Python colors.rgb_to_hsv函数代码示例

 def remove_noise_with_hsv(self, img):
     # Use number of occurrences to find the standard h, s, v
     # Convert to int so we can sort the colors
     # noinspection PyTypeChecker
     img_int = np.dot(np.rint(img * 255), np.power(256, np.arange(3)))
     color_array = sort_by_occurrence(img_int.flatten())
     # standard color is the 2nd most frequent color
     std_color = color_array[1]
     std_b, mod = divmod(std_color, 256 ** 2)
     std_g, std_r = divmod(mod, 256)
     # noinspection PyTypeChecker
     std_h, std_s, std_v = colors.rgb_to_hsv(np.array([std_r, std_g, std_b]) / 255)
     # print(std_h * 360, std_s * 100, std_v * 100)
     height, width, _ = img.shape
     img_hsv = colors.rgb_to_hsv(img)
     h, s, v = img_hsv[:, :, 0], img_hsv[:, :, 1], img_hsv[:, :, 2]
     h_mask = np.abs(h - std_h) > self.h_tolerance
     s_mask = np.abs(s - std_s) > self.s_tolerance
     delta_v = np.abs(v - std_v)
     v_mask = delta_v > self.v_tolerance
     hsv_mask = np.logical_or(np.logical_or(h_mask, s_mask), v_mask)
     new_img = 1 - delta_v
     new_img[hsv_mask] = 0
     # Three types of grayscale colors in new_img:
     # Type A: 1. Outside noise, or inside point.
     # Type B: between 0 and 1. Outside noise, or contour point.
     # Type C: 0. Inside noise, or background.
     return new_img

 def generate_color_range(self):
     # color and marker range:
     self.colorrange = []
     self.markerrange = []
     mr2 = []
     # first color range:
     cc0 = plt.cm.gist_rainbow(np.linspace(0.0, 1.0, 8.0))
     # shuffle it:
     for k in range((len(cc0) + 1) // 2):
         self.colorrange.extend(cc0[k::(len(cc0) + 1) // 2])
     self.markerrange.extend(len(cc0) * 'o')
     mr2.extend(len(cc0) * 'v')
     # second darker color range:
     cc1 = plt.cm.gist_rainbow(np.linspace(0.33 / 7.0, 1.0, 7.0))
     cc1 = mc.hsv_to_rgb(mc.rgb_to_hsv(np.array([cc1])) * np.array([1.0, 0.9, 0.7, 0.0]))[0]
     cc1[:, 3] = 1.0
     # shuffle it:
     for k in range((len(cc1) + 1) // 2):
         self.colorrange.extend(cc1[k::(len(cc1) + 1) // 2])
     self.markerrange.extend(len(cc1) * '^')
     mr2.extend(len(cc1) * '*')
     # third lighter color range:
     cc2 = plt.cm.gist_rainbow(np.linspace(0.67 / 6.0, 1.0, 6.0))
     cc2 = mc.hsv_to_rgb(mc.rgb_to_hsv(np.array([cc2])) * np.array([1.0, 0.5, 1.0, 0.0]))[0]
     cc2[:, 3] = 1.0
     # shuffle it:
     for k in range((len(cc2) + 1) // 2):
         self.colorrange.extend(cc2[k::(len(cc2) + 1) // 2])
     self.markerrange.extend(len(cc2) * 'D')
     mr2.extend(len(cc2) * 'x')
     self.markerrange.extend(mr2)

def test_rgb_hsv_round_trip():
    for a_shape in [(500, 500, 3), (500, 3), (1, 3), (3,)]:
        np.random.seed(0)
        tt = np.random.random(a_shape)
        assert_array_almost_equal(tt,
            mcolors.hsv_to_rgb(mcolors.rgb_to_hsv(tt)))
        assert_array_almost_equal(tt,
            mcolors.rgb_to_hsv(mcolors.hsv_to_rgb(tt)))

def test2(dicLoc):
    itemCollection = pickle.load(open('itemCollection',"rb"))
    bagTypes = itemCollection.getBagTypes()
    
    for image in bagTypes['silver']:
        img = scipy.misc.imread(image.getFullImageLocation())
        array=np.asarray(img)
        arr=(array.astype(float))/255.0
        img_hsv = colors.rgb_to_hsv(arr[...,:3])
        
        lu1=img_hsv[...,0].flatten()
        plt.subplot(1,3,1)
        plt.hist(lu1*360,bins=10,range=(0.0,360.0),histtype='stepfilled', color='r', label='Hue')
        plt.title("Hue")
        plt.xlabel("Value")
        plt.ylabel("Frequency")
        plt.legend()
        
        lu2=img_hsv[...,1].flatten()
        plt.subplot(1,3,2)                  
        plt.hist(lu2,bins=10,range=(0.0,1.0),histtype='stepfilled', color='g', label='Saturation')
        plt.title("Saturation")   
        plt.xlabel("Value")    
        plt.ylabel("Frequency")
        plt.legend()
        
        lu3=img_hsv[...,2].flatten()
        plt.subplot(1,3,3)                  
        plt.hist(lu3*255,bins=10,range=(0.0,255.0),histtype='stepfilled', color='b', label='Intesity')
        plt.title("Intensity")   
        plt.xlabel("Value")    
        plt.ylabel("Frequency")
        plt.legend()
        plt.show()

def get_color_range(n):
    colors = mcolors.TABLEAU_COLORS
    by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)
                    for name, color in colors.items())
    sorted_names = [name for hsv, name in by_hsv]
    delta = int(len(sorted_names)/n)
    return sorted_names[::delta]

    def abrir_imagen(self, widget):
        img = None
        fc = gtk.FileChooserDialog(title='Abrir imagen...', parent=None, action=gtk.FILE_CHOOSER_ACTION_OPEN, 
                                   buttons=(gtk.STOCK_CANCEL,gtk.RESPONSE_CANCEL,gtk.STOCK_OPEN,gtk.RESPONSE_OK))
        fc.set_default_response(gtk.RESPONSE_OK)
        respuesta = fc.run()
        if respuesta == gtk.RESPONSE_OK:
            self.nombre_archivo = fc.get_filename()
            ext = self.nombre_archivo[-4:]

            if ext == ".jpg":
                img = Image.open(self.nombre_archivo)
                self.imagen = numpy.asarray(img,dtype=numpy.float64)
                if len(self.imagen.shape) == 3: # si es a color
                    self.imagen_color = c.rgb_to_hsv(self.imagen)
                    self.imagen = self.imagen_color[:,:,2]
                    self.imagen = self.imagen.astype(numpy.uint8)
                    self.color = True
                else:
                    self.color = False
            else:
                self.imagen = self.switch_ext(ext)

            self.dibujar_img(self.imagen)
            self.historial = []
            self.historial_color = []
            self.indice_historial = -1
            self.actualiza_historial()
        fc.destroy()

def main():
    from scipy import ndimage

    from matplotlib import colors

    image = ndimage.imread('../../../heavy/raspberry.jpg')
    image = colors.rgb_to_hsv(image)

    height = image.shape[0]
    width = image.shape[1]

    hue = image[:, :, 0]
    saturation = image[:, :, 1]
    value = image[:, :, 2]

    analyzer = Analyzer(width, height)

    from PIL import Image, ImageEnhance

    output = Image.open('../../../heavy/raspberry.jpg')
    converter = ImageEnhance.Color(output)
    output = converter.enhance(0)

    prepared = analyzer.prepare(saturation, value)

    for target in HUES:
        x, y, angle, r = analyzer.analyze(hue, saturation, value, target, prepared)
        if r != 0:
            render(output, target, x, y, angle, r)

    output.save('output.png')

def sorted_color_maps():
    '''List of color name and their hex values sorted by HSV.

    This code is taken from:
        http://matplotlib.org/examples/color/named_colors.html
    '''
    colors_ = list(six.iteritems(colors.cnames))

    # Add the single letter colors.
    for name, rgb in six.iteritems(colors.ColorConverter.colors):
        hex_ = colors.rgb2hex(rgb)
        colors_.append((name, hex_))

    # Transform to hex color values.
    hex_ = [color[1] for color in colors_]
    # Get the rgb equivalent.
    rgb = [colors.hex2color(color) for color in hex_]
    # Get the hsv equivalent.
    hsv = [colors.rgb_to_hsv(color) for color in rgb]

    # Split the hsv values to sort.
    hue = [color[0] for color in hsv]
    sat = [color[1] for color in hsv]
    val = [color[2] for color in hsv]

    # Sort by hue, saturation and value.
    ind = np.lexsort((val, sat, hue))
    sorted_colors = [colors_[i] for i in ind]
    sorted_colors = [
        c_1
        for (c_1, c_2) in zip(sorted_colors[:-1], sorted_colors[1:])
        if c_1[1] != c_2[1]]
    return sorted_colors

def shadow_filter(image, dpi):
    """This filter creates a metallic look on patches.

    image : the image of the patch
    dpi   : the resultion of the patch"""
    # Get the shape of the image
    nx, ny, depth = image.shape
    # Create a mash grid
    xx, yy = np.mgrid[0:nx, 0:ny]
    # Draw a circular "shadow"
    circle = (xx + nx * 4) ** 2 + (yy + ny) ** 2
    # Normalize
    circle -= circle.min()
    circle = circle / circle.max()
    # Steepness
    value = circle.clip(0.3, 0.6) + 0.4
    saturation = 1 - circle.clip(0.7, 0.8)
    # Normalize
    saturation -= saturation.min() - 0.1
    saturation = saturation / saturation.max()
    # Convert the rgb part (without alpha) to hsv
    hsv = mc.rgb_to_hsv(image[:, :, :3])
    # Multiply the value of hsv image with the shadow
    hsv[:, :, 2] = hsv[:, :, 2] * value
    # Highlights with saturation
    hsv[:, :, 1] = hsv[:, :, 1] * saturation
    # Copy the hsv back into the image (we haven't touched alpha)
    image[:, :, :3] = mc.hsv_to_rgb(hsv)
    # the return values are: new_image, offset_x, offset_y
    return image, 0, 0

def rgb_to_greyscale(dataset_rgb):
    """
    Convert each image in the given dataset to greyscale.

    The dataset used in the model uses this specific shape:
        [channels, hight, width]
    it has to be changed as the rgb_to_hsv function needs this shape:
        [hight, width, channels]
    The new greyscale image is stored in a new array only using the last
    value of the hsv array.
    This new array has to be reshaped to meet the original criteria
    of the model.
    """
    dataset_grey = np.zeros((dataset_rgb.shape[0], 1,
                             dataset_rgb.shape[2], dataset_rgb.shape[3]))

    for i in range(len(dataset_rgb)):
        img_rgb = np.swapaxes(dataset_rgb[i], 0, 2)
        img_hsv = colors.rgb_to_hsv(img_rgb)
        img_grey = np.zeros((img_hsv.shape[0], img_hsv.shape[1]))
        for x in range(len(img_hsv)):
            for y in range(len(img_hsv[x])):
                img_grey[x][y] = img_hsv[x][y][2:]
        # plt.imshow(img_grey, cmap=cm.Greys_r)
        # plt.show()
        img_grey = img_grey.reshape(32, 32, 1)
        img_grey = np.swapaxes(img_grey, 2, 0)
        dataset_grey[i] = img_grey

    return dataset_grey

def Detect_laser(image):
    hsv= col.rgb_to_hsv((image/255.0))
    laser = np.zeros(image.shape[:2])
    for i in range(150,450):
        for j in range(260,640):
            if  hsv[i,j,1]<0.25 and hsv[i,j,2]>0.85:
                laser[i,j] = 1
    return laser

def rgb_to_hsv_tuple(rgb_tuple):
    """
    Convert 3 tuple that represents a RGB color (values between 0..1) 
    to a 3 tuple in HSV color space.
    If you have an array of color values use: ``matplotlib.colors.rgb_to_hsv``.
    """ 
    colarr = rgb_to_hsv(np.array([[rgb_tuple]]))
    return tuple(colarr[0, 0, :])

 def __init__(self, imageRGB):
     self.imageRGB = imageRGB.copy()
     self.imageHSV = colors.rgb_to_hsv(self.imageRGB)
     self.imageHeight = self.imageRGB.shape[0]
     self.imageWidth = self.imageRGB.shape[1]
     self.segmentedRGB = None
     self.segmentedHSV = None
     self.computationTime = None

def extract_hue(infile):
	"""
		Returns a hue greyscale image from an rgb image.
	"""
	rgb_image = imread(infile)
	hsv_image = rgb_to_hsv(rgb_image)
	hue_image = -hsv_image[:,:,0]
	imsave(fname='%s-hue.png' %(infile.split('.')[0]),arr=hue_image,cmap='gray')

def Detect_laser(image):
    hsv= col.rgb_to_hsv((image/255.0))
    laser = np.zeros(image.shape[:2])
    for i in range(150,450):
        for j in range(260,640):
            if  hsv[i,j,1]<0.25 and hsv[i,j,2]>0.85:
                laser[i,j] = 1
    open_laser = cv2.morphologyEx(laser, cv2.MORPH_OPEN, disk(2))
    return open_laser