Python skimage.color 模块，hsv2rgb() 实例源码

def create_image(model, x, y, r, z):
    '''
    create an image for the given latent vector z 
    '''
    # create input vector
    Z = np.repeat(z, x.shape[0]).reshape((-1,x.shape[0]))
    X = np.concatenate([x, y, r, Z.T], axis=1)

    pred = model.predict(X)

    img = []
    for k in range(pred.shape[1]):
        yp = pred[:, k]
#        if k == pred.shape[1]-1:
#            yp = np.sin(yp)
        yp = (yp - yp.min()) / (yp.max()-yp.min())
        img.append(yp.reshape(y_dim, x_dim))

    img = np.dstack(img)
    if img.shape[-1] == 3:
        from skimage.color import hsv2rgb
        img = hsv2rgb(img)

    return (img*255).astype(np.uint8)

def from_hsv(picture):
        return color.hsv2rgb(picture)

def HSVAtoRGBA(image):
    # print("image:", image)
    hsv = image[:,:,:3]
    # print("hsv:", hsv)
    a = image[:,:,3:]
    hsv = hsv.clip(0,1)
    # print("hsvclip:", hsv)
    a = a.clip(0,1)
    # print("aclip:", hsv)
    rgb = color.hsv2rgb(hsv)
    a = a
    rgba = np.concatenate((rgb,a),axis=2) * 255.0
    # print("rgba:", hsv)
    rgba = rgba.clip(0,255).astype(np.uint8)
    return rgba

def add_hsv_noise(rgb, hue_offset, saturation_offset, value_offset, proba=0.5):
        mask = np.all(rgb != 0, axis=2)
        hsv = rgb2hsv(rgb)
        if random.uniform(0, 1) > proba:
            hsv[:, :, 0] = (hsv[:, :, 0] + random.uniform(-hue_offset, hue_offset)) % 1
        if random.uniform(0, 1) > proba-0.1:
            hsv[:, :, 1] = (hsv[:, :, 1] + random.uniform(-saturation_offset, saturation_offset)) % 1
        if random.uniform(0, 1) > proba-0.1:
            hsv[:, :, 2] = (hsv[:, :, 2] + random.uniform(-value_offset, value_offset)) % 1
        rgb = hsv2rgb(hsv) * 255
        return rgb.astype(np.uint8) * mask[:, :, np.newaxis]

def trans(self, img1, img2, img3):
        rst = np.array((img1.T, img2.T, img3.T), dtype=np.float64)
        rst /= 255.0
        rst = color.hsv2rgb(rst.T)
        rst *= 255
        return rst.astype(np.uint8)
# ============= RGB - CIE ============

def get_overlayed_image(x, c, gray_factor_bg = 0.3):    
    '''
    For an image x and a relevance vector c, overlay the image with the 
    relevance vector to visualise the influence of the image pixels.
    '''
    imDim = x.shape[0]

    if np.ndim(c)==1:
        c = c.reshape((imDim,imDim))
    if np.ndim(x)==2: # this happens with the MNIST Data
        x = 1-np.dstack((x, x, x))*gray_factor_bg # make it a bit grayish
    if np.ndim(x)==3: # this is what happens with cifar data        
        x = color.rgb2gray(x)
        x = 1-(1-x)*0.5
        x = np.dstack((x,x,x))

    alpha = 0.8

    # Construct a colour image to superimpose
    im = plt.imshow(c, cmap = cm.seismic, vmin=-np.max(np.abs(c)), vmax=np.max(np.abs(c)), interpolation='nearest')
    color_mask = im.to_rgba(c)[:,:,[0,1,2]]

    # Convert the input image and color mask to Hue Saturation Value (HSV) colorspace
    img_hsv = color.rgb2hsv(x)
    color_mask_hsv = color.rgb2hsv(color_mask)

    # Replace the hue and saturation of the original image
    # with that of the color mask
    img_hsv[..., 0] = color_mask_hsv[..., 0]
    img_hsv[..., 1] = color_mask_hsv[..., 1] * alpha

    img_masked = color.hsv2rgb(img_hsv)

    return img_masked

def TF_shift_hue(x, shift=0.0):
    assert len(x.shape) == 3
    h, w, nc = x.shape

    hsv = rgb2hsv(x)
    hsv[:,:,0] += shift
    return hsv2rgb(hsv)

def _change_brightness(self, X):
        X = color.rgb2hsv(X.transpose(1, 2, 0)/255.0)
        diff = np.random.uniform(-1 * self._brightness, self._brightness)
        X[:,:,2] = np.clip((X[:,:,2] + diff), 0.0, 1.0)
        X = color.hsv2rgb(X).transpose(2, 0, 1) * 255.0

        return X.astype(np.int32)