| import numpy as np |
| from scipy import signal |
| import sys |
| import cv2 |
| from pyVHR.utils.HDI import hdi, hdi2 |
|
|
| class SkinDetect(): |
|
|
| def __init__(self, strength=0.2): |
| self.description = 'Skin Detection Module' |
| self.strength = strength |
| self.stats_computed = False |
|
|
| def compute_stats(self, face): |
|
|
| assert (self.strength > 0 and self.strength < 1), "'strength' parameter must have values in [0,1]" |
|
|
| faceColor = cv2.cvtColor(face, cv2.COLOR_RGB2HSV) |
| h = faceColor[:,:,0].reshape(-1,1) |
| s = faceColor[:,:,1].reshape(-1,1) |
| v = faceColor[:,:,2].reshape(-1,1) |
| |
| alpha = self.strength |
|
|
| hpd_h, x_h, y_h, modes_h = hdi2(np.squeeze(h), alpha=alpha) |
| min_s, max_s = hdi(np.squeeze(s), alpha=alpha) |
| min_v, max_v = hdi(np.squeeze(v), alpha=alpha) |
|
|
| if len(hpd_h) > 1: |
|
|
| self.multiple_modes = True |
|
|
| if len(hpd_h) > 2: |
| print('WARNING!! Found more than 2 HDIs in Hue Channel empirical Distribution... Considering only 2') |
| from scipy.spatial.distance import pdist, squareform |
| m = np.array(modes_h).reshape(-1,1) |
| d = squareform(pdist(m)) |
| maxij = np.where(d==d.max())[0] |
| i = maxij[0] |
| j = maxij[1] |
| else: |
| i = 0 |
| j = 1 |
|
|
| min_h1 = hpd_h[i][0] |
| max_h1 = hpd_h[i][1] |
| min_h2 = hpd_h[j][0] |
| max_h2 = hpd_h[j][1] |
| |
| self.lower1 = np.array([min_h1, min_s, min_v], dtype = "uint8") |
| self.upper1 = np.array([max_h1, max_s, max_v], dtype = "uint8") |
| self.lower2 = np.array([min_h2, min_s, min_v], dtype = "uint8") |
| self.upper2 = np.array([max_h2, max_s, max_v], dtype = "uint8") |
|
|
| elif len(hpd_h) == 1: |
|
|
| self.multiple_modes = False |
| |
| min_h = hpd_h[0][0] |
| max_h = hpd_h[0][1] |
| |
| self.lower = np.array([min_h, min_s, min_v], dtype = "uint8") |
| self.upper = np.array([max_h, max_s, max_v], dtype = "uint8") |
|
|
| self.stats_computed = True |
|
|
|
|
| def get_skin(self, face, filt_kern_size=7, verbose=False, plot=False): |
|
|
| if not self.stats_computed: |
| raise ValueError("ERROR! You must compute stats at least one time") |
|
|
| faceColor = cv2.cvtColor(face, cv2.COLOR_RGB2HSV) |
|
|
| if self.multiple_modes: |
| if verbose: |
| print('\nLower1: ' + str(self.lower1)) |
| print('Upper1: ' + str(self.upper1)) |
| print('\nLower2: ' + str(self.lower2)) |
| print('Upper2: ' + str(self.upper2) + '\n') |
|
|
| skinMask1 = cv2.inRange(faceColor, self.lower1, self.upper1) |
| skinMask2 = cv2.inRange(faceColor, self.lower2, self.upper2) |
| skinMask = np.logical_or(skinMask1, skinMask2).astype(np.uint8)*255 |
|
|
| else: |
|
|
| if verbose: |
| print('\nLower: ' + str(lower)) |
| print('Upper: ' + str(upper) + '\n') |
|
|
| skinMask = cv2.inRange(faceColor, self.lower, self.upper) |
|
|
| if filt_kern_size > 0: |
| skinMask = signal.medfilt2d(skinMask, kernel_size=filt_kern_size) |
| skinFace = cv2.bitwise_and(face, face, mask=skinMask) |
|
|
| if plot: |
| |
| h = faceColor[:,:,0].reshape(-1,1) |
| s = faceColor[:,:,1].reshape(-1,1) |
| v = faceColor[:,:,2].reshape(-1,1) |
|
|
| import matplotlib.pyplot as plt |
| plt.figure() |
| plt.subplot(2,2,1) |
| plt.hist(h, 20) |
| plt.title('Hue') |
| plt.subplot(2,2,2) |
| plt.hist(s, 20) |
| plt.title('Saturation') |
| plt.subplot(2,2,3) |
| plt.hist(v, 20) |
| plt.title('Value') |
| plt.subplot(2,2,4) |
| plt.imshow(skinFace) |
| plt.title('Masked Face') |
| plt.show() |
|
|
| return skinFace |
|
|
