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image_manipulation.py
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image_manipulation.py
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"""
This file is part of Fish Tracker.
Copyright 2021, VTT Technical research centre of Finland Ltd.
Developed by: Mikael Uimonen.
Fish Tracker 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.
Fish Tracker 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 Fish Tracker. If not, see <https://www.gnu.org/licenses/>.
"""
import cv2
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
class ImageManipulation:
@staticmethod
def CLAHE(img):
#-----Converting image to LAB Color model-----------------------------------
lab= cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
#-----Splitting the LAB image to different channels-------------------------
l, a, b = cv2.split(lab)
#-----Applying CLAHE to L-channel-------------------------------------------
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
cl = clahe.apply(l)
#-----Merge the CLAHE enhanced L-channel with the a and b channel-----------
limg = cv2.merge((cl,a,b))
#-----Converting image from LAB Color model to RGB model--------------------
final = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
return final
@staticmethod
def distanceCompensation(img):
avg = np.mean(img, axis=1).astype(np.single)
x = np.arange(len(avg))
z = np.polyfit(x, avg, 2)
p = np.poly1d(z)
col = np.expand_dims(p(x)**-1, axis=1)
m = np.tile(col, (1, img.shape[1]))
img2 = np.multiply(img, m)
min_value = np.amin(img2)
max_value = np.amax(img2)
img2 = (255 * (img2 - min_value) / float(max_value - min_value)).astype(np.uint8)
return img2
@staticmethod
def adjustGamma(image, gamma):
invGamma = 1.0 / gamma
table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8")
return cv2.LUT(image, table)
class ImageProcessor:
def __init__(self):
self.use_any = True
self.use_clahe = False
self.use_colormap = True
self.additional = []
self.gamma = 1
self.fig = plt.figure()
x1 = np.linspace(0, 47, 48)
y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
self.line, = plt.plot(x1, y1, 'ko-')
def processImage(self, ind, image):
if not self.use_any:
return cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
if self.use_clahe:
image = ImageManipulation.CLAHE(image)
if self.gamma != 1:
image = ImageManipulation.adjustGamma(image, self.gamma)
if self.use_colormap:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = cv2.applyColorMap(image, cv2.COLORMAP_OCEAN)
if len(self.additional) > 0:
for f in self.additional:
image = f((ind, image))
return image
def processGrayscaleImage(self, image):
return cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
def distancePlot(self, image):
print(image.shape)
avg = np.mean(image, axis=1)
self.line.set_ydata(avg)
self.fig.canvas.draw()
img = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8,
sep='')
img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
img = cv2.cvtColor(img,cv2.COLOR_RGB2BGR)
cv2.imshow("plot",img)
def setAny(self):
self.use_any = self.use_clahe or self.use_colormap or self.gamma != 1 or len(self.additional) > 0
def addAdditional(self, f):
if not f in self.additional:
self.additional.append(f)
self.setAny()
def removeAdditional(self, f):
if f in self.additional:
self.additional.remove(f)
self.setAny()
def setAutomaticContrast(self, value):
self.use_clahe = value
self.setAny()
def setColorMap(self, value):
self.use_colormap = value
self.setAny()
def setGamma(self, value):
self.gamma = value
self.setAny()