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removing trailing spaces

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iperov 2019-03-19 23:53:27 +04:00
parent fa4e579b95
commit a3df04999c
61 changed files with 2110 additions and 2103 deletions
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153
utils/image_utils.py
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@ -21,7 +21,7 @@ def reinhard_color_transfer(target, source, clip=False, preserve_paper=False, so
OpenCV image in BGR color space (the source image)
target: NumPy array
OpenCV image in BGR color space (the target image)
clip: Should components of L*a*b* image be scaled by np.clip before
clip: Should components of L*a*b* image be scaled by np.clip before
converting back to BGR color space?
If False then components will be min-max scaled appropriately.
Clipping will keep target image brightness truer to the input.
@ -32,7 +32,7 @@ def reinhard_color_transfer(target, source, clip=False, preserve_paper=False, so
aesthetically pleasing results.
If False then L*a*b* components will scaled using the reciprocal of
the scaling factor proposed in the paper. This method seems to produce
more consistently aesthetically pleasing results
more consistently aesthetically pleasing results
Returns:
-------
@ -40,13 +40,13 @@ def reinhard_color_transfer(target, source, clip=False, preserve_paper=False, so
OpenCV image (w, h, 3) NumPy array (uint8)
"""
# convert the images from the RGB to L*ab* color space, being
# sure to utilizing the floating point data type (note: OpenCV
# expects floats to be 32-bit, so use that instead of 64-bit)
source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype(np.float32)
target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype(np.float32)
target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype(np.float32)
# compute color statistics for the source and target images
src_input = source if source_mask is None else source*source_mask
tgt_input = target if target_mask is None else target*target_mask
@ -86,7 +86,7 @@ def reinhard_color_transfer(target, source, clip=False, preserve_paper=False, so
# type
transfer = cv2.merge([l, a, b])
transfer = cv2.cvtColor(transfer.astype(np.uint8), cv2.COLOR_LAB2BGR)
# return the color transferred image
return transfer
@ -127,7 +127,7 @@ def linear_color_transfer(target_img, source_img, mode='pca', eps=1e-5):
matched_img[matched_img>1] = 1
matched_img[matched_img<0] = 0
return matched_img
def lab_image_stats(image):
# compute the mean and standard deviation of each channel
(l, a, b) = cv2.split(image)
@ -137,7 +137,7 @@ def lab_image_stats(image):
# return the color statistics
return (lMean, lStd, aMean, aStd, bMean, bStd)
def _scale_array(arr, clip=True):
if clip:
return np.clip(arr, 0, 255)
@ -145,12 +145,12 @@ def _scale_array(arr, clip=True):
mn = arr.min()
mx = arr.max()
scale_range = (max([mn, 0]), min([mx, 255]))
if mn < scale_range[0] or mx > scale_range[1]:
return (scale_range[1] - scale_range[0]) * (arr - mn) / (mx - mn) + scale_range[0]
return arr
def channel_hist_match(source, template, hist_match_threshold=255, mask=None):
# Code borrowed from:
# https://stackoverflow.com/questions/32655686/histogram-matching-of-two-images-in-python-2-x
@ -179,22 +179,22 @@ def channel_hist_match(source, template, hist_match_threshold=255, mask=None):
t_quantiles = 255 * t_quantiles / t_quantiles[-1]
interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)
return interp_t_values[bin_idx].reshape(oldshape)
return interp_t_values[bin_idx].reshape(oldshape)
def color_hist_match(src_im, tar_im, hist_match_threshold=255):
h,w,c = src_im.shape
matched_R = channel_hist_match(src_im[:,:,0], tar_im[:,:,0], hist_match_threshold, None)
matched_G = channel_hist_match(src_im[:,:,1], tar_im[:,:,1], hist_match_threshold, None)
matched_B = channel_hist_match(src_im[:,:,2], tar_im[:,:,2], hist_match_threshold, None)
to_stack = (matched_R, matched_G, matched_B)
for i in range(3, c):
to_stack += ( src_im[:,:,i],)
matched = np.stack(to_stack, axis=-1).astype(src_im.dtype)
return matched
pil_fonts = {}
def _get_pil_font (font, size):
@ -204,65 +204,65 @@ def _get_pil_font (font, size):
if font_str_id not in pil_fonts.keys():
pil_fonts[font_str_id] = ImageFont.truetype(font + ".ttf", size=size, encoding="unic")
pil_font = pil_fonts[font_str_id]
return pil_font
return pil_font
except:
return ImageFont.load_default()
def get_text_image( shape, text, color=(1,1,1), border=0.2, font=None):
try:
try:
size = shape[1]
pil_font = _get_pil_font( localization.get_default_ttf_font_name() , size)
text_width, text_height = pil_font.getsize(text)
canvas = Image.new('RGB', shape[0:2], (0,0,0) )
draw = ImageDraw.Draw(canvas)
offset = ( 0, 0)
draw.text(offset, text, font=pil_font, fill=tuple((np.array(color)*255).astype(np.int)) )
result = np.asarray(canvas) / 255
if shape[2] != 3:
if shape[2] != 3:
result = np.concatenate ( (result, np.ones ( (shape[1],) + (shape[0],) + (shape[2]-3,)) ), axis=2 )
return result
except:
except:
return np.zeros ( (shape[1], shape[0], shape[2]), dtype=np.float32 )
def draw_text( image, rect, text, color=(1,1,1), border=0.2, font=None):
h,w,c = image.shape
l,t,r,b = rect
l = np.clip (l, 0, w-1)
r = np.clip (r, 0, w-1)
t = np.clip (t, 0, h-1)
b = np.clip (b, 0, h-1)
image[t:b, l:r] += get_text_image ( (r-l,b-t,c) , text, color, border, font )
def draw_text_lines (image, rect, text_lines, color=(1,1,1), border=0.2, font=None):
text_lines_len = len(text_lines)
if text_lines_len == 0:
return
l,t,r,b = rect
h = b-t
h_per_line = h // text_lines_len
for i in range(0, text_lines_len):
draw_text (image, (l, i*h_per_line, r, (i+1)*h_per_line), text_lines[i], color, border, font)
def get_draw_text_lines ( image, rect, text_lines, color=(1,1,1), border=0.2, font=None):
image = np.zeros ( image.shape, dtype=np.float )
draw_text_lines ( image, rect, text_lines, color, border, font)
return image
def draw_polygon (image, points, color, thickness = 1):
points_len = len(points)
for i in range (0, points_len):
p0 = tuple( points[i] )
p1 = tuple( points[ (i+1) % points_len] )
cv2.line (image, p0, p1, color, thickness=thickness)
def draw_rect(image, rect, color, thickness=1):
l,t,r,b = rect
draw_polygon (image, [ (l,t), (r,t), (r,b), (l,b ) ], color, thickness)
@ -272,40 +272,40 @@ def rectContains(rect, point) :
def applyAffineTransform(src, srcTri, dstTri, size) :
warpMat = cv2.getAffineTransform( np.float32(srcTri), np.float32(dstTri) )
return cv2.warpAffine( src, warpMat, (size[0], size[1]), None, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101 )
def morphTriangle(dst_img, src_img, st, dt) :
return cv2.warpAffine( src, warpMat, (size[0], size[1]), None, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101 )
def morphTriangle(dst_img, src_img, st, dt) :
(h,w,c) = dst_img.shape
sr = np.array( cv2.boundingRect(np.float32(st)) )
dr = np.array( cv2.boundingRect(np.float32(dt)) )
sRect = st - sr[0:2]
dRect = dt - dr[0:2]
d_mask = np.zeros((dr[3], dr[2], c), dtype = np.float32)
cv2.fillConvexPoly(d_mask, np.int32(dRect), (1.0,)*c, 8, 0);
imgRect = src_img[sr[1]:sr[1] + sr[3], sr[0]:sr[0] + sr[2]]
size = (dr[2], dr[3])
warpImage1 = applyAffineTransform(imgRect, sRect, dRect, size)
cv2.fillConvexPoly(d_mask, np.int32(dRect), (1.0,)*c, 8, 0);
imgRect = src_img[sr[1]:sr[1] + sr[3], sr[0]:sr[0] + sr[2]]
size = (dr[2], dr[3])
warpImage1 = applyAffineTransform(imgRect, sRect, dRect, size)
if c == 1:
warpImage1 = np.expand_dims( warpImage1, -1 )
dst_img[dr[1]:dr[1]+dr[3], dr[0]:dr[0]+dr[2]] = dst_img[dr[1]:dr[1]+dr[3], dr[0]:dr[0]+dr[2]]*(1-d_mask) + warpImage1 * d_mask
def morph_by_points (image, sp, dp):
if sp.shape != dp.shape:
raise ValueError ('morph_by_points() sp.shape != dp.shape')
(h,w,c) = image.shape
(h,w,c) = image.shape
result_image = np.zeros(image.shape, dtype = image.dtype)
for tri in Delaunay(dp).simplices:
for tri in Delaunay(dp).simplices:
morphTriangle(result_image, image, sp[tri], dp[tri])
return result_image
def equalize_and_stack_square (images, axis=1):
max_c = max ([ 1 if len(image.shape) == 2 else image.shape[2] for image in images ] )
target_wh = 99999
for i,image in enumerate(images):
if len(image.shape) == 2:
@ -313,113 +313,112 @@ def equalize_and_stack_square (images, axis=1):
c = 1
else:
h,w,c = image.shape
if h < target_wh:
target_wh = h
if w < target_wh:
target_wh = w
for i,image in enumerate(images):
if len(image.shape) == 2:
h,w = image.shape
c = 1
else:
h,w,c = image.shape
if c < max_c:
if c == 1:
if len(image.shape) == 2:
image = np.expand_dims ( image, -1 )
image = np.expand_dims ( image, -1 )
image = np.concatenate ( (image,)*max_c, -1 )
elif c == 2: #GA
image = np.expand_dims ( image[...,0], -1 )
image = np.concatenate ( (image,)*max_c, -1 )
image = np.concatenate ( (image,)*max_c, -1 )
else:
image = np.concatenate ( (image, np.ones((h,w,max_c - c))), -1 )
if h != target_wh or w != target_wh:
image = cv2.resize ( image, (target_wh, target_wh) )
h,w,c = image.shape
images[i] = image
return np.concatenate ( images, axis = 1 )
def bgr2hsv (img):
def bgr2hsv (img):
return cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
def hsv2bgr (img):
def hsv2bgr (img):
return cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
def bgra2hsva (img):
def bgra2hsva (img):
return np.concatenate ( (cv2.cvtColor(img[...,0:3], cv2.COLOR_BGR2HSV ), np.expand_dims (img[...,3], -1)), -1 )
def bgra2hsva_list (imgs):
return [ bgra2hsva(img) for img in imgs ]
def hsva2bgra (img):
return np.concatenate ( (cv2.cvtColor(img[...,0:3], cv2.COLOR_HSV2BGR ), np.expand_dims (img[...,3], -1)), -1 )
def hsva2bgra_list (imgs):
return [ hsva2bgra(img) for img in imgs ]
def gen_warp_params (source, flip, rotation_range=[-10,10], scale_range=[-0.5, 0.5], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05] ):
h,w,c = source.shape
if (h != w) or (w != 64 and w != 128 and w != 256 and w != 512 and w != 1024):
raise ValueError ('TrainingDataGenerator accepts only square power of 2 images.')
rotation = np.random.uniform( rotation_range[0], rotation_range[1] )
scale = np.random.uniform(1 +scale_range[0], 1 +scale_range[1])
tx = np.random.uniform( tx_range[0], tx_range[1] )
ty = np.random.uniform( ty_range[0], ty_range[1] )
ty = np.random.uniform( ty_range[0], ty_range[1] )
#random warp by grid
cell_size = [ w // (2**i) for i in range(1,4) ] [ np.random.randint(3) ]
cell_count = w // cell_size + 1
grid_points = np.linspace( 0, w, cell_count)
mapx = np.broadcast_to(grid_points, (cell_count, cell_count)).copy()
mapy = mapx.T
mapx[1:-1,1:-1] = mapx[1:-1,1:-1] + random_utils.random_normal( size=(cell_count-2, cell_count-2) )*(cell_size*0.24)
mapy[1:-1,1:-1] = mapy[1:-1,1:-1] + random_utils.random_normal( size=(cell_count-2, cell_count-2) )*(cell_size*0.24)
half_cell_size = cell_size // 2
mapx = cv2.resize(mapx, (w+cell_size,)*2 )[half_cell_size:-half_cell_size-1,half_cell_size:-half_cell_size-1].astype(np.float32)
mapy = cv2.resize(mapy, (w+cell_size,)*2 )[half_cell_size:-half_cell_size-1,half_cell_size:-half_cell_size-1].astype(np.float32)
#random transform
random_transform_mat = cv2.getRotationMatrix2D((w // 2, w // 2), rotation, scale)
random_transform_mat[:, 2] += (tx*w, ty*w)
params = dict()
params['mapx'] = mapx
params['mapy'] = mapy
params['rmat'] = random_transform_mat
params['w'] = w
params['w'] = w
params['flip'] = flip and np.random.randint(10) < 4
return params
def warp_by_params (params, img, warp, transform, flip, is_border_replicate):
if warp:
img = cv2.remap(img, params['mapx'], params['mapy'], cv2.INTER_CUBIC )
if transform:
img = cv2.warpAffine( img, params['rmat'], (params['w'], params['w']), borderMode=(cv2.BORDER_REPLICATE if is_border_replicate else cv2.BORDER_CONSTANT), flags=cv2.INTER_CUBIC )
img = cv2.warpAffine( img, params['rmat'], (params['w'], params['w']), borderMode=(cv2.BORDER_REPLICATE if is_border_replicate else cv2.BORDER_CONSTANT), flags=cv2.INTER_CUBIC )
if flip and params['flip']:
img = img[:,::-1,:]
return img
#n_colors = [0..256]
def reduce_colors (img_bgr, n_colors):
img_rgb = (cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB) * 255.0).astype(np.uint8)
img_rgb_pil = Image.fromarray(img_rgb)
img_rgb_pil_p = img_rgb_pil.convert('P', palette=Image.ADAPTIVE, colors=n_colors)
img_rgb_p = img_rgb_pil_p.convert('RGB')
img_bgr = cv2.cvtColor( np.array(img_rgb_p, dtype=np.float32) / 255.0, cv2.COLOR_RGB2BGR )
return img_bgr