Dynamic directory read and pickle example in python

import math
from sklearn import neighbors
import os
import os.path
import pickle
import glob
from PIL import Image, ImageDraw

nameL = []
name_indx = []
i = 0
X = []
y = []

dirName = glob.glob('data\\*\\')
for name in dirName:
 #print(glob.glob(name+'*.jpg'))
 v = os.path.dirname(name)
 print(v)
 nameL.append(v)
 name_indx.append(i)
 
 i = i+1
 imgs = glob.glob(name+'*.jpg')
 for img_path in imgs:
  print(img_path)
  X.append(img_path)
  
print(nameL)
print(name_indx)
dictionary = dict(zip(name_indx, nameL))
print(dictionary)
#pickling.........
pickle_out = open("dict.pickle","wb")
pickle.dump(dictionary, pickle_out)
pickle_out.close()

#Unpickling.......
pickle_in = open("dict.pickle","rb")
example_dict = pickle.load(pickle_in)
print(example_dict)
print(example_dict[1])

Extract text from image using Pytesseract in windows platform

For windows Os, we need an installation. Pytesseract binary is available here. Then Add a new variable with name tesseract in environment variables with value C:\Program Files (x86)\Tesseract-OCR\tesseract.exe. 

Then we need to install a python package: pip install tesseract 

Some cases we need the following line of code (if the environment variable is not added correctly) 

pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files (x86)\Tesseract-OCR\tesseract.exe'

Here we provide the Pytesseract path to the interpreter.

Full Code

       
'''
download and install-https://github.com/UB-Mannheim/tesseract/wiki

'''

import numpy as np
import cv2
import time
import pytesseract
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files (x86)\Tesseract-OCR\tesseract.exe'
frame1 = cv2.imread('poc.jpg',0);

cv2.imwrite('ocr.jpg',frame1)

#from tesseract import image_to_string
text = pytesseract.image_to_string(frame1)
print(text)
cv2.imshow(text,frame1 )

cv2.waitKey(0)
cv2.destroyAllWindows()

Input Image

Output

ORB Feature matching Example in OpenCv

       

import cv2
import numpy as np
 
img1 = cv2.imread("face1.jpg", cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread("face2.jpg", cv2.IMREAD_GRAYSCALE)
 
# ORB Detector
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)
 
# Brute Force Matching
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1, des2)
matches = sorted(matches, key = lambda x:x.distance)
 
matching_result = cv2.drawMatches(img1, kp1, img2, kp2, matches[:50], None, flags=2)
 
cv2.imshow("Img1", img1)
cv2.imshow("Img2", img2)
cv2.imshow("Matching result", matching_result)
cv2.imwrite("Matching result.jpg", matching_result)
cv2.waitKey(0)
cv2.destroyAllWindows()

OutPut

Feature Descriptor like ORB, Shift and Surf Implementation using OpenCv Python

       
import cv2
import numpy as np
 
img = cv2.imread("2.PNG", cv2.IMREAD_GRAYSCALE)
 
sift = cv2.xfeatures2d.SIFT_create()
surf = cv2.xfeatures2d.SURF_create()
 
orb = cv2.ORB_create(nfeatures=1500)
# here None is for non-masking
keypoints1, descriptors1 = orb.detectAndCompute(img, None)
keypoints2, descriptors2 = sift.detectAndCompute(img, None)
keypoints3, descriptors3 = surf.detectAndCompute(img, None)
 
imgOrb  = cv2.drawKeypoints(img, keypoints1, None)
imgSift = cv2.drawKeypoints(img, keypoints2, None)
imgSurf = cv2.drawKeypoints(img, keypoints3, None)


cv2.imshow("Orb", cv2.resize(imgOrb,(700,500)) )
cv2.imshow("Sift", cv2.resize(imgSift,(700,500)) )
cv2.imshow("Surf", cv2.resize(imgSurf,(700,500)) )

cv2.waitKey(0)
cv2.destroyAllWindows()

OutPut

Capture Image by showing two finger to Camera using OpenCV python

You can find the code Here.

Here I use a binary classification model of SVM with rbf kernel. 

OutPut

Simple implementation of SVM in python

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn import svm



#train set
x = [[86,105], [109, 100], [94, 105], [106, 100], [100, 100], 

     [80, 90], [103, 80], [105, 80], [120, 85], [77, 83], [92, 75], 

     [98, 76], [106, 82],[106, 77], [105, 77], [119, 80], [115, 70], 

     [110, 66], [105, 65], [90, 67], [80, 60], [90, 57], [105, 55],

     [115, 55], [110, 50], [109, 49], [95, 45], [100, 42], [105, 40],

     [110, 42], [115, 42], [115,35], [105, 35], [85, 35], [95, 35], [109, 35],

     [115, 35], [120, 30], [105, 29], [109, 25]]

#train set lebels

y = [130, 130, 130, 130, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,

     120, 110, 110, 110, 110, 100, 100, 100, 100, 90, 90, 80, 80, 80, 80, 80, 70, 

     70, 60, 60, 60, 60, 50, 50, 40]
clf = svm.SVC(gamma=0.001, C=100)

clf.fit(x,y)

from sklearn.externals import joblib
joblib.dump(clf, 'C:/Users/kiit1/Desktop/svm.pkl')
test_set = [[86,105], [109, 100], [80, 90], [103, 80], [98, 76], [106, 82],

 [105, 77], [119, 80], [90, 57], [105, 55], [119, 27]]
y_test = [130, 130, 120, 123, 120, 120, 120, 120, 100, 100, 40]
# model accuracy for X_testaccuracy = clf.score(test_set, y_test)
print(clf.predict(test_set))
print(accuracy)

# creating a confusion matrix

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, clf.predict(test_set))
print(cm)

clf1 = joblib.load('C:/Users/kiit1/Desktop/svm.pkl')
print(clf1.predict([[86,105]]))

Output

Text separation using OpenCv python

       

import numpy as np
import time
import cv2

# Load an color image in grayscale
img = cv2.imread('1.PNG',0)
cv2.imshow('Realimage',img)
Img_height = np.size(img, 0)
Img_width = np.size(img, 1)
print(Img_height)
print("-----------------------------------------")
imgray = img
thresh = cv2.adaptiveThreshold(imgray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)#cv2.threshold(imgray,127,255,0)
im2, contours, hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0,255,0), 3)
#cnt = contours[4]
for cnt in contours:
 #cv2.drawContours(img, [cnt], 0, (128,255,0), 2)
 x,y,w,h = cv2.boundingRect(cnt)
 img =cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
 crop_img = img[y:y+h, x:x+w]
 height_sub = np.size(crop_img, 0)
 width_sub = np.size(crop_img, 1)
 print(height_sub)
 if Img_height/2 <height_sub and Img_width/2 > width_sub:
     cv2.imwrite('./dataIMG/'+ str(time.time())+'.png',crop_img)
 
cv2.imshow('image',imgray)

cv2.waitKey(0)
cv2.destroyAllWindows()

Input

Output