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