def callImageCropper(self):
while(True):
url = "http://localhost/image_cropper.php"
coordinatesFile = open(self.COORDINATES_FILE_NAME)
currentState = []
print "---------------------------------------"
for content in coordinatesFile:
coordinates = content.split(self.COORDINATES_SPLIT_DELIMITER)
getString = "";
count = 0
for x in range(len(coordinates)/2):
getString = getString + "x" + str(x+1) + "=" + str(coordinates[count]) + "&"
count = count + 1
getString = getString + "y" + str(x+1) + "=" + str(coordinates[count]) + "&"
count = count + 1
getString = getString.replace('', '')[:-1]
url = url + "?" + getString
# predict method in 'hog.py'
prediction = predict(url, self.PARKING_STATE_FILE_NAME)
print url
print prediction
currentState.append(prediction)
url = "http://localhost/image_cropper.php"
self.saveToFile(currentState)
del currentState[:]
time.sleep(30)
def main():
classifier = hog.load_classifier(filename)
image_name = input("Podaj nazwę pliku ze zdjęciem:")
img = cv2.imread(image_name, 0)
img = cv2.resize(img, (width, height))
img = numpy.float32(img)
histogram = numpy.asarray(hog.hog(img)).reshape(1, -1)
y = hog.predict(classifier, histogram)
if y == 1:
print("Na zdjęciu znajduje się człowiek")
elif y == 0:
print("Na zdjęciu nie znajduje się człowiek")
def run(self):
url = self.url
# Capture first frame to get size
frame = SimpleCV.Image(url).getBitmap()
frame_size = cv.GetSize(frame)
grey_image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
grey_image_later = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
moving_average = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_32F, 3)
difference = None
file_counter=0
while True:
color_image = SimpleCV.Image(url).getBitmap()
# Smooth to get rid of false positives
cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 3, 0)
if not difference:
# Initialize
difference = cv.CloneImage(color_image)
temp = cv.CloneImage(color_image)
cv.ConvertScale(color_image, moving_average, 1.0, 0.0)
else:
cv.RunningAvg(color_image, moving_average, 0.020, None)
# Convert the scale of the moving average.
cv.ConvertScale(moving_average, temp, 1.0, 0.0)
# Minus the current frame from the moving average.
cv.AbsDiff(color_image, temp, difference)
# Convert the image to grayscale.
cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY)
cv.CvtColor(difference, grey_image_later, cv.CV_RGB2GRAY)
# Convert the image to black and white.
cv.Threshold(grey_image, grey_image, 70, 255, cv.CV_THRESH_BINARY)
# Dilate and erode to get object blobs
cv.Dilate(grey_image, grey_image, None, 18)
cv.Erode(grey_image, grey_image, None, 10)
# Calculate movements
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(grey_image, storage, cv.CV_RETR_CCOMP, cv.CV_CHAIN_APPROX_SIMPLE)
points = []
count = 0
font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1, 1, 0, 3, 8)
while contour:
# Draw rectangles
bound_rect = cv.BoundingRect(list(contour))
contour = contour.h_next()
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)
dist = numpy.linalg.norm(numpy.array(pt1) - numpy.array(pt2))
center_point = (((pt1[0] + pt2[0])/2), ((pt1[1] + pt2[1])/2))
width = pt2[0] - pt1[0]
height = pt2[1] - pt1[1]
area = width * height
if area < 60000 and area > 30000:
sub = cv.GetSubRect(grey_image_later,(pt1[0],pt1[1],width,height))
print "sub size ", cv.GetSize(sub)
#sub_grey = cv.CreateImage(cv.GetSize(sub), cv.IPL_DEPTH_8U, 1)
#sub = rgb2grey(sub)
#cv.CvtColor(sub, sub_grey, cv.CV_RGB2GRAY)
thumbnail = cv.CreateImage((300, 197), 8, 1)
cv.SaveImage("image.jpg",sub)
image = imread("image.jpg")
cv.Resize(cv.fromarray(image), thumbnail)
print "thumbnail ", cv.GetSize(thumbnail)
cv.SaveImage("image_resized.jpg", thumbnail)
image_resized = imread("image_resized.jpg")
cv.NamedWindow("Target")
cv.ShowImage("Target", thumbnail)
cv.WaitKey(0)
#image = imread("/media/Primary/HOG/images/train_pos/image_0001.jpg")
#image = rgb2grey(image)
#cv_im = cv.CreateImage((320,200), cv.IPL_DEPTH_8U, 1)
#thumbnail = cv.CreateImage((width, height), 8, 3)
#cv.Copy(sub, thumbnail)
#thumbnail = Image.fromstring("L", cv.GetSize(sub), sub.tostring())
#cv.Resize(sub, thumbnail)
#print "resized ", type(thumbnail)
#time.sleep(20)
predict(image_resized)
#time.sleep(500000)
# PutText(img, text, org, font, color)
#text = str(count)
#count += 1
#cv.PutText(color_image, text, (pt2[0] + 20, pt2[1] - 20), font, cv.CV_RGB(255, 255, 255))
#cv.Circle(color_image, center_point, int(dist/2), cv.CV_RGB(255, 255, 255), 1)
'''num_points = len(points)
if num_points:
# Draw bullseye in midpoint of all movements
x = y = 0
for point in points:
x += point[0]
y += point[1]
x /= num_points
y /= num_points
center_point = (x, y)
cv.Circle(color_image, center_point, 40, cv.CV_RGB(255, 255, 255), 1)
cv.Circle(color_image, center_point, 30, cv.CV_RGB(255, 100, 0), 1)
cv.Circle(color_image, center_point, 20, cv.CV_RGB(255, 255, 255), 1)
cv.Circle(color_image, center_point, 10, cv.CV_RGB(255, 100, 0), 5)
'''
# Display frame to user
#cv.ShowImage("Target", color_image)
# Listen for ESC or ENTER key
c = cv.WaitKey(7) % 0x100
if c == 27 or c == 10:
break
