I am required to access all images in a folder and store it in a matrix. I was able to do it using matlab and here is the code:
input_dir = 'C:\Users\Karim\Downloads\att_faces\New Folder';
image_dims = [112, 92];
filenames = dir(fullfile(input_dir, '*.pgm'));
num_images = numel(filenames);
images = [];
for n = 1:num_images
filename = fullfile(input_dir, filenames(n).name);
img = imread(filename);
img = imresize(img,image_dims);
end
but I am required to do it using python and here is my python code:
import Image
import os
from PIL import Image
from numpy import *
import numpy as np
#import images
dirname = "C:\\Users\\Karim\\Downloads\\att_faces\\New folder"
#get number of images and dimentions
path, dirs, files = os.walk(dirname).next()
num_images = len(files)
image_file = "C:\\Users\\Karim\\Downloads\\att_faces\\New folder\\2.pgm"
im = Image.open(image_file)
width, height = im.size
images = []
for x in xrange(1, num_images):
filename = os.listdir(dirname)[x]
img = Image.open(filename)
img = im.convert('L')
images[:, x] = img[:]
but I am getting this error:
IOError: [Errno 2] No such file or directory: '10.pgm'
although the file is present.
I'm not quite sure what your end goal is, but try something more like this:
import numpy as np
import Image
import glob
filenames = glob.glob('/path/to/your/files/*.pgm')
images = [Image.open(fn).convert('L') for fn in filenames]
data = np.dstack([np.array(im) for im in images])
This will yield a width x height x num_images numpy array, assuming that all of your images have the same dimensions.
However, your images will be unsorted, so you may want to do filenames.sort().
Also, you may or may not want things as a 3D numpy array, but that depends entirely on what you're actually doing. If you just want to operate on each "frame" individually, then don't bother stacking them into one gigantic array.
Related
I would like to save some tiff images I have into a new npy file.
My data are saved in 5 different files (tiff format). I want to access to each one of them, convert them in narray and then save them in a new npy file (for deep learning classification).
import numpy as np
from PIL import Image
import os
Data_dir = r"C:\Desktop\Université_2019_2020\CoursS2_Mosef\Stage\Data\Grand_Leez\shp\imagettes"
Categories = ["Bouleau_tiff", "Chene_tiff", "Erable_tiff", "Frene_tiff", "Peuplier_tiff"]
for categorie in Categories:
path = os.path.join(Data_dir, categorie) #path for each species
for img in os.listdir(path):
path_img = os.path.join(path,img)
im = Image.open(os.path.join(path_img)) #load an image file
imarray = np.array(im) # convert it to a matrix
imarray = np.delete(imarray, 3, axis=2)
np.save(Data_dir, imarray)
Problem: It's only return me the last observation of my last category "Peuplier_tiff", also it's saved into the name imagette, I don't know why.
Last but not least, I have a doubt for my targets, how I can be sure that my categories are correctly assign to the corresponding arrays.
A lot of questions,
thanks in advance for your help.
S.V
Thanks for your response. Its working with this code :
import numpy as np
from PIL import Image
import os
new_dir = "dta_npy"
directory = r"C:\Desktop\Université_2019_2020\CoursS2_Mosef\Stage\Data\Grand_Leez\shp\imagettes"
Data_dir = os.path.join(directory, new_dir)
os.makedirs(Data_dir)
print("Directory '%s' created" %Data_dir)
Categories = ["Bouleau_tif","Chene_tif", "Erable_tif", "Frene_tif", "Peuplier_tif"]
for categorie in Categories:
path = os.path.join(directory,categorie) #path for each species
for img in os.listdir(path):
im = Image.open(os.path.join(path,img)) #load an image file
imarray = np.array(im) # convert it to a matrix
imarray = np.delete(imarray, 3, axis=2)
unique_name=img
unique_name = unique_name.split(".")
unique_name = unique_name[0]
np.save(Data_dir+"/"+unique_name, imarray)
Now my objective is to format my data, for each of my class, in this way : (click on the link)
format goal
I've face this problem when figuring out how to export external images in blender script. But I guess this is not related straight to blender anymore, more to numpy and how to handle arrays. Here is post about first problem.
So the problem is that when saving numpy array to image it will distorted and there is multiple same images. Look below image for a better understanding.
The goal is trying to figure out how to make this work with numpy and python using the blender's own pixel data. So avoiding to use libraries like PIL or cv2 that do not include in blender python.
When saving data where is images that all is final size works correctly. And when trying to merge 4 smaller pieces to final larger image it not exported correctly.
I've done example script with python in blender to demonstrate the problem:
# Example script to show how to merge external images in Blender
# using numpy. In this example we use 4 images (2x2) that should
# be merged to one actual final image.
# Regular (not cropped render borders) seems to work fine but
# how to merge cropped images properly???
#
# Usage: Just run script and it will export image named "MERGED_IMAGE"
# to root of this project folder and you'll see what's the problem.
import bpy, os
import numpy as np
ctx = bpy.context
scn = ctx.scene
print('START')
# Get all image files
def get_files_in_folder(path):
path = bpy.path.abspath(path)
render_files = []
for root, dirs, files in os.walk(path):
for file in files:
if (file.lower().endswith(('.png', '.jpg', '.jpeg', '.tiff', '.bmp', '.gif'))):
render_files.append(file)
return render_files
def merge_images(image_files, image_cropped = True):
image_pixels = []
final_image_pixels = 0
print(image_files)
for file in image_files:
if image_cropped is True:
filepath = bpy.path.abspath('//Cropped\\' + file)
else:
filepath = bpy.path.abspath('//Regular\\' + file)
loaded_pixels = bpy.data.images.load(filepath, check_existing=True).pixels
image_pixels.append(loaded_pixels)
np_array = np.array(image_pixels)
# Merge images
if image_cropped:
final_image_pixels = np_array
# HOW MERGE PROPERLY WHEN USING CROPPED IMAGES???
else:
for arr in np_array:
final_image_pixels += arr
# Save output image
output_image = bpy.data.images.new('MERGED_IMAGE', alpha=True, width=256, height=256)
output_image.file_format = 'PNG'
output_image.alpha_mode = 'STRAIGHT'
output_image.pixels = final_image_pixels.ravel()
output_image.filepath_raw = bpy.path.abspath("//MERGED_IMAGE.png")
output_image.save()
images_cropped = get_files_in_folder("//Cropped")
images_regular = get_files_in_folder('//Regular')
# Change between these to get different example
merge_images(images_cropped)
#merge_images(images_regular, False)
print('END')
So I guess the problem is related to how to handle image pixel data and arrays with numpy.
Here is project folder in zip file that contains working test script example, where you can test how this works in blender. https://drive.google.com/file/d/1R4G_fubEzFWbHZMLtAAES-QsRhKyLKWb/view?usp=sharing
Since all of your images are the same dimension of 128x128, and since OpenCV images are Numpy arrays, here are three methods. You can save the image using cv2.imwrite.
Input images:
Method #1: np.hstack + np.vstack
hstack1 = np.hstack((image1, image2))
hstack2 = np.hstack((image3, image4))
hstack_result = np.vstack((hstack1, hstack2))
Method #2: np.concatenate
concatenate1 = np.concatenate((image1, image2), axis=1)
concatenate2 = np.concatenate((image3, image4), axis=1)
concatenate_result = np.concatenate((concatenate1, concatenate2), axis=0)
Method #3: cv2.hconcat + cv2.vconcat
hconcat1 = cv2.hconcat([image1, image2])
hconcat2 = cv2.hconcat([image3, image4])
hconcat_result = cv2.vconcat([hconcat1, hconcat2])
Result should be the same for all methods
Full code
import cv2
import numpy as np
# Load images
image1 = cv2.imread('Fart_1_2.png')
image2 = cv2.imread('Fart_2_2.png')
image3 = cv2.imread('Fart_1_1.png')
image4 = cv2.imread('Fart_2_1.png')
# Method #1
hstack1 = np.hstack((image1, image2))
hstack2 = np.hstack((image3, image4))
hstack_result = np.vstack((hstack1, hstack2))
# Method #2
concatenate1 = np.concatenate((image1, image2), axis=1)
concatenate2 = np.concatenate((image3, image4), axis=1)
concatenate_result = np.concatenate((concatenate1, concatenate2), axis=0)
# Method #3
hconcat1 = cv2.hconcat([image1, image2])
hconcat2 = cv2.hconcat([image3, image4])
hconcat_result = cv2.vconcat([hconcat1, hconcat2])
# Display
cv2.imshow('concatenate_result', concatenate_result)
cv2.imshow('hstack_result', hstack_result)
cv2.imshow('hconcat_result', hconcat_result)
cv2.waitKey()
In this directory "C:\Users\KG\Documents\R\data" I have 40 folders that are named from s1 to s40, where in each of the folder there are 10 pictures (.png) of faces named as (1,2,..10). How to import collection of pictures - faces as a flattened array? I use the code below, but it provide me with the mistake (does not download pictures):
from skimage import io
ic = io.ImageCollection('C:/Users/KG/Documents/R/data/*/*.png')
ic = np.array(ic)
ic_flat = ic.reshape((len(ic), -1))
You can use PIL library :
from PIL import Image
import numpy as np
ic = []
for i in folders:
for j in images:
image = Image.open(i + j)
ic.append(np.asarray(image))
ic = np.array(ic)
where folders and images are arrays of string with names
Give this code a try:
import os
from skimage import io
import numpy as np
folder = 'C:/Users/KG/Documents/R/data'
images = [os.path.join(root, filename)
for root, dirs, files in os.walk(folder)
for filename in files
if filename.lower().endswith('.png')]
ic = []
for img in images:
ic.append(io.imread(img).flatten())
It's a letter recognition task and there are 284 images, and 19 classes. I want to apply naive bayesian. First I have to convert each image to feature vector and for reducing extra info I should use some feature selection code like cropping images to remove extra black borders. But I'm not much experienced in python.
How can I crop black spaces in images in order to decrease the size of csv files? ( because the columns are more than expected!) And also how can I resize images to be the same size?
from PIL import Image, ImageChops
from resize import trim
import numpy as np
import cv2
import os
import csv
#Useful function
def createFileList(myDir, format='.jpg'):
fileList = []
print(myDir)
for root, dirs, files in os.walk(myDir, topdown=False):
for name in files:
if name.endswith(format):
fullName = os.path.join(root, name)
fileList.append(fullName)
return fileList
# load the original image
myFileList = createFileList('image_ocr')
#print(myFileList)
for file in myFileList:
#print(file)
img_file = Image.open(file)
# img_file.show()
# get original image parameters...
width, height = img_file.size
format = img_file.format
mode = img_file.mode
# Make image Greyscale
img_grey = img_file.convert('L')
# Save Greyscale values
value = np.asarray(img_grey.getdata(), dtype=np.int).reshape((img_grey.size[1], img_grey.size[0]))
value = value.flatten()
#print(value)
with open("trainData.csv", 'a') as f:
writer = csv.writer(f)
writer.writerow(value)
I have a set of 1000 gray scale images (28x28), I want to read them into 4D numpy array (number of images, 1, img_dim1,img_dim2). Following is my code but it doesn't work properly. Any idea how I can fix the issue in the code?
from PIL import Image
import numpy as np
import os
mypath=os.path.dirname('path/to/directory/')
def load_dataset( ) :
data =np.zeros((1000,1,28,28), dtype=np.float64)
for fname in os.listdir(mypath):
pathname = os.path.join(mypath, fname)
img = Image.open(pathname)
data = np.dstack((data, img))
return data
data=load_dataset()
print(data.shape)
The problem has been solved by using append and adding a new axis np.newaxis
from PIL import Image
import numpy as np
import os
mypath=os.path.dirname('path/to/directory/')
def load_dataset( ) :
data =[]
for fname in os.listdir(mypath):
pathname = os.path.join(mypath, fname)
img = Image.open(pathname)
img1 = img[np.newaxis,:,:]
data.append(img1)
return data
data= load_dataset()
data_x=np.array(data)
print data_x.shape