# coding: utf8
import numpy as np
def rgb_to_hsv(arr):
    """
    convert float rgb values (in the range [0, 1]), in a numpy array to hsv
    values.
    Parameters
    ----------
    arr : (..., 3) array-like
       All values must be in the range [0, 1]
    Returns
    -------
    hsv : (..., 3) ndarray
       Colors converted to hsv values in range [0, 1]
    """
    # make sure it is an ndarray
    arr = np.asarray(arr)
    # check length of the last dimension, should be _some_ sort of rgb
    if arr.shape[-1] != 3:
        raise ValueError("Last dimension of input array must be 3; "
                         "shape {shp} was found.".format(shp=arr.shape))
    in_ndim = arr.ndim
    if arr.ndim == 1:
        arr = np.array(arr, ndmin=2)
    # make sure we don\'t have an int image
    if arr.dtype.kind in (\'iu\'):
        arr = arr.astype(np.float32)
    out = np.zeros_like(arr)
    arr_max = arr.max(-1)
    ipos = arr_max > 0
    delta = arr.ptp(-1)
    s = np.zeros_like(delta)
    s[ipos] = delta[ipos] / arr_max[ipos]
    ipos = delta > 0
    # red is max
    idx = (arr[..., 0] == arr_max) & ipos
    out[idx, 0] = (arr[idx, 1] - arr[idx, 2]) / delta[idx]
    # green is max
    idx = (arr[..., 1] == arr_max) & ipos
    out[idx, 0] = 2. + (arr[idx, 2] - arr[idx, 0]) / delta[idx]
    # blue is max
    idx = (arr[..., 2] == arr_max) & ipos
    out[idx, 0] = 4. + (arr[idx, 0] - arr[idx, 1]) / delta[idx]
    out[..., 0] = (out[..., 0] / 6.0) % 1.0
    out[..., 1] = s
    out[..., 2] = arr_max
    if in_ndim == 1:
        out.shape = (3,)
    return out
def hsv_to_rgb(hsv):
    """
    convert hsv values in a numpy array to rgb values
    all values assumed to be in range [0, 1]
    Parameters
    ----------
    hsv : (..., 3) array-like
       All values assumed to be in range [0, 1]
    Returns
    -------
    rgb : (..., 3) ndarray
       Colors converted to RGB values in range [0, 1]
    """
    hsv = np.asarray(hsv)
    # check length of the last dimension, should be _some_ sort of rgb
    if hsv.shape[-1] != 3:
        raise ValueError("Last dimension of input array must be 3; "
                         "shape {shp} was found.".format(shp=hsv.shape))
    # if we got pased a 1D array, try to treat as
    # a single color and reshape as needed
    in_ndim = hsv.ndim
    if in_ndim == 1:
        hsv = np.array(hsv, ndmin=2)
    # make sure we don\'t have an int image
    if hsv.dtype.kind in (\'iu\'):
        hsv = hsv.astype(np.float32)
    h = hsv[..., 0]
    s = hsv[..., 1]
    v = hsv[..., 2]
    r = np.empty_like(h)
    g = np.empty_like(h)
    b = np.empty_like(h)
    i = (h * 6.0).astype(np.int)
    f = (h * 6.0) - i
    p = v * (1.0 - s)
    q = v * (1.0 - s * f)
    t = v * (1.0 - s * (1.0 - f))
    idx = i % 6 == 0
    r[idx] = v[idx]
    g[idx] = t[idx]
    b[idx] = p[idx]
    idx = i == 1
    r[idx] = q[idx]
    g[idx] = v[idx]
    b[idx] = p[idx]
    idx = i == 2
    r[idx] = p[idx]
    g[idx] = v[idx]
    b[idx] = t[idx]
    idx = i == 3
    r[idx] = p[idx]
    g[idx] = q[idx]
    b[idx] = v[idx]
    idx = i == 4
    r[idx] = t[idx]
    g[idx] = p[idx]
    b[idx] = v[idx]
    idx = i == 5
    r[idx] = v[idx]
    g[idx] = p[idx]
    b[idx] = q[idx]
    idx = s == 0
    r[idx] = v[idx]
    g[idx] = v[idx]
    b[idx] = v[idx]
    rgb = np.empty_like(hsv)
    rgb[..., 0] = r
    rgb[..., 1] = g
    rgb[..., 2] = b
    if in_ndim == 1:
        rgb.shape = (3, )
    return rgb
def hsv2rgb(h, s, v):
    h60 = int(h * 6.0)
    hi = h60 % 6
    f = h *6.0 - h60
    p = v * (1.0 - s)
    q = v * (1.0 - f * s)
    t = v * (1.0 - (1.0 - f) * s)
    if hi == 0:
        return v, t, p
    elif hi == 1:
        return q, v, p
    elif hi == 2:
        return p, v, t
    elif hi == 3:
        return p, q, v
    elif hi == 4:
        return t, p, v
    else:  #  hi == 5:
        return v, p, q
def rgb2hsv(r, g, b):
    mx = np.max([r, g, b])
    mn = np.min([r, g, b])
    df = mx-mn
    if mx == mn:
        h = 0.0
    elif mx == r:
        h = (((g-b)/df))
    elif mx == g:
        h = (((b-r)/df) + 2.0)
    else:  # mx == b
        h = (((r-g)/df) + 4.0)
    if mx == 0:
        s = 0.0
    else:
        s = df/mx
    h = h/6.0%1.0
    v = mx
    return h, s, v
if __name__ == "__main__":
    for r,g,b in np.random.rand(10,3):
        h,s,v= rgb_to_hsv((r,g,b))
        h1,s1,v1=rgb2hsv(r, g, b)
        r,g,b = hsv_to_rgb((h,s,v))
        r1,g1,b1= hsv2rgb(h,s,v)
        print [h,s,v,r,g,b]==[h1,s1,v1,r1,g1,b1]
    """
    True
    True
    True
    True
    True
    True
    True
    True
    True
    True
    """