def adaptive_histogram_equalize(src):
is_color = face_detect.is_color(src)
if is_color:
ycrcb = cv2.cvtColor(src, cv2.COLOR_BGR2YCR_CB)
y, cr, cb = cv2.split(ycrcb)
else:
y = src
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(4, 4))
dst = clahe.apply(y)
# dst = cv2.equalizeHist(y)
if is_color:
ycrcb = cv2.merge((dst, cr, cb))
dst = cv2.cvtColor(ycrcb, cv2.COLOR_YCR_CB2BGR)
return dst
def adaptive_histogram_equalize(src):
is_color = face_detect.is_color(src)
if is_color:
ycrcb = cv2.cvtColor(src, cv2.COLOR_BGR2YCR_CB)
y, cr, cb = cv2.split(ycrcb)
else:
y = src
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(4, 4))
dst = clahe.apply(y)
# dst = cv2.equalizeHist(y)
if is_color:
ycrcb = cv2.merge((dst, cr, cb))
dst = cv2.cvtColor(ycrcb, cv2.COLOR_YCR_CB2BGR)
return dst
def auto_crop_hsv(src, crop_white=False):
try:
if face_detect.is_color(src):
hsv = cv2.cvtColor(src, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
else:
v = src
if crop_white:
v = abs(255-v) # invert light to dark
threshval, thresh = cv2.threshold(v, 20, 255, cv2.THRESH_BINARY) # simple global thresh to get anything "blackish"
projx = [sum(col) for col in thresh.transpose()]
projy = [sum(row) for row in thresh]
nonzero = lambda value: value > 0
x1 = first_index_of(projx, nonzero)
x2 = last_index_of(projx, nonzero)
y1 = first_index_of(projy, nonzero)
y2 = last_index_of(projy, nonzero)
cropped = src[y1:y2+1, x1:x2+1]
return cropped
except:
return src
def auto_crop_hsv(src, crop_white=False):
try:
if face_detect.is_color(src):
hsv = cv2.cvtColor(src, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
else:
v = src
if crop_white:
v = abs(255 - v) # invert light to dark
threshval, thresh = cv2.threshold(
v, 20, 255, cv2.THRESH_BINARY
) # simple global thresh to get anything "blackish"
projx = [sum(col) for col in thresh.transpose()]
projy = [sum(row) for row in thresh]
nonzero = lambda value: value > 0
x1 = first_index_of(projx, nonzero)
x2 = last_index_of(projx, nonzero)
y1 = first_index_of(projy, nonzero)
y2 = last_index_of(projy, nonzero)
cropped = src[y1:y2 + 1, x1:x2 + 1]
return cropped
except:
return src
def lightness_score(im):
if face_detect.is_color(im):
hls = cv2.cvtColor(im, cv2.cv.CV_BGR2HLS)
return hls[:, :, 1].mean() / 255.0
return im.mean() / 255.0
def lightness_score(im):
if face_detect.is_color(im):
hls = cv2.cvtColor(im, cv2.cv.CV_BGR2HLS)
return hls[:, :, 1].mean()/255.0
return im.mean()/255.0
