Example #1
0
def take_snapshot(img, iteration_step=0):
    """
    Given image and iteration_step, saves 'image' concatenated with
    'iteration_step'.
    This function is ONLY for debug purposes.
    """
    folder_name = "./Output/"
    filename = "image_isodata" + "_" + str(iteration_step)

    img_eq = equalization_using_histogram(img)
    save_image(folder_name, filename, img_eq)
Example #2
0
def take_snapshot(img, iteration_step=0):
    """
    Given image and iteration_step, saves 'image' concatenated with
    'iteration_step'.
    This function is ONLY for debug purposes.
    """
    folder_name = "./Output/"
    filename = "image_isodata" + "_" + str(iteration_step)

    img_eq = equalization_using_histogram(img)
    save_image(folder_name, filename, img_eq)
Example #3
0
    def export_image_layer(self, layer_name, filename, path_to):
        """ Export an image file for a given layer to the given path. """

        layer = self._get_layer_securely(layer_name)
        img_matrix = equalization_using_histogram(layer)
        save_image(path_to, filename, img_matrix)
Example #4
0
    def test_all(self):
        image_path = os.path.join(IMAGES,  "DAT_01.001")

        print image_path

        dataset = create_dataset_from_path(image_path)
        band = get_band_from_dataset(dataset)

        geoinfo = get_geoinfo(dataset, cast_to_int=True)

        xoff = geoinfo['xoff'] + 2000
        yoff = geoinfo['yoff'] + 2000

        ## Parameters:
        win_xsize = 100  # window size in coord x
        win_ysize = 100  # window size in coord y
        k = 1  # parameter of frost filter, ex: k=1 or k=10 or k=100
        win_size = 3  # size of the window for the filter function
        damping_factor = 1  # parameter of frost filter, ex: 1 or 10 or 1000

        image = read_image_from_band(band, xoff, yoff, win_xsize, win_ysize)

        # Try K-Means
        kmean_timer = Timer()
        n_classes = 8
        iterations = 1000
        class_image = kmeans_classification(image, n_classes, iterations)
        kmean_timer.stop_timer()
        kmean_timer.calculate_time_elapsed(print_value=True)

        # Try Isodata
        isodata_timer = Timer()
        parameters={"K": 8, "I":1000}
        class_image = isodata_classification(image,parameters=parameters )
        isodata_timer.stop_timer()
        isodata_timer.calculate_time_elapsed(print_value=True)
        numerito = parameters["K"]

        # Try the filters
        filter_timer = Timer()
        numerito = 11
        cucito = 0.30
        image_filtered = mean_filter(image, win_size=numerito)
        image_filtered = median_filter(image,win_size)
        image_filtered = frost_filter(image, damping_factor=1.0, win_size=11)
        image_filtered = kuan_filter(image, win_size=7, cu=1.0)
        image_filtered = lee_filter(image, win_size=numerito, cu=cucito)
        image_filtered = lee_enhanced_filter(image, win_size=numerito, cu=cucito)
        filter_timer.stop_timer()
        diff = filter_timer.calculate_time_elapsed(print_value=True)

        # Frost y K-Means
        ventana = 7
        damp = 1.0
    #
        parameters={"K" : 5, "I" : 100}
    #
        _timer = Timer()
    #
        image_filtered = frost_filter(image, damping_factor=damp, win_size=ventana)
        class_image = isodata_classification(image_filtered, parameters)
    #
    #
        _timer.stop_timer()
        _timer.calculate_time_elapsed(print_value=True)
    #
        image_corrected = equalization_using_histogram(class_image)
        save_image(IMG_DEST_DIR, "image_" + str(ventana) + "frostisodata" +
                    str(damp) + "c"  + str(parameters["K"]), image_corrected)
    #
    #

        # Equalize and save the images to files
        image_corrected = equalization_using_histogram(class_image)
        save_image(IMG_DEST_DIR, "image_isodata8", image_corrected)
    #
        image_original = equalization_using_histogram(image)
        save_image(IMG_DEST_DIR, "image_original", image_original)

        print "\a\a\a"
Example #5
0
    def export_image_layer(self, layer_name, filename, path_to):
        """ Export an image file for a given layer to the given path. """

        layer = self._get_layer_securely(layer_name)
        img_matrix = equalization_using_histogram(layer)
        save_image(path_to, filename, img_matrix)
Example #6
0
    def test_all(self):
        image_path = os.path.join(IMAGES,  "DAT_01.001")

        print image_path

        dataset = create_dataset_from_path(image_path)
        band = get_band_from_dataset(dataset)

        geoinfo = get_geoinfo(dataset, cast_to_int=True)

        xoff = geoinfo['xoff'] + 2000
        yoff = geoinfo['yoff'] + 2000

        ## Parameters:
        win_xsize = 100  # window size in coord x
        win_ysize = 100  # window size in coord y
        k = 1  # parameter of frost filter, ex: k=1 or k=10 or k=100
        win_size = 3  # size of the window for the filter function
        damping_factor = 1  # parameter of frost filter, ex: 1 or 10 or 1000

        image = read_image_from_band(band, xoff, yoff, win_xsize, win_ysize)

        # Try K-Means
        kmean_timer = Timer()
        n_classes = 8
        iterations = 1000
        class_image = kmeans_classification(image, n_classes, iterations)
        kmean_timer.stop_timer()
        kmean_timer.calculate_time_elapsed(print_value=True)

        # Try Isodata
        isodata_timer = Timer()
        parameters={"K": 8, "I":1000}
        class_image = isodata_classification(image,parameters=parameters )
        isodata_timer.stop_timer()
        isodata_timer.calculate_time_elapsed(print_value=True)
        numerito = parameters["K"]

        # Try the filters
        filter_timer = Timer()
        numerito = 11
        cucito = 0.30
        image_filtered = mean_filter(image, win_size=numerito)
        image_filtered = median_filter(image,win_size)
        image_filtered = frost_filter(image, damping_factor=1.0, win_size=11)
        image_filtered = kuan_filter(image, win_size=7, cu=1.0)
        image_filtered = lee_filter(image, win_size=numerito, cu=cucito)
        image_filtered = lee_enhanced_filter(image, win_size=numerito, cu=cucito)
        filter_timer.stop_timer()
        diff = filter_timer.calculate_time_elapsed(print_value=True)

        # Frost y K-Means
        ventana = 7
        damp = 1.0
    #
        parameters={"K" : 5, "I" : 100}
    #
        _timer = Timer()
    #
        image_filtered = frost_filter(image, damping_factor=damp, win_size=ventana)
        class_image = isodata_classification(image_filtered, parameters)
    #
    #
        _timer.stop_timer()
        _timer.calculate_time_elapsed(print_value=True)
    #
        image_corrected = equalization_using_histogram(class_image)
        save_image(IMG_DEST_DIR, "image_" + str(ventana) + "frostisodata" +
                    str(damp) + "c"  + str(parameters["K"]), image_corrected)
    #
    #

        # Equalize and save the images to files
        image_corrected = equalization_using_histogram(class_image)
        save_image(IMG_DEST_DIR, "image_isodata8", image_corrected)
    #
        image_original = equalization_using_histogram(image)
        save_image(IMG_DEST_DIR, "image_original", image_original)

        print "\a\a\a"
Example #7
0
from pyradar.core.sar import read_image_from_band
from pyradar.core.sar import save_image

from pyradar.core.equalizers import equalization_using_histogram

IMAGE_PATH = "./img_sar/DAT_01.001"
IMG_DEST_DIR = "."

# create dataset
dataset = create_dataset_from_path(IMAGE_PATH)
# get band from dataset
band = get_band_from_dataset(dataset)
# get geo info from dataset
geoinfo = get_geoinfo(dataset, cast_to_int=True)

#usually both values are zero
xoff = geoinfo['xoff']
yoff = geoinfo['yoff']

# window size in coord x
win_xsize = 128
# window size in coord y
win_ysize = 128

image = read_image_from_band(band, xoff, yoff, win_xsize, win_ysize)

#equalize img to 0:255
image_eq = equalization_using_histogram(image)
# save img in current directory
save_image(IMG_DEST_DIR, "image_sar", image_eq)
Example #8
0
from pyradar.core.sar import save_image

from pyradar.core.equalizers import equalization_using_histogram


IMAGE_PATH = "./img_sar/DAT_01.001"
IMG_DEST_DIR = "."

# create dataset
dataset = create_dataset_from_path(IMAGE_PATH)
# get band from dataset
band = get_band_from_dataset(dataset)
# get geo info from dataset
geoinfo = get_geoinfo(dataset, cast_to_int=True)

#usually both values are zero
xoff = geoinfo['xoff']
yoff = geoinfo['yoff']

# window size in coord x
win_xsize = 128
# window size in coord y
win_ysize = 128

image = read_image_from_band(band, xoff, yoff, win_xsize, win_ysize)

#equalize img to 0:255
image_eq = equalization_using_histogram(image)
# save img in current directory
save_image(IMG_DEST_DIR, "image_sar", image_eq)
		# pixel x can be stored in (i,j) defined as:
		# i = x/75 and j = x%75
		if pixel < minVal:
			minVal=pixel
		Matrix[1][int(x/75)][x%75]=val
		Matrix[2][int(x/75)][x%75]/=val
		x+=1
	#print(x)
	
	ImageArray.append(Matrix)
	count+=1
print(len(ImageArray))
print(ImageArray[0][0][0][2])
print(minVal)
c=1
'''
#img = Image.fromarray(np.uint8(ImageArray[0]))
#img.save('myimg.jpeg')
image = ImageArray[0][1]
# get actual range
input_range = image.min(), image.max()
# set new range
output_range = 0, 255
# equalize image
image_eq = naive_equalize_image(image, input_range, output_range)
# save image in current directory
save_image(".", "image_sar", image_eq)
'''

for image in ImageArray:
	# Creates PIL image