nmg.py

python/030.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sun Nov 06 20:57:23 2016
+
+@author: Parshu Rath
+"""
+
+import os
+import sys
+#from IPython.display import Image
+import matplotlib.pyplot as plt
+#import time 
+import numpy as np
+import scipy
+import scipy.misc
+from scipy import ndimage
+from scipy import misc
+from skimage import transform as tf
+import math
+import re
+
+""" 
+Define transformation functons.
+"""
+
+#Flip the image 
+def flipt(f):
+    return(np.flipud(f))
+
+#Rotate at some random angle transform 
+def rotatet(f):
+    angle = math.floor(np.random.uniform(-90, 90, 1))
+    return (ndimage.rotate(f, angle, reshape=False))
+
+#Add some random noise transform 
+def noisyt(f):
+    noisy = f + 0.9 * f.std() * np.random.random(f.shape)
+    return (f)
+
+#Add Gaussian transform Filter
+def gausst(f):
+    f1 = ndimage.gaussian_filter(f, sigma=3)
+    return(f1)
+
+#Add Uniform transform Filter
+def uniformt(f):
+    f1 = ndimage.uniform_filter(f, size=10)
+    return(f1)
+
+#Add Median transform Filter
+def mediant(f):
+    f1 = ndimage.median_filter(f, size=10)
+    return(f1)
+
+#Add Fourrier ellipsoid transform Filter
+def fouriert(f):
+    f1 = ndimage.fourier_ellipsoid(f, size=1.0)
+    return(f1)
+
+#Add Fourrier uniform transform Filter
+def fourierut(f):
+    f1 = ndimage.fourier_uniform(f, size=1.25)
+    return(f1)
+
+#Add Affine transform Filter
+def affinet(f):
+    H = np.array([[1.4,0.05,-100],[0.05,1.4,-100],[0,0,1]])
+    f1 = ndimage.affine_transform(f,H[:2,:2],(H[0,2],H[1,2]))
+    return(f1)
+
+#Add Geometric transform Filter
+def shift_func(output_coordinates):
+     return (output_coordinates[0] - 10.5, output_coordinates[1] - 10.5)
+def geomt(f):
+    H = np.array([[1.4,0.05,-100],[0.05,1.4,-100],[0,0,1]])
+    f1 = ndimage.geometric_transform(f, shift_func)
+    return(f1)
+
+"""
+Below is an example of applying the above transformations to images in a directory 
+and saving the transformed images.
+"""    
+#List of trsansformations
+transforms = [flipt, rotatet, noisyt, gausst, uniformt, mediant, fouriert, fourierut, affinet, geomt]
+#Source image directory
+imgdir = r'C:\pr\CUNY\MSDA Fall 2016\DATA 622\Kaggle Project\img10'
+#Get names of the images
+file_list = os.listdir(imgdir)
+#Directory to save the transformed images
+img_savedir = r'C:\pr\CUNY\MSDA Fall 2016\DATA 622\Kaggle Project\trans_img'
+
+#Transform the files and save.
+
+#import re
+for f in file_list:
+    #use one transformation at a time
+    for i in range(0, len(transforms)):
+        #read the image file
+        f1 = misc.imread(''.join([imgdir, '\\', f]))
+        #transform the image
+        f1t = transforms[i](f1)
+        #save the image after adding '-[i]t' in the file name
+        scipy.misc.imsave(''.join([img_savedir, '\\', re.sub(r'.jpg', '-', f), str(i),'t.jpg']), f1t)
+

python/nmg.py

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+#transform 500 images 10 times using opencv library
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+from os import listdir
+from os.path import isfile, join
+
+mypath='/users/nathangroom/desktop/new_docker/500_photos_2'
+#first transformation - rotate image 90 degrees
+for i in range(1,500):
+    img=cv2.imread('/users/nathangroom/desktop/new_docker/500_photos_2/'+listdir(mypath)[i],0)
+    rows,cols = img.shape
+    M = cv2.getRotationMatrix2D((cols/2,rows/2),90,1)
+    dst = cv2.warpAffine(img,M,(cols,rows))
+    cv2.imwrite('/users/nathangroom/desktop/new_docker/new_photos1/newphoto1_'+str(i)+'.jpg', dst)
+
+#second transformation - shift image
+for i in range(1,500):
+    img=cv2.imread('/users/nathangroom/desktop/new_docker/500_photos_2/'+listdir(mypath)[i],0)
+    rows,cols = img.shape
+    M = np.float32([[1,0,200],[0,1,90]])
+    dst = cv2.warpAffine(img,M,(cols,rows))
+    cv2.imwrite('/users/nathangroom/desktop/new_docker/new_photos2/newphoto2_'+str(i)+'.jpg', dst)
+
+#third transformation -- affine transformation
+for i in range(1,500):
+    img=cv2.imread('/users/nathangroom/desktop/new_docker/500_photos_2/'+listdir(mypath)[i])
+    rows,cols,ch = img.shape
+    pts1 = np.float32([[50,50],[200,50],[50,200]])
+    pts2 = np.float32([[10,100],[200,50],[100,250]])
+    M = cv2.getAffineTransform(pts1,pts2)
+    dst = cv2.warpAffine(img,M,(cols,rows))
+    cv2.imwrite('/users/nathangroom/desktop/new_docker/new_photos3/newphoto3_'+str(i)+'.jpg', dst)
+
+#fourth transformation -- perspective transformation
+for i in range(1,500):
+    img=cv2.imread('/users/nathangroom/desktop/new_docker/500_photos_2/'+listdir(mypath)[i])
+    rows,cols,ch = img.shape
+    pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]])
+    pts2 = np.float32([[0,0],[300,0],[0,300],[300,300]])
+    M = cv2.getPerspectiveTransform(pts1,pts2)
+    dst = cv2.warpPerspective(img,M,(300,300))
+    cv2.imwrite('/users/nathangroom/desktop/new_docker/new_photos4/newphoto4_'+str(i)+'.jpg', dst)
+
+#fifth transformation -- rotation 190 degrees
+for i in range(1,500):
+    img=cv2.imread('/users/nathangroom/desktop/new_docker/500_photos_2/'+listdir(mypath)[i],0)
+    rows,cols = img.shape
+    M = cv2.getRotationMatrix2D((cols/2,rows/2),190,1)
+    dst = cv2.warpAffine(img,M,(cols,rows))
+    cv2.imwrite('/users/nathangroom/desktop/new_docker/new_photos5/newphoto5_'+str(i)+'.jpg', dst)
+
+#sixth transformation -- combination of transformations 1 and 2
+for i in range(1,500):
+    img=cv2.imread('/users/nathangroom/desktop/new_docker/500_photos_2/'+listdir(mypath)[i],0)
+    rows, cols=img.shape
+    M = cv2.getRotationMatrix2D((cols/2,rows/2),90,1)
+    dst = cv2.warpAffine(img,M,(cols,rows))
+    M2=M = np.float32([[1,0,100],[0,1,45]])
+    dst2= cv2.warpAffine(dst,M2,(cols,rows))
+    cv2.imwrite('/users/nathangroom/desktop/new_docker/new_photos6/newphoto6_'+str(i)+'.jpg', dst2)
+
+#seventh transformation -- combination of transformations 1 and 3
+for i in range(1,500):
+    img=cv2.imread('/users/nathangroom/desktop/new_docker/500_photos_2/'+listdir(mypath)[i],0)
+    rows,cols = img.shape
+    M = cv2.getRotationMatrix2D((cols/2,rows/2),270,1)
+    dst = cv2.warpAffine(img,M,(cols,rows))
+    rows,cols=dst.shape
+    pts1 = np.float32([[50,50],[200,50],[50,200]])
+    pts2 = np.float32([[10,100],[200,50],[100,250]])
+    M2=cv2.getAffineTransform(pts1,pts2)
+    dst2=cv2.warpAffine(dst,M2,(cols,rows))
+    cv2.imwrite('/users/nathangroom/desktop/new_docker/new_photos7/newphoto7_'+str(i)+'.jpg', dst2)
+
+#eighth tranformation -- a combination of transformations 1 and 4
+for i in range(1,500):
+    img=cv2.imread('/users/nathangroom/desktop/new_docker/500_photos_2/'+listdir(mypath)[i],0)
+    rows,cols = img.shape
+    M = cv2.getRotationMatrix2D((cols/2,rows/2),200,1)
+    dst = cv2.warpAffine(img,M,(cols,rows))
+    pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]])
+    pts2 = np.float32([[0,0],[300,0],[0,300],[300,300]])
+    M2 = cv2.getPerspectiveTransform(pts1,pts2)
+    dst2 = cv2.warpPerspective(dst,M2,(300,300))
+    cv2.imwrite('/users/nathangroom/desktop/new_docker/new_photos8/newphoto8_'+str(i)+'.jpg', dst2)
+
+#ninth transformation -- combination of transformations 2 and 4
+for i in range(1,500):
+    img=cv2.imread('/users/nathangroom/desktop/new_docker/500_photos_2/'+listdir(mypath)[i],0)
+    rows,cols = img.shape
+    M = np.float32([[1,0,95],[0,1,55]])
+    dst = cv2.warpAffine(img,M,(cols,rows))
+    pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]])
+    pts2 = np.float32([[0,0],[300,0],[0,300],[300,300]])
+    M2 = cv2.getPerspectiveTransform(pts1,pts2)
+    dst2= cv2.warpPerspective(img,M2,(300,300))
+    cv2.imwrite('/users/nathangroom/desktop/new_docker/new_photos9/newphoto9_'+str(i)+'.jpg', dst2)
+
+#tenth transformation--combination of transformations 3 and 4
+for i in range(1,500):
+    img=cv2.imread('/users/nathangroom/desktop/new_docker/500_photos_2/'+listdir(mypath)[i])
+    rows,cols,ch = img.shape
+    pts1 = np.float32([[50,50],[200,50],[50,200]])
+    pts2 = np.float32([[10,100],[200,50],[100,250]])
+    M = cv2.getAffineTransform(pts1,pts2)
+    dst = cv2.warpAffine(img,M,(cols,rows))
+
+    pts3 = np.float32([[56,65],[368,52],[28,387],[389,390]])
+    pts4 = np.float32([[0,0],[300,0],[0,300],[300,300]])
+    M2 = cv2.getPerspectiveTransform(pts3,pts4)
+    dst2= cv2.warpPerspective(dst,M2,(300,300))
+    cv2.imwrite('/users/nathangroom/desktop/new_docker/new_photos10/newphoto10_'+str(i)+'.jpg', dst2)