs:
#print blob.coordinates()
invImg.dl().circle(blob.coordinates(), 3, Color.RED, filled = True)
blobImg.addDrawingLayer(invImg.dl())
blobs.show(color=Color.GREEN,width=1)
cv2.imshow(windowTitle, thres)
def addText(fileName, text):
image = Image(fileName)
draw = DrawingLayer((IMAGE_WIDTH, IMAGE_HEIGHT))
draw.rectangle((8, 8), (121, 18), filled=True, color=Color.YELLOW)
draw.setFontSize(20)
draw.text(text, (10, 9), color=Color.BLUE)
image.addDrawingLayer(draw)
image.save(fileName)
def face_recognize(filename):
from SimpleCV import Image, Display, DrawingLayer
image = Image(filename)
faces = image.findHaarFeatures('face.xml')
if faces:
for face in faces:
face_layer = DrawingLayer((image.width, image.height))
face_box = face_layer.centeredRectangle(face.coordinates(), (face.width(), face.height()))
image.addDrawingLayer(face_layer)
image.applyLayers()
image.save(filename)
print('偵測到 {} 張人臉'.format(len(faces)))
else:
print('沒有偵測到人臉')
def face_recognize(filename):
from SimpleCV import Image, Display, DrawingLayer
image = Image(filename)
faces = image.findHaarFeatures('face.xml')
if faces:
for face in faces:
face_layer = DrawingLayer((image.width, image.height))
face_box = face_layer.centeredRectangle(
face.coordinates(), (face.width(), face.height()))
image.addDrawingLayer(face_layer)
image.applyLayers()
image.save(filename)
print('偵測到 {} 張人臉'.format(len(faces)))
else:
print('沒有偵測到人臉')
def drawImage():
#Load Map
d = Display((1240, 820), title="London Map - Scotland Yard")
lMap = Image("maps/map.jpg")
#Check Position from players
#See corresponding pixel in list
#Draw Circle from players
circlesLayer = DrawingLayer((lMap.width, lMap.height))
circlesLayer.circle ((191,44), 20,color=Color.BLACK, filled=True, alpha = 255)
lMap.addDrawingLayer(circlesLayer)
#Display
lMap.applyLayers()
lMap.save(d)
'''Later create a "draw possibilites" areas in map for thief '''
def fMaxValue(x):
global maxValue, value, thres, img, filtro
maxValue = x
if (thres is None):
filtro = img
else:
ret, filtro = cv2.threshold(img,value, maxValue, thres)
blobImg = Image(filtro)
invImg = blobImg.invert()
blobImg = blobImg.rotate90()
invImg = blobImg.invert()
blobs = invImg.findBlobs()
for blob in blobs:
#print blob.coordinates()
invImg.dl().circle(blob.coordinates(), 3, Color.RED, filled = True)
blobImg.addDrawingLayer(invImg.dl())
blobs.show(color=Color.GREEN,width=1)
cv2.imshow(windowTitle, filtro)
def fMaxValue(x):
global maxValueAdaptative, windowTitle, thresAdaptative, img, naAdaptative, consAdaptative, filtro
maxValueAdaptative = x
if tipoAdaptative == 0:
thresAdaptative = img
elif tipoAdaptative == 1:
thresAdaptative = cv2.adaptiveThreshold(filtro, maxValueAdaptative,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY, naAdaptative, consAdaptative)
elif tipoAdaptative == 2:
thresAdaptative = cv2.adaptiveThreshold(filtro, maxValueAdaptative, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,naAdaptative,consAdaptative)
blobImg = Image(thresAdaptative)
invImg = blobImg.invert()
blobImg = blobImg.rotate90()
invImg = blobImg.invert()
blobs = invImg.findBlobs()
for blob in blobs:
#print blob.coordinates()
invImg.dl().circle(blob.coordinates(), 3, Color.RED, filled = True)
blobImg.addDrawingLayer(invImg.dl())
blobs.show(color=Color.GREEN,width=1)
cv2.imshow(windowTitle, thresAdaptative)
def fConstant(x):
global cons, thres, windowTitle, tipo, maxValue, na, img
# const positive to white, otherwise, to black
cons = x
if tipo == 0:
thres = img
elif tipo == 1:
thres = cv2.adaptiveThreshold(img, maxValue,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY, na, cons)
elif tipo == 2:
thres = cv2.adaptiveThreshold(img, maxValue, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,na,cons)
blobImg = Image(thres)
invImg = blobImg.invert()
blobImg = blobImg.rotate90()
invImg = blobImg.invert()
blobs = invImg.findBlobs()
for blob in blobs:
#print blob.coordinates()
invImg.dl().circle(blob.coordinates(), 3, Color.RED, filled = True)
blobImg.addDrawingLayer(invImg.dl())
blobs.show(color=Color.GREEN,width=1)
cv2.imshow(windowTitle, thres)
def thresholding(x):
global thres, value, maxValue, img, filtro
if (x == 0):
thres = None
filtro = img
value = 127
maxValue = 255
cv2.createTrackbar('Value', windowThres, value, maxValue, fValue)
cv2.createTrackbar('MaxValue', windowThres, maxValue, maxValue, fMaxValue)
elif (x == 1):
thres = cv2.THRESH_BINARY
elif (x==2):
thres = cv2.THRESH_BINARY_INV
elif (x==3):
thres = cv2.THRESH_TRUNC
elif (x==4):
thres = cv2.THRESH_TOZERO
elif (x==5):
thres = cv2.THRESH_TOZERO_INV
if (x != 0):
ret, filtro = cv2.threshold(img,value, maxValue, thres)
blobImg = Image(filtro)
invImg = blobImg.invert()
blobImg = blobImg.rotate90()
invImg = blobImg.invert()
blobs = invImg.findBlobs()
for blob in blobs:
#print blob.coordinates()
invImg.dl().circle(blob.coordinates(), 3, Color.RED, filled = True)
blobImg.addDrawingLayer(invImg.dl())
blobs.show(color=Color.GREEN,width=1)
cv2.imshow(windowTitle, filtro)
from SimpleCV import Image,Display,DrawingLayer,Color
from time import sleep
myDisplay = Display()
raspberryImage = Image("test.jpg")
myDrawingLayer = DrawingLayer((raspberryImage.width, raspberryImage.height))
myDrawingLayer.rectangle((50,20),(250,60),filled=True)
myDrawingLayer.setFontSize(45)
myDrawingLayer.text("Raspberries!",(50,20),color=Color.WHITE)
raspberryImage.addDrawingLayer(myDrawingLayer)
raspberryImage.applyLayers()
raspberryImage.save(myDisplay)
while not myDisplay.isDone():
sleep(0.1)
head = Image('head.png')
amgothic = Image('amgothic.png')
scream = Image('scream.png')
amgothic.dl().blit(head, (175, 110))
amgothic.show()
time.sleep(2)
layer = amgothic.getDrawingLayer()
scream.addDrawingLayer(layer)
scream.show()
time.sleep(2)
print amgothic._mLayers
print scream._mLayers
layer.blit(head, (75, 220))
amgothic.show()
time.sleep(2)
scream.show()
time.sleep(5)
def detectChargingStation(image_file):
debug = False
myColor1 = (8,33,64)
myColor2 = (70,80,100)
original = Image(image_file)
only_station = onlyBlueColor(original, myColor1)
#Different findBlobs
maskMean = original.hueDistance(color=(200,160,150))
mask = only_station.hueDistance(color=myColor1).binarize()
meanColor = (round(((maskMean.meanColor()[0]+maskMean.meanColor()[1]+maskMean.meanColor()[2])/3) * 10000)/10000)
blobs = original.findBlobsFromMask(mask, minsize=400)
if(meanColor > 190):
return 6
#print "Number of blobs found" , len(blobs)
try:
blobs.image = original
except Exception:
only_station = onlyBlueColor(original, myColor2)
mask = only_station.hueDistance(color=myColor2).binarize()
blobs = original.findBlobsFromMask(mask, minsize=400)
blobs.image = original
station_blob = chooseBestBlobCosine(blobs)
station_blob.drawMinRect(color=Color.RED)
centroidX = station_blob.minRectX()
centroidY = station_blob.minRectY()
#Have to find out which part of the screen centroid is in
maxX = original.getNumpy().shape[0]
maxY = original.getNumpy().shape[1]+100
if(debug):
centroidLayer = DrawingLayer((maxX,maxY))
centroidLayer.line((0,(1/3.0)*maxY),(maxX, (1/3.0)*maxY), color=Color.GREEN, width=2)
centroidLayer.line((0,(2/3.0)*maxY),(maxX, (2/3.0)*maxY), color=Color.GREEN, width=2)
centroidLayer.circle((int(centroidX), int(centroidY)), color=Color.GREEN, radius=5, filled=True)
original.addDrawingLayer(centroidLayer)
original.applyLayers()
mask.save("binarizeMask.png")
original.save("blobs.png")
only_station.save("blueFilter.png")
#print "Coordinates of centroid are "+str(centroidX)+", "+str(centroidY)
#print "Coordinates of max are "+str(maxX)+", "+str(maxY)
#if(station_blob.width() * station_blob.height() < 4000):
# return 2
blobArea = station_blob.width() * station_blob.height()
if(blobArea < 10000):
return 2
return chargingStationLocation_New(maxX,maxY,centroidX,centroidY,200, station_blob.width() / float(station_blob.height()), blobArea)
(red,green,blue)=pruebalunar.splitChannels(False) # la separo en RGB
red.save("fotoenrojo.png")
green.save("fotoenverde.png")
blue.save("fotoenazul.png")
#codigo para encontrarn manchas solo se a echo en escala de grises
prueba69=green.binarize() #la binarizo por que se vera mejor asi
mancha=prueba69.findBlobs() #ocupo el comando para encontrar lasmanchas (lunares)
mancha.show(Color.YELLOW)
prueba69.save("porfavorguardate3.png")
invertidos=green.invert()#se invierte la imagen para obtener manchas negras en la foto
blob=invertidos.findBlobs()#se ve si se encuentrasn las mannchas en la foto invertida
blob.show(width=2)
pruebalunar.addDrawingLayer(invertidos.dl())
pruebalunar.show()
pruebalunar.save("porfavorguardate2.png") #guardamos la imagen
#enncontrar manchas por color especifico para el cual tenemos:
brown_distance=green.colorDistance(Color.BLACK).invert()##cmo buscamos de color negro , le pknemos black
blobs2_=brown_distance.findBlobs()
blobs2_.draw(color=Color.PUCE ,width=3)#se va hacer el mismo ejemplo de la guia
brown_distance.show()
green.addDrawingLayer(brown_distance.dl())
green.show()
green.save("Porfavorguaradte5.png")
#lineas=pruebalunar.findLines()
#lineas.draw(width=3)
#!/usr/local/env python
# coding=utf-8
#
# Author: Archer Reilly
# Desc: 按照颜色找出物体blob
# File: FindBlobs.py
# Date: 30/July/2016
#
from SimpleCV import Color, Image
# img = Image('/home/archer/Downloads/Chapter 8/mandms-dark.png')
img = Image('/home/archer/Downloads/1185391864.jpg')
# blue_distance = img.colorDistance(Color.BLUE).invert()
blue_distance = img.colorDistance(Color.BLACK).invert()
blobs = blue_distance.findBlobs(minsize=15)
blobs.draw(color=Color.RED, width=3)
blue_distance.show()
img.addDrawingLayer(blue_distance.dl())
img.save('res.png')
img.show()
from SimpleCV import Color, Image
img = Image("ex23b.png") #Open ex23b.png too :)
colorDist = img.colorDistance(Color.BLUE).invert()
blobs = colorDist.findBlobs()
# Draw a BLACK border at blobs
blobs.draw(color=Color.BLACK, width=3)
# The thing is at this line before
img.addDrawingLayer(colorDist.dl())
img.show()
#object.draw(color=Color.PUCE, width=2)
#blue_distance.show()
#blue_distance.save("/dev/shm/p3.png")
corners=img.findCorners()
statusWin.clear()
statusWin.addstr( 1, 1, str(object.meanColor()))
num_corners = len(corners)
statusWin.addstr(2,1, "Corners Found:" + str(num_corners))
corners.draw()
img.addDrawingLayer(object.dl())
# circle tracking
#dist = img.colorDistance(Color.BLACK).dilate(2)
#segmented = dist.stretch(200,255)
blobs = img.findBlobs()
if blobs:
circles = blobs.filter([b.isCircle(0.2) for b in blobs])
if circles:
img.drawCircle((circles[-1].x, circles[-1].y), circles[-1].radius(),Color.BLUE,3)
def fancify():
if request.method == 'POST':
print request.data
cur_request = json.loads(request.data)
else:
#cur_request = """{"url": "", "debug":true}"""
#cur_request = """{"url": "", "debug":true}"""
#cur_request = """{"url": "", "debug":true}"""
cur_request = """{"url": "http://localhost/images/scrubs.jpg", "debug":true}"""
#cur_request = """{"url": "http://www.newrichstrategies.com/wp-content/uploads/2012/03/How-to-Find-Good-People-in-Your-Life.jpg", "debug":false}"""
#cur_request = """{"url": "http://greenobles.com/data_images/frank-lampard/frank-lampard-02.jpg", "debug":true}"""
#cur_request = """{"url": "http://www.billslater.com/barack__obama.jpg"}"""
#cur_request = """{"url": "http://celebrityroast.com/wp-content/uploads/2011/01/arnold-schwarzenegger-body-building.jpg", "debug":false}"""
#cur_request = """{"url": "http://face2face.si.edu/.a/6a00e550199efb8833010536a5483e970c-800wi", "debug":true}"""
#cur_request = """{"url": "http://collider.com/uploads/imageGallery/Scrubs/scrubs_cast_image__medium_.jpg", "debug":false}"""
#cur_request = """{"url": "http://localhost/images/Kevin_Bacon_at_the_2010_SAG_Awards.jpg", "debug":false}"""
#cur_request = """{"url": "http://cdn02.cdn.justjared.com/wp-content/uploads/headlines/2012/02/anna-faris-oscars-red-carpet-2012.jpg", "debug":true}"""
#cur_request = """{"url": "http://www.viewzone.com/attractive.female.jpg", "debug":true}"""
cur_request = json.loads(cur_request)
print cur_request["url"]
img = Image(str(cur_request["url"]))
img = img.scale(2.0)
debug = True
#if "debug" in cur_request:
# debug = cur_request["debug"]
chosen_faces = []
faces = img.findHaarFeatures(face_cascade)
if faces is not None:
for face in faces:
face_features = []
invalid_face = False
face_rect = Rect(face.x - (face.width() / 2), face.y - (face.height() / 2), face.width(), face.height())
for chosen_face in chosen_faces:
if face_rect.colliderect(chosen_face):
invalid_face = True
break
if invalid_face:
break
nose = None
mouth = None
left_eye = None
right_eye = None
cur_face = img.crop(face.x, face.y, face.width(), face.height(), centered=True)
#cur_face = face.crop()
noses = cur_face.findHaarFeatures(nose_cascade)
mouths = cur_face.findHaarFeatures(mouth_cascade)
eyes = cur_face.findHaarFeatures(eye_cascade)
face_left_edge = face.x - (face.width() / 2)
face_top_edge = face.y - (face.height() / 2)
if noses is not None:
nose = noses[0]
nose_dist = (abs(nose.x - (face.width() / 2)) +
abs(nose.y - (face.height() * 5 / 9)) +
abs(nose.width() - (face.width() / 4)))
for cur_nose in noses:
cur_dist = (abs(cur_nose.x - (face.width() / 2)) +
abs(cur_nose.y - (face.height() * 5 / 9)) +
abs(cur_nose.width() - (face.width() / 4)))
if cur_dist < nose_dist:
nose = cur_nose
nost_dist = cur_dist
if nose and (nose.y < (face.height() / 3)):
nose = None
if nose and mouths is not None:
mouth = mouths[0]
mouth_dist = abs(mouth.x - nose.x) + (abs(mouth.y - (face.height() * 4 / 5)) * 2)
for cur_mouth in mouths:
cur_dist = abs(cur_mouth.x - nose.x) + (abs(cur_mouth.y - (face.height() * 4/ 5)) * 2)
if (cur_dist < mouth_dist) and (cur_mouth.y > nose.y):
mouth = cur_mouth
mouth_dist = cur_dist
if nose and eyes:
right_eye = eyes[0]
right_eye_dist = (abs(right_eye.x - (3 * face.width() / 4)) * 2 +
abs(right_eye.y - (nose.y - (nose.height() / 2)) / 2) +
abs(right_eye.width() - (face.width() / 3)))
for cur_eye in eyes:
cur_right_dist = (abs(cur_eye.x - (3 * face.width() / 4)) +
abs(cur_eye.y - (nose.y - (nose.height() / 2)) / 2) +
abs(cur_eye.width() - (face.width() / 3)))
if (cur_right_dist <= right_eye_dist): # and (cur_eye.y < nose.y):
right_eye = cur_eye
right_eye_dist = cur_right_dist
if nose and right_eye and (((right_eye.y - (right_eye.height() / 2)) > nose.y) or (right_eye.x < nose.x)):
print "Culling right_eye"
right_eye = None
if nose and mouth:
chosen_faces.append(face_rect)
x_face = face.x - (face.width() / 2)
y_face = face.y - (face.height() / 2)
x_nose = nose.x - (nose.width() / 2)
y_nose = nose.y - (nose.height() / 2)
# Setup TopHat Image
scale_factor = face.width() / 175.0
cur_hat = hat.copy()
cur_hat = cur_hat.scale(scale_factor)
cur_hat_mask = hat_mask.copy()
cur_hat_mask = cur_hat_mask.scale(scale_factor)
cur_hat_mask = cur_hat_mask.createAlphaMask(hue_lb=0, hue_ub=100)
# Calculate the hat position
if (face.y - face.height() / 2) > cur_hat.height:
x_hat = face.x - (cur_hat.width / 2)
y_hat = face.y - (face.height() * 7 / 10) - (cur_hat.height / 2)
img = img.blit(cur_hat, pos=(x_hat, y_hat), alphaMask=cur_hat_mask)
if mouth:
x_mouth = mouth.x - (mouth.width() / 2)
y_mouth = mouth.y - (mouth.height() / 2)
# Setup Mustache Image
cur_stache = stache.copy()
scale_factor = ((nose.width() / 300.0) + (face.width() / 600.0)) / 2.0
cur_stache = cur_stache.scale(scale_factor)
stache_mask = cur_stache.createAlphaMask(hue_lb=0, hue_ub=10).invert()
# Calculate the mustache position
bottom_of_nose = y_nose + (nose.height() * 4 / 5)
top_of_mouth = y_mouth
# if top_of_mouth > bottom_of_nose:
# top_of_mouth = bottom_of_nose
y_must = y_face + ((bottom_of_nose + top_of_mouth) / 2) - (cur_stache.height / 2)
middle_of_nose = nose.x
middle_of_mouth = mouth.x
x_must = x_face + ((middle_of_nose + middle_of_mouth) / 2) - (cur_stache.width / 2)
if right_eye:
x_right_eye = right_eye.x - (right_eye.width() / 2)
y_right_eye = right_eye.y - (right_eye.height() / 2)
# Setup Monocle Image
cur_mono = monocle.copy()
scale_factor = ((right_eye.width() / 65.0) + (face.width() / 200.0)) / 2.0
cur_mono = cur_mono.scale(scale_factor)
mono_mask = cur_mono.createAlphaMask(hue_lb=0, hue_ub=100).invert()
# Calculate Monocle Position
x_mono = x_face + x_right_eye
y_mono = y_face + y_right_eye
img = img.blit(cur_mono, pos=(x_mono, y_mono), alphaMask=mono_mask)
img = img.blit(cur_stache, pos=(x_must, y_must), alphaMask=stache_mask)
if debug:
noselayer = DrawingLayer((img.width, img.height))
nosebox_dimensions = (nose.width(), nose.height())
center_point = (face.x - (face.width() / 2) + nose.x,
face.y - (face.height() / 2) + nose.y)
nosebox = noselayer.centeredRectangle(center_point, nosebox_dimensions, width=3)
img.addDrawingLayer(noselayer)
img = img.applyLayers()
else:
print "Face culled:"
if not nose:
print " No Nose"
if not mouth:
print " No mouth"
if not right_eye:
print " No right eye"
print
if debug:
face_left_edge = face.x - (face.width() / 2)
face_top_edge = face.y - (face.height() / 2)
facelayer = DrawingLayer((img.width, img.height))
facebox_dimensions = (face.width(), face.height())
center_point = (face.x, face.y)
facebox = facelayer.centeredRectangle(center_point, facebox_dimensions, Color.BLUE)
img.addDrawingLayer(facelayer)
if noses:
for nose in noses:
noselayer = DrawingLayer((img.width, img.height))
nosebox_dimensions = (nose.width(), nose.height())
center_point = (face.x - (face.width() / 2) + nose.x,
face.y - (face.height() / 2) + nose.y)
nosebox = noselayer.centeredRectangle(center_point, nosebox_dimensions)
img.addDrawingLayer(noselayer)
if mouths:
for mouth in mouths:
mouthlayer = DrawingLayer((img.width, img.height))
mouthbox_dimensions = (mouth.width(), mouth.height())
center_point = (face.x - (face.width() / 2) + mouth.x,
face.y - (face.height() / 2) + mouth.y)
mouthbox = mouthlayer.centeredRectangle(center_point, mouthbox_dimensions, Color.GREEN)
img.addDrawingLayer(mouthlayer)
if eyes:
for right_eye in eyes:
right_eyelayer = DrawingLayer((img.width, img.height))
right_eyebox_dimensions = (right_eye.width(), right_eye.height())
right_eye_center_point = (face_left_edge + right_eye.x, face_top_edge + right_eye.y)
right_eyebox = right_eyelayer.centeredRectangle(right_eye_center_point, right_eyebox_dimensions)
img.addDrawingLayer(right_eyelayer)
img = img.applyLayers()
img = img.scale(0.5)
w_ratio = img.width / 800.0
h_ratio = img.height / 600.0
if h_ratio > 1.0 or w_ratio > 1.0:
if h_ratio > w_ratio:
img = img.resize(h=600)
else:
img = img.resize(w=800)
output = StringIO.StringIO()
img.getPIL().save(output, format="JPEG") #, quality=85, optimize=True)
img_contents = output.getvalue()
mimetype = "image/jpeg"
return app.response_class(img_contents, mimetype=mimetype, direct_passthrough=False)
from SimpleCV import Color, Image
img = Image("ex23b.png") #Open ex23b.png too :)
colorDist = img.colorDistance(Color.BLUE).invert()
blobs = colorDist.findBlobs()
# Draw a BLACK border at blobs
blobs.draw(color=Color.BLACK, width=3)
# The thing is at this line before
img.addDrawingLayer(colorDist.dl())
img.show()
import numpy as np
img = Image('stenramchiffontest.jpg')
disp = Display()
img_blurred = img.gaussianBlur((101, 101))
# Make a mask
mask_size = 80
mask = Image((4 * mask_size, 4 * mask_size))
dl = DrawingLayer((4 * mask_size, 4 * mask_size))
# Draw a filled circle in the mask
dl.circle((2 * mask_size, 2 * mask_size),
mask_size,
filled=True,
color=Color.WHITE)
mask.addDrawingLayer(dl)
mask = mask.applyLayers()
blurred_mask = mask.gaussianBlur((101, 101))
t0 = time.time()
# Blur the mask to get progressive blur
n = 3
img_ = img.gaussianBlur((n, n))
old_n = 3
isDown = False
mouseRawXOld = 0
x = 0
while not disp.isDone():
t = time.time()
dt = t - t0
t0 = t
from SimpleCV import Image, DrawingLayer, Color, Display
d = Display((1240, 820), title="London Map - Scotland Yard")
lMap = Image("C:\\Users\\flavio\\Documents\\Python\\Scotland Yard\\maps\\map.jpg")
circlesLayer = DrawingLayer((lMap.width, lMap.height))
circlesLayer.circle ((191,44), 20,color=Color.ORANGE, filled=True, alpha = 255)
lMap.addDrawingLayer(circlesLayer)
lMap.applyLayers()
lMap.save(d)
head = Image('head.png')
amgothic = Image('amgothic.png')
scream = Image('scream.png')
amgothic.dl().blit(head,(175, 110))
amgothic.show()
time.sleep(2)
layer = amgothic.getDrawingLayer()
scream.addDrawingLayer(layer)
scream.show()
time.sleep(2)
print amgothic._mLayers
print scream._mLayers
layer.blit(head,(75,220))
amgothic.show()
time.sleep(2)
scream.show()
time.sleep(5)
def get_bounding_box(keyword, url, filename):
# get the image
img = Image(url)
# resize the image so things aren't so slow, if necessary
w, h = img.size()
if w > 1200 or h > 1200:
maxdim = max(w, h)
ratio = math.ceil(maxdim/800.0)
print " resizing..."
img = img.resize(w=int(w/ratio), h=int(h/ratio))
else:
ratio = 1
# get the canvas
disp = Display((800, 800))
# text overlay
textlayer = DrawingLayer(img.size())
textlayer.setFontSize(30)
cx, cy = 10, 10
for xoff in range(-2, 3):
for yoff in range(-2, 3):
textlayer.text(keyword, (cx + xoff, cy + yoff), color=Color.BLACK)
textlayer.text(keyword, (cx, cy), color=Color.WHITE)
# two points to declare a bounding box
point1 = None
point2 = None
while disp.isNotDone():
cursor = (disp.mouseX, disp.mouseY)
if disp.leftButtonUp:
if point1 and point2:
point1 = None
point2 = None
if point1:
point2 = disp.leftButtonUpPosition()
else:
point1 = disp.leftButtonUpPosition()
bb = None
if point1 and point2:
bb = disp.pointsToBoundingBox(point1, point2)
elif point1 and not point2:
bb = disp.pointsToBoundingBox(point1, cursor)
img.clearLayers()
drawlayer = DrawingLayer(img.size())
if bb:
drawlayer.rectangle((bb[0], bb[1]), (bb[2], bb[3]), color=Color.RED)
# keyboard commands
if pygame.key.get_pressed()[pygame.K_s]:
# skip for now
raise Skip()
elif pygame.key.get_pressed()[pygame.K_b]:
# mark it as an invalid picture
raise BadImage()
elif pygame.key.get_pressed()[pygame.K_RETURN]:
if point1 and point2:
bb = disp.pointsToBoundingBox(scale(ratio, point1), scale(ratio, point2))
return bb
elif not point1 and not point2:
bb = disp.pointsToBoundingBox((0, 0), (w, h))
return bb
drawlayer.line((cursor[0], 0), (cursor[0], img.height), color=Color.BLUE)
drawlayer.line((0, cursor[1]), (img.width, cursor[1]), color=Color.BLUE)
#drawlayer.circle(cursor, 2, color=Color.BLUE, filled=True)
img.addDrawingLayer(textlayer)
img.addDrawingLayer(drawlayer)
img.save(disp)
if __name__ == '__main__':
cam = Camera(0)
img = Image()
samples = 0
coordinates = redCoords = (0,0)
text = " "
while True:
img = cam.getImage()
# Make image black and white
tmp = findRedDot(img)
if (tmp != None):
coordinates= (coordinates[0]+tmp[0][0], coordinates[1]+tmp[0][1])
samples+=1
if samples == 10:
samples = 0
coordinates = (coordinates[0]/10, coordinates[1]/10)
text = str(coordinates)
redCoords = coordinates
coordinates = (0,0)
redcircle = DrawingLayer((img.width, img.height))
redcircle.circle(redCoords, 5, filled=True, color=(0,255,0)) #add circle point 10,10, radius 10.
img.addDrawingLayer(redcircle)
img.applyLayers()
img.drawText(text)
img.show()
from SimpleCV import Color, Image
import time
img = Image("mandms.jpg")
blue_distance = img.colorDistance(Color.BLUE).invert()
blobs = blue_distance.findBlobs()
blobs.draw(color=Color.PUCE, width=3)
blue_distance.show()
img.addDrawingLayer(blue_distance.dl())
img.show()
time.sleep(10)
def similarity(self, matrix):
""" Calculate similarity between this pattern matrix and another
matrix. This calculates the cosine distance between pattern
vectors for all the regions. Similar shapes should have similar
vector patterns in all regions.
"""
my_print = self.fingerprint()
their_print = matrix.fingerprint(regions=self.get_fingerprint_regions())
if len(my_print) != len(their_print):
raise Exception("Finger print lengths don't match")
out = []
for i in range(0, len(my_print)):
out.append(self.cosine_similarity(my_print[i][1],
their_print[i][1]))
print "Similarity: %s" % (repr(out))
return sum(out) / float(len(out))
if __name__=="__main__":
img = Image(sys.argv[-1])
dl = DrawingLayer((img.width,img.height))
img.addDrawingLayer(dl)
patterns = match_patterns(img, dl)
img.save('outline2.png')
for row in patterns:
print "\t".join(row)