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utils_img.py
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utils_img.py
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"""
* This file is part of PYSLAM
*
* Copyright (C) 2016-present Luigi Freda <luigi dot freda at gmail dot com>
*
* PYSLAM is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* PYSLAM is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with PYSLAM. If not, see <http://www.gnu.org/licenses/>.
"""
import os
import numpy as np
import cv2
import math
from utils_geom import add_ones, homography_matrix
from utils_draw import draw_random_img
from utils import Printer
# combine two images horizontally
def combine_images_horizontally(img1, img2):
if img1.ndim<=2:
img1 = cv2.cvtColor(img1,cv2.COLOR_GRAY2RGB)
if img2.ndim<=2:
img2 = cv2.cvtColor(img2,cv2.COLOR_GRAY2RGB)
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
img3 = np.zeros((max(h1, h2), w1+w2,3), np.uint8)
img3[:h1, :w1,:3] = img1
img3[:h2, w1:w1+w2,:3] = img2
return img3
# create a generator over an image to extract 'row_divs' x 'col_divs' sub-blocks
def img_blocks(img, row_divs, col_divs):
rows, cols = img.shape[:2]
#print('img.shape: ', img.shape)
xs = np.uint32(np.rint(np.linspace(0, cols, num=col_divs+1))) # num = Number of samples to generate
ys = np.uint32(np.rint(np.linspace(0, rows, num=row_divs+1)))
#print('img_blocks xs: ', xs)
#print('img_blocks ys: ', ys)
ystarts, yends = ys[:-1], ys[1:]
xstarts, xends = xs[:-1], xs[1:]
for y1, y2 in zip(ystarts, yends):
for x1, x2 in zip(xstarts, xends):
yield img[y1:y2, x1:x2], y1, x1 # return block, row, col
def mask_block(mask,x1,x2,y1,y2):
if mask is None:
return None
else:
return mask[y1:y2, x1:x2]
# create a generator over an image to extract 'row_divs' x 'col_divs' sub-blocks
def img_mask_blocks(img, mask, row_divs, col_divs):
rows, cols = img.shape[:2]
#print('img.shape: ', img.shape)
xs = np.uint32(np.rint(np.linspace(0, cols, num=col_divs+1))) # num = Number of samples to generate
ys = np.uint32(np.rint(np.linspace(0, rows, num=row_divs+1)))
#print('img_blocks xs: ', xs)
#print('img_blocks ys: ', ys)
ystarts, yends = ys[:-1], ys[1:]
xstarts, xends = xs[:-1], xs[1:]
for y1, y2 in zip(ystarts, yends):
for x1, x2 in zip(xstarts, xends):
yield img[y1:y2, x1:x2], mask_block(mask,x1,x2,y1,y2), y1, x1 # return block, row, col
# create a generator over an image to produce a pyramid of images in the scale space by using the input scale factor
# N.B: check the newer Pyramid class in pyramid.py!
def pyramid(image, scale=1.2, minSize=(30, 30), gauss_filter=True, sigma0=1.0):
level = 0
inv_scale = 1./scale
# from https://github.com/opencv/opencv/blob/173442bb2ecd527f1884d96d7327bff293f0c65a/modules/nonfree/src/sift.cpp#L212
# \sigma_{total}^2 = \sigma_{i}^2 + \sigma_{i-1}^2
sigma_nominal = 0.5 # no filtering on the original image from https://www.vlfeat.org/api/sift.html#sift-tech-ss
#sigma0 = 1.0 # N.B.: SIFT use 1.6 for this value
sigma_prev = sigma_nominal
sigma_total = math.pow(scale,level) * sigma0
print('level %d, sigma_total: %f' %(level,sigma_total))
sigma_cur = math.sqrt(sigma_total*sigma_total - sigma_prev*sigma_prev)
sigma_prev = sigma_cur
if gauss_filter:
image = cv2.GaussianBlur(image,ksize=(0,0),sigmaX=sigma_cur)
# yield the original image
yield image, level
while True:
level += 1
sigma_total = math.pow(scale,level) * sigma0
print('level %d, sigma_total: %f' %(level,sigma_total))
sigma_cur = math.sqrt(sigma_total*sigma_total - sigma_prev*sigma_prev)
sigma_prev = sigma_cur
if gauss_filter:
blur = cv2.GaussianBlur(image,ksize=(0,0),sigmaX=sigma_cur)
image = cv2.resize(blur,(0,0),fx=inv_scale,fy=inv_scale)#,interpolation = cv2.INTER_NEAREST)
else:
image = cv2.resize(image,(0,0),fx=inv_scale,fy=inv_scale)#,interpolation = cv2.INTER_NEAREST)
# if the resized image does not meet the supplied minimum
# size, then stop constructing the pyramid
if image.shape[0] < minSize[1] or image.shape[1] < minSize[0]:
break
# yield the next image in the pyramid
yield image, level
# N.B.: if you want the mask indexs, you can return mask_idxs = (mask.ravel() == 1)
def mask_from_polygon(size,pts):
pts = pts.astype(np.int32) #reshape(-1,1,2)
mask = np.zeros(size[:2],np.uint8)
mask = cv2.fillConvexPoly(mask,pts,255)
return mask
# rotate an image by adjusting the output image size in order to contain the rotated image
# angle in degrees
def rotate_img(img, center=None, angle=0, scale=1):
(h, w) = img.shape[:2]
if center is None:
center = (w / 2, h / 2)
img_box = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ])
#print('img_box:',img_box)
M = cv2.getRotationMatrix2D(center, angle, scale)
# grab sin and cos from matrix
cos = np.abs(M[0, 0])
sin = np.abs(M[0, 1])
# compute the new bounding dimensions of the image
new_w = int((w * cos) + (h * sin))
new_h = int((w * sin) + (h * cos))
# adjust the rotation matrix to take into account translation (in the new image)
M[0, 2] += (new_w / 2) - center[0]
M[1, 2] += (new_h / 2) - center[1]
rotated_img_box = (M @ add_ones(img_box).T).T
#print('rotated_img_box:',rotated_img_box)
img_out = cv2.warpAffine(img, M, (new_w, new_h))
return img_out, rotated_img_box, M
# transform an image by rotating and translating the camera (camera-x along image-x, camera-y along image-y, camera-z along the optical axis)
# the image is assumed to lie on the plane Z=1 (in front of the camera at distance d=1 along the optical axis);
# we compute the homography induced by the plane Z=1 when the camera is moved from [I|0] to [R|t] (see homography_matrix());
# adjust_frame => adjust the frame or not in order to contain the transformed image, in this case tx,ty are useless
# tx=0.5 correspond to half image width (see homography_matrix());
# angles input are in degrees
def transform_img(img,rotx,roty,rotz,tx=0,ty=0,scale=1,adjust_frame=True):
roll = rotx*math.pi/180.0
pitch = roty*math.pi/180.0
yaw = rotz*math.pi/180.0
# N.B.: in the computed homography_matrix we set d=1 (see homography_matrix())
# u=fx*X/Z => on Z=d=1 one has u=fx*X/1
# if we shift the camera of tz along Z, then one has u'=fx*X/(1-tz)
# hence we have a zoom_factor = 1/(1-tz) => tz = (zoom_factor - 1)/zoom_factor
tz = (scale - 1)/scale
(h, w) = img.shape[:2]
center = np.float32([w / 2, h / 2, 1])
img_box = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ])
#print('img_box:',img_box)
H = homography_matrix(img,roll,pitch,yaw,tx,ty,tz)
#print('H:',H)
transformed_img_box = (H @ add_ones(img_box).T)
transformed_img_box = (transformed_img_box[:2]/transformed_img_box[2]).T
transformed_center = (H @ center.T).T
#print('transformed_img_box:',transformed_img_box)
if adjust_frame:
# adjust the frame in order to contain the transformed image
min_u = math.floor(transformed_img_box[:,0].min())
max_u = math.ceil(transformed_img_box[:,0].max())
min_v = math.floor(transformed_img_box[:,1].min())
max_v = math.ceil(transformed_img_box[:,1].max())
new_w = max_u-min_u
new_h = max_v-min_v
if H[2,2] != 0:
H = H/H[2, 2]
T = np.array([[ 1, 0, -min_u],
[ 0, 1, -min_v],
[ 0, 0, 1]])
H = T @ H
transformed_img_box = (H @ add_ones(img_box).T)
transformed_img_box = (transformed_img_box[:2]/transformed_img_box[2]).T
transformed_center = (H @ center.T).T
else:
# simulate the camera pose change
new_w = w
new_h = h
img_out = cv2.warpPerspective(img, H, (new_w,new_h))
return img_out, transformed_img_box, H
# add 'disturbing' background on `img` outside the given bounding `img_box`
def add_background(img, img_box, img_background=None):
if img_background is None:
# create random image
img_background = draw_random_img(img.shape)
else:
# check if we have to resize img_background
if img_background.shape != img.shape:
#print('resizing img background')
(h, w) = img.shape[:2]
img_background = cv2.resize(img_background,(w,h))
# check if we have to convert to gray image
if img.ndim == 2:
img_background = cv2.cvtColor(img_background,cv2.COLOR_RGB2GRAY)
#print('img.shape:',img.shape,', img_background.shape:',img_background.shape)
mask = mask_from_polygon(img.shape,img_box)
inverse_mask = cv2.bitwise_not(mask)
img_background = cv2.bitwise_or(img_background, img_background, mask=inverse_mask)
# combine foreground+background
final = cv2.bitwise_or(img, img_background)
return final
def proc_clahe(img):
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
lab = cv2.cvtColor(img, cv2.COLOR_RGB2Lab)
lab[:, :, 0] = clahe.apply(lab[:, :, 0])
return cv2.cvtColor(lab, cv2.COLOR_Lab2RGB)
# create a scaled image of uint8 from a image of floats
def img_from_floats(img_flt, eps=1e-9):
assert(img_flt.dtype in [np.float32, np.float64, np.double, np.single])
img_max = np.amax(img_flt)
img_min = np.amin(img_flt)
img_range = img_max - img_min
if img_range < eps:
img_range = 1
img = ((img_flt-img_min)*255/img_range).astype(np.uint8)
return img
# remove borders from img
def remove_borders(image, borders):
shape = image.shape
new_im = np.zeros_like(image)
if len(shape) == 4:
shape = [shape[1], shape[2], shape[3]]
new_im[:, borders:shape[0]-borders, borders:shape[1]-borders, :] = image[:, borders:shape[0]-borders, borders:shape[1]-borders, :]
elif len(shape) == 3:
new_im[borders:shape[0] - borders, borders:shape[1] - borders, :] = image[borders:shape[0] - borders, borders:shape[1] - borders, :]
else:
new_im[borders:shape[0] - borders, borders:shape[1] - borders] = image[borders:shape[0] - borders, borders:shape[1] - borders]
return new_im