def preprocesing(path, down_tresh, up_thresh, left_tresh, right_tresh, gamma):
import cv2 as cv
from imadjust import imadjust
# ################################################# WCZYTANIE OBRAZU ################################################# #
img_gray = cv.imread(path, 0)
# ######################################## PRZYGOTOWANIE DO SEGMENTACJI ############################################## #
# FILTRACJA ____________________________________________________________________________________________________________
img_gray_filtr = cv.medianBlur(img_gray, 5)
# WYROWNANIE HISTOGRAMU ________________________________________________________________________________________________
clahe = cv.createCLAHE(clipLimit=1., tileGridSize=(3, 3))
img_gray_equal = clahe.apply(img_gray_filtr)
# NIELINIOWA ZMIANA JASNOSCI ___________________________________________________________________________________________
img_gamma = imadjust(img_gray_equal, down_tresh, up_thresh, 10, 215, gamma)
# ########################################## KONIEC PRZYGOTOWANIA DO SEGMENTACJI ##################################### #
return img_gamma
if eyes[b][2] > eyes[maxi][2]:
maxi = b
# recortar ojos
bbox_eye = eyes[maxi]
eye = face[bbox_eye[1]:bbox_eye[1] + bbox_eye[3],
bbox_eye[0]:bbox_eye[0] + bbox_eye[2]]
eyepic = facepic[bbox_eye[1]:bbox_eye[1] + bbox_eye[3],
bbox_eye[0]:bbox_eye[0] + bbox_eye[2]]
# correccion de campo de plano para emparejar sombreado
flatfielde = ff.gammaCorrection(
eye, 0.7) #valor 0.73 análogo a alpha=60 en MATLAB
# ajuste de contraste - funcion análoga Imadjust de MATLAB
adjust_e = ij.imadjust(flatfielde, (0.3, 0.5)) # 0.5 - 0.7
double_e = i2d.im2double(adjust_e)
# parametros de segmentacion
alto, ancho = double_e.shape[:2]
mid = round(ancho / 2)
sec_R = [0, 0, mid, alto]
sec_L = [mid + 1, 0, ancho, alto]
# separamos el ojo izquierdo y derecho
Reye = double_e[sec_R[1]:sec_R[1] + sec_R[3],
sec_R[0]:sec_R[0] + sec_R[2]]
Leye = double_e[sec_L[1]:sec_L[1] + sec_L[3],
import cv2 as cv
import imflatfield as ff
import imadjust as ij
path = r'C:\Users\Ryzen\Documents\GitHub\Proyecto\Frame1.jpg'
img = cv.imread(path)
img_original = img[:, :, 2]
gamma = 0.724
res = ff.gammaCorrection(img_original, gamma)
m_e = ij.imadjust(res, (0.18, 0.32))
cv.imshow("Gamma correction", res)
cv.imshow("Imadjust", m_e)
cv.waitKey()
cv.destroyAllWindows()
# This section takes an RGB image as an input and prepares it for
# detection by removing the background.
(y, x, clrs) = im.shape # Get size of image
img_hou = np.copy(im[0:y / p2crop, 0:x]) # Crop image for circle detection
# Determine ideal limits for brightness/contrast adjustment
if lim_type == 1:
lims = stretchlim(im)
lims_hou = stretchlim(img_hou)
if lim_type == 2:
lims = stretchlim2(im)
lims_hou = stretchlim2(img_hou)
# Adjust the brightness/contrast of the RGB image based on limits
img2 = np.copy(imadjust(im, lims))
img2_hou = np.copy(imadjust(img_hou, lims_hou))
# Remove Background from adjusted brightness/contrast image
img_remove_hou = np.copy(back_ground_remove(img2_hou))
img_remove = np.copy(back_ground_remove(img2))
# Convert the image to binary
img_seg_hou, img_gray_hou = image_segmentation(img_remove_hou)
img_seg, img_gray = image_segmentation_length(img_remove)
# Edge detection
# NOTE: This is not actually in use. cv2.HoughCircles uses canny edge
# detection internally, so passing an edge image to it does not
# nice things.
img_edge = cv2.Canny(img_seg_hou, canny_param[0], canny_param[1])
#!/usr/bin/python
import cv2
import numpy as np
import sys
import matplotlib.pyplot as plt
import math
from imadjust import imadjust
from stretchlim import stretchlim
from back_ground_remove import back_ground_remove
from image_segmentation_length import image_segmentation_length
#Read image
im = cv2.imread('pipewrench.jpeg', 1)
lims = stretchlim(im)
img2 = imadjust(im, lims)
img_remove = back_ground_remove(img2)
cv2.imshow('imadjust output', img2)
cv2.imshow('background removal output', img_remove)
img_seg = image_segmentation_length(img_remove)
cv2.imshow('Image Segmentation Output', img_seg)
cv2.waitKey(0)