Python matplotlib.colors.rgb_to_hsv() Examples

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 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 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 _override_hsv(rgb, h=None, s=None, v=None):
    h_, s_, v_ = rgb_to_hsv(np.array([[rgb]])).flat
    h = h if h is not None else h_
    s = s if s is not None else s_
    v = v if v is not None else v_
    r, g, b = hsv_to_rgb(np.array([[[h, s, v]]])).flat
    return r, g, b


#------------------------------------------------------------------------------
# Colormap utilities
#------------------------------------------------------------------------------ 

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 imageColorHist(imgPath,imgList,nrows):
    ncols=math.ceil(len(imgList)/nrows)
    fig,axes=plt.subplots(ncols,nrows,sharex=True,sharey=True,figsize=(15,20))
    ax=axes.flatten()
    #print(ax)
    num_bins = 50  #设置直方图的bin参数，及柱数量
    totalH=np.array([])
    totalS=np.array([])
    totalV=np.array([])
    
    for i in range(len(imgList)):
    #for i in range(3):
        #print(i)
        img=os.path.join(imgPath,imgList[i])
        lum_img=mpimg.imread(img)
        lum_imgSmall=misc.imresize(lum_img, 0.3)
        lum_imgSmallHSV=mpc.rgb_to_hsv(lum_imgSmall/255) #RGB空间结构不符合人们对颜色相似性的主观判断，因此将其转换为HSV空间、更接近于人们对颜色的主观认识。色调（H），饱和度（S），明度（V）
        #print(lum_imgSmallHSV[...,0].reshape(-1))
        ax[i].hist(lum_imgSmallHSV[...,0].reshape(-1), num_bins, normed=1)  #提取H色调分量
        #print(totalH.shape,lum_imgSmallHSV[...,0].reshape(-1).shape)
        totalH=np.append(totalH,lum_imgSmallHSV[...,0].reshape(-1))
        totalS=np.append(totalS,lum_imgSmallHSV[...,1].reshape(-1))
        totalV=np.append(totalV,lum_imgSmallHSV[...,2].reshape(-1))
        ax[i].set_title(i+1)
    fig.tight_layout()
    fig.suptitle("images show",fontsize=14,fontweight='bold',y=1.02)
    
    totalStat,(totalAXH,totalAXS,totalAXV)=plt.subplots(ncols=3,figsize=(20, 6))  #建立新图表，用于显示总体HSV的直方图统计
    totalAXH.hist(totalH*360,num_bins,normed=1,facecolor='y')    
    totalAXS.hist(totalS*100,num_bins,normed=1,facecolor='k')
    totalAXV.hist(totalV*100,num_bins,normed=1,facecolor='g')
   
    plt.show() 

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 get_random_data(image, label, input_shape, jitter=.2, hue=.2, sat=1.1, val=1.1):
    h, w = input_shape

    # resize image
    rand_jit1 = rand(1-jitter,1+jitter)
    rand_jit2 = rand(1-jitter,1+jitter)
    new_ar = w/h * rand_jit1/rand_jit2
    scale = rand(.6, 1.4)
    if new_ar < 1:
        nh = int(scale*h)
        nw = int(nh*new_ar)
    else:
        nw = int(scale*w)
        nh = int(nw/new_ar)
    image = image.resize((nw,nh), Image.BICUBIC)
    label = label.resize((nw,nh), Image.BICUBIC)
    # place image
    dx = int(rand(0, w-nw))
    dy = int(rand(0, h-nh))
    new_image = Image.new('RGB', (w,h), (0,0,0))
    new_label = Image.new('RGB', (w,h), (0,0,0))
    new_image.paste(image, (dx, dy))
    new_label.paste(label, (dx, dy))
    image = new_image
    label = new_label
    # flip image or not
    flip = rand()<.5
    if flip: 
        image = image.transpose(Image.FLIP_LEFT_RIGHT)
        label = label.transpose(Image.FLIP_LEFT_RIGHT)

    # distort image
    hue = rand(-hue, hue)
    sat = rand(1, sat) if rand()<.5 else 1/rand(1, sat)
    val = rand(1, val) if rand()<.5 else 1/rand(1, val)
    x = rgb_to_hsv(np.array(image)/255.)
    x[..., 0] += hue
    x[..., 0][x[..., 0]>1] -= 1
    x[..., 0][x[..., 0]<0] += 1
    x[..., 1] *= sat
    x[..., 2] *= val
    x[x>1] = 1
    x[x<0] = 0
    image_data = hsv_to_rgb(x)
    return image_data,label 

def get_random_data(image, label, input_shape, jitter=.1, hue=.1, sat=1.1, val=1.1):

    h, w = input_shape

    # resize image
    rand_jit1 = rand(1-jitter,1+jitter)
    rand_jit2 = rand(1-jitter,1+jitter)
    new_ar = w/h * rand_jit1/rand_jit2
    scale = rand(.7, 1.3)
    if new_ar < 1:
        nh = int(scale*h)
        nw = int(nh*new_ar)
    else:
        nw = int(scale*w)
        nh = int(nw/new_ar)
    image = image.resize((nw,nh), Image.NEAREST)
    label = label.resize((nw,nh), Image.NEAREST)
    # place image
    dx = int(rand(0, w-nw))
    dy = int(rand(0, h-nh))
    new_image = Image.new('RGB', (w,h), (0,0,0))
    new_label = Image.new('RGB', (w,h), (0,0,0))
    new_image.paste(image, (dx, dy))
    new_label.paste(label, (dx, dy))
    image = new_image
    label = new_label
    # flip image or not
    flip = rand()<.5
    if flip: 
        image = image.transpose(Image.FLIP_LEFT_RIGHT)
        label = label.transpose(Image.FLIP_LEFT_RIGHT)

    # distort image
    hue = rand(-hue, hue)
    sat = rand(1, sat) if rand()<.5 else 1/rand(1, sat)
    val = rand(1, val) if rand()<.5 else 1/rand(1, val)
    x = rgb_to_hsv(np.array(image)/255.)
    x[..., 0] += hue
    x[..., 0][x[..., 0]>1] -= 1
    x[..., 0][x[..., 0]<0] += 1
    x[..., 1] *= sat
    x[..., 2] *= val
    x[x>1] = 1
    x[x<0] = 0
    image_data = hsv_to_rgb(x) 
    return image_data,label 

def get_random_data(annotation_line, input_shape, random=True, max_boxes=20, jitter=.3, hue=.1, sat=1.5, val=1.5, proc_img=True):
    '''r实时数据增强的随机预处理'''
    line = annotation_line.split()
    image = Image.open(line[0])
    iw, ih = image.size
    h, w = input_shape
    box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])

    # resize image
    new_ar = w/h * rand(1-jitter,1+jitter)/rand(1-jitter,1+jitter)
    scale = rand(.25, 2)
    if new_ar < 1:
        nh = int(scale*h)
        nw = int(nh*new_ar)
    else:
        nw = int(scale*w)
        nh = int(nw/new_ar)
    image = image.resize((nw,nh), Image.BICUBIC)

    # place image
    dx = int(rand(0, w-nw))
    dy = int(rand(0, h-nh))
    new_image = Image.new('RGB', (w,h), (128,128,128))
    new_image.paste(image, (dx, dy))
    image = new_image

    # flip image or not
    flip = rand()<.5
    if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT)

    # distort image
    hue = rand(-hue, hue)
    sat = rand(1, sat) if rand()<.5 else 1/rand(1, sat)
    val = rand(1, val) if rand()<.5 else 1/rand(1, val)
    x = rgb_to_hsv(np.array(image)/255.)
    x[..., 0] += hue
    x[..., 0][x[..., 0]>1] -= 1
    x[..., 0][x[..., 0]<0] += 1
    x[..., 1] *= sat
    x[..., 2] *= val
    x[x>1] = 1
    x[x<0] = 0
    image_data = hsv_to_rgb(x) # numpy array, 0 to 1

    # correct boxes
    box_data = np.zeros((max_boxes,5))
    if len(box)>0:
        np.random.shuffle(box)
        box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
        box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
        if flip: box[:, [0,2]] = w - box[:, [2,0]]
        box[:, 0:2][box[:, 0:2]<0] = 0
        box[:, 2][box[:, 2]>w] = w
        box[:, 3][box[:, 3]>h] = h
        box_w = box[:, 2] - box[:, 0]
        box_h = box[:, 3] - box[:, 1]
        box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box
        if len(box)>max_boxes: box = box[:max_boxes]
        box_data[:len(box)] = box

    return image_data, box_data 

def distort_color(img, fast_mode=True,
                  brightness_delta=32./255., hue_delta=0.2, saturation_range=(0.5, 1.5), contrast_range=(0.5, 1.5),
                  data_format='channels_last'):
    
    nonnormalized = (img.max() > 2.0)
    if nonnormalized:
        img = img.astype(np.float32) / 255.
    if data_format == 'channels_first':
        img = np.transpose(img, (1, 2, 0))
    if (not nonnormalized) and (data_format == 'channels_last'):
        img = img.copy()

    noop = lambda x: x
    brightness_hsv = (lambda x: random_brightness_hsv(x, max_delta=brightness_delta)) if brightness_delta > 0 else noop
    saturation = (lambda x: random_saturation(x, *saturation_range)) if (saturation_range[0] <= saturation_range[1]) and ((saturation_range[0] != 1) or (saturation_range[1] != 1)) else noop
    
    if fast_mode:

        ordering = np.random.choice(2)
        if ordering == 0:
            img = hsv_to_rgb(saturation(brightness_hsv(rgb_to_hsv(img))))
        else:
            img = hsv_to_rgb(brightness_hsv(saturation(rgb_to_hsv(img))))

    else:

        brightness = (lambda x: random_brightness(x, max_delta=brightness_delta)) if brightness_delta > 0 else noop
        hue = (lambda x: random_hue(x, max_delta=hue_delta)) if hue_delta > 0 else noop
        contrast = (lambda x: random_contrast(x, *contrast_range)) if (contrast_range[0] <= contrast_range[1]) and ((contrast_range[0] != 1) or (contrast_range[1] != 1)) else noop

        ordering = np.random.choice(4)
        if ordering == 0:
            img = contrast(hsv_to_rgb(hue(saturation(rgb_to_hsv(brightness(img))))))
        elif ordering == 1:
            img = hsv_to_rgb(hue(rgb_to_hsv(contrast(brightness(hsv_to_rgb(saturation(rgb_to_hsv(img))))))))
        elif ordering == 2:
            img = hsv_to_rgb(saturation(brightness_hsv(hue(rgb_to_hsv(contrast(img))))))
        elif ordering == 3:
            img = brightness(contrast(hsv_to_rgb(saturation(hue(rgb_to_hsv(img))))))

    if data_format == 'channels_first':
        img = np.transpose(img, (2, 0, 1))
    if nonnormalized:
        img = img * 255.
    
    return img 

def plot_colortable(colors, title, sort_colors=True, emptycols=0):

    cell_width = 212
    cell_height = 22
    swatch_width = 48
    margin = 12
    topmargin = 40

    # Sort colors by hue, saturation, value and name.
    by_hsv = ((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)
                    for name, color in colors.items())
    if sort_colors is True:
        by_hsv = sorted(by_hsv)
    names = [name for hsv, name in by_hsv]

    n = len(names)
    ncols = 4 - emptycols
    nrows = n // ncols + int(n % ncols > 0)

    width = cell_width * 4 + 2 * margin
    height = cell_height * nrows + margin + topmargin
    dpi = 72

    fig, ax = plt.subplots(figsize=(width / dpi, height / dpi), dpi=dpi)
    fig.subplots_adjust(margin/width, margin/height,
                        (width-margin)/width, (height-topmargin)/height)
    ax.set_xlim(0, cell_width * 4)
    ax.set_ylim(cell_height * (nrows-0.5), -cell_height/2.)
    ax.yaxis.set_visible(False)
    ax.xaxis.set_visible(False)
    ax.set_axis_off()
    ax.set_title(title, fontsize=24, loc="left", pad=10)

    for i, name in enumerate(names):
        row = i % nrows
        col = i // nrows
        y = row * cell_height

        swatch_start_x = cell_width * col
        swatch_end_x = cell_width * col + swatch_width
        text_pos_x = cell_width * col + swatch_width + 7

        ax.text(text_pos_x, y, name, fontsize=14,
                horizontalalignment='left',
                verticalalignment='center')

        ax.hlines(y, swatch_start_x, swatch_end_x,
                  color=colors[name], linewidth=18)

    return fig