OpenCV | Blur in Python

blur | NumPy | open | Python Methods and Functions

Motion blur filter
Applying motion blur to an image is reduced to a convolution of the filter on the image. Examples of 5 * 5 filters are shown below.

vertical :

horizontal :

The larger the filter size, the greater the motion blur effect. In addition, direction 1 on the filter screen is the direction of the desired movement. To set motion blur in a specific direction of a vector, such as diagonally, simply place 1 along the vector to create a filter.

Code
Consider the following image of a car.

Code: Python code to apply motion blur to an image.

# library loading

import cv2

import numpy as np

   
   img   =   cv2.imread (  `car.jpg`  ) 
   
  # Specify the kernel size.  
  # The larger the size, the more movement.  
   kernel_size   =   30  
   
  # Create a vertical core.  
   kernel_v   =   np.zeros ((kernel_size, kernel_size)) 
   
  # Make the same copy to create a horizontal kernel.  
   kernel_h   =   np.copy (kernel_v) 
   
  # Fill in middle row with them.  
   kernel_v [:,   int   ((kernel_size   -   1  )   /   2  )]   =   np.ones (kernel_size) 
   kernel_h [   int   ((kernel_size   -   1  )   /   2  ),:]   =   np.ones (kernel_size)  
   
  Normalize  
   kernel_v   /   =   kernel_size 
   kernel_h   /   =   kernel_size 
   
  # Apply a vertical core.  
   vertical_mb   =   cv2.filter2D (img,   -   1  , kernel_v) 
    
  # Use a horizontal core.  
   horizonal_mb   =   cv2.filter2D (img,   -   1  , kernel_h) 
   
  Save the results.  
   cv2.imwrite (  `car_vertical.jpg`  , vertical_mb) 
   cv2.imwrite ( ` car_horizontal.jpg`  , horizonal_mb)

Exit

Vertical blur:

Horizontal blur:

OpenCV | Blur in Python: StackOverflow Questions

Answer #1

They contain byte code, which is what the Python interpreter compiles the source to. This code is then executed by Python"s virtual machine.

Python"s documentation explains the definition like this:

Python is an interpreted language, as opposed to a compiled one, though the distinction can be blurry because of the presence of the bytecode compiler. This means that source files can be run directly without explicitly creating an executable which is then run.

Answer #2

Well, I decided to workout myself on my question to solve above problem. What I wanted is to implement a simpl OCR using KNearest or SVM features in OpenCV. And below is what I did and how. ( it is just for learning how to use KNearest for simple OCR purposes).

1) My first question was about letter_recognition.data file that comes with OpenCV samples. I wanted to know what is inside that file.

It contains a letter, along with 16 features of that letter.

And this SOF helped me to find it. These 16 features are explained in the paperLetter Recognition Using Holland-Style Adaptive Classifiers. ( Although I didn"t understand some of the features at end)

2) Since I knew, without understanding all those features, it is difficult to do that method. I tried some other papers, but all were a little difficult for a beginner.

So I just decided to take all the pixel values as my features. (I was not worried about accuracy or performance, I just wanted it to work, at least with the least accuracy)

I took below image for my training data:

( I know the amount of training data is less. But, since all letters are of same font and size, I decided to try on this).

To prepare the data for training, I made a small code in OpenCV. It does following things:

It loads the image.

Selects the digits ( obviously by contour finding and applying constraints on area and height of letters to avoid false detections).

Draws the bounding rectangle around one letter and wait for key press manually. This time we press the digit key ourselves corresponding to the letter in box.

Once corresponding digit key is pressed, it resizes this box to 10x10 and saves 100 pixel values in an array (here, samples) and corresponding manually entered digit in another array(here, responses).

Then save both the arrays in separate txt files.

At the end of manual classification of digits, all the digits in the train data( train.png) are labeled manually by ourselves, image will look like below:

Below is the code I used for above purpose ( of course, not so clean):

import sys import numpy as np import cv2 im = cv2.imread("pitrain.png") im3 = im.copy() gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) blur = cv2.GaussianBlur(gray,(5,5),0) thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2) ################# Now finding Contours ################### contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) samples = np.empty((0,100)) responses = [] keys = [i for i in range(48,58)] for cnt in contours: if cv2.contourArea(cnt)>50: [x,y,w,h] = cv2.boundingRect(cnt) if h>28: cv2.rectangle(im,(x,y),(x+w,y+h),(0,0,255),2) roi = thresh[y:y+h,x:x+w] roismall = cv2.resize(roi,(10,10)) cv2.imshow("norm",im) key = cv2.waitKey(0) if key == 27: # (escape to quit) sys.exit() elif key in keys: responses.append(int(chr(key))) sample = roismall.reshape((1,100)) samples = np.append(samples,sample,0) responses = np.array(responses,np.float32) responses = responses.reshape((responses.size,1)) print "training complete" np.savetxt("generalsamples.data",samples) np.savetxt("generalresponses.data",responses)

Now we enter in to training and testing part.

For testing part I used below image, which has same type of letters I used to train.

For training we do as follows:

Load the txt files we already saved earlier

create a instance of classifier we are using ( here, it is KNearest)

Then we use KNearest.train function to train the data

For testing purposes, we do as follows:

We load the image used for testing

process the image as earlier and extract each digit using contour methods

Draw bounding box for it, then resize to 10x10, and store its pixel values in an array as done earlier.

Then we use KNearest.find_nearest() function to find the nearest item to the one we gave. ( If lucky, it recognises the correct digit.)

I included last two steps ( training and testing) in single code below:

import cv2 import numpy as np ####### training part ############### samples = np.loadtxt("generalsamples.data",np.float32) responses = np.loadtxt("generalresponses.data",np.float32) responses = responses.reshape((responses.size,1)) model = cv2.KNearest() model.train(samples,responses) ############################# testing part ######################### im = cv2.imread("pi.png") out = np.zeros(im.shape,np.uint8) gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) thresh = cv2.adaptiveThreshold(gray,255,1,1,11,2) contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: if cv2.contourArea(cnt)>50: [x,y,w,h] = cv2.boundingRect(cnt) if h>28: cv2.rectangle(im,(x,y),(x+w,y+h),(0,255,0),2) roi = thresh[y:y+h,x:x+w] roismall = cv2.resize(roi,(10,10)) roismall = roismall.reshape((1,100)) roismall = np.float32(roismall) retval, results, neigh_resp, dists = model.find_nearest(roismall, k = 1) string = str(int((results[0][0]))) cv2.putText(out,string,(x,y+h),0,1,(0,255,0)) cv2.imshow("im",im) cv2.imshow("out",out) cv2.waitKey(0)

And it worked, below is the result I got:

Here it worked with 100% accuracy. I assume this is because all the digits are of same kind and same size.

But any way, this is a good start to go for beginners ( I hope so).

Answer #3

If you"re just wanting (semi) contiguous regions, there"s already an easy implementation in Python: SciPy"s ndimage.morphology module. This is a fairly common image morphology operation.

Basically, you have 5 steps:

def find_paws(data, smooth_radius=5, threshold=0.0001): data = sp.ndimage.uniform_filter(data, smooth_radius) thresh = data > threshold filled = sp.ndimage.morphology.binary_fill_holes(thresh) coded_paws, num_paws = sp.ndimage.label(filled) data_slices = sp.ndimage.find_objects(coded_paws) return object_slices

Blur the input data a bit to make sure the paws have a continuous footprint. (It would be more efficient to just use a larger kernel (the structure kwarg to the various scipy.ndimage.morphology functions) but this isn"t quite working properly for some reason...)

Threshold the array so that you have a boolean array of places where the pressure is over some threshold value (i.e. thresh = data > value)

Fill any internal holes, so that you have cleaner regions (filled = sp.ndimage.morphology.binary_fill_holes(thresh))

Find the separate contiguous regions (coded_paws, num_paws = sp.ndimage.label(filled)). This returns an array with the regions coded by number (each region is a contiguous area of a unique integer (1 up to the number of paws) with zeros everywhere else)).

Isolate the contiguous regions using data_slices = sp.ndimage.find_objects(coded_paws). This returns a list of tuples of slice objects, so you could get the region of the data for each paw with [data[x] for x in data_slices]. Instead, we"ll draw a rectangle based on these slices, which takes slightly more work.

The two animations below show your "Overlapping Paws" and "Grouped Paws" example data. This method seems to be working perfectly. (And for whatever it"s worth, this runs much more smoothly than the GIF images below on my machine, so the paw detection algorithm is fairly fast...)

Here"s a full example (now with much more detailed explanations). The vast majority of this is reading the input and making an animation. The actual paw detection is only 5 lines of code.

import numpy as np import scipy as sp import scipy.ndimage import matplotlib.pyplot as plt from matplotlib.patches import Rectangle def animate(input_filename): """Detects paws and animates the position and raw data of each frame in the input file""" # With matplotlib, it"s much, much faster to just update the properties # of a display object than it is to create a new one, so we"ll just update # the data and position of the same objects throughout this animation... infile = paw_file(input_filename) # Since we"re making an animation with matplotlib, we need # ion() instead of show()... plt.ion() fig = plt.figure() ax = fig.add_subplot(111) fig.suptitle(input_filename) # Make an image based on the first frame that we"ll update later # (The first frame is never actually displayed) im = ax.imshow(infile.next()[1]) # Make 4 rectangles that we can later move to the position of each paw rects = [Rectangle((0,0), 1,1, fc="none", ec="red") for i in range(4)] [ax.add_patch(rect) for rect in rects] title = ax.set_title("Time 0.0 ms") # Process and display each frame for time, frame in infile: paw_slices = find_paws(frame) # Hide any rectangles that might be visible [rect.set_visible(False) for rect in rects] # Set the position and size of a rectangle for each paw and display it for slice, rect in zip(paw_slices, rects): dy, dx = slice rect.set_xy((dx.start, dy.start)) rect.set_width(dx.stop - dx.start + 1) rect.set_height(dy.stop - dy.start + 1) rect.set_visible(True) # Update the image data and title of the plot title.set_text("Time %0.2f ms" % time) im.set_data(frame) im.set_clim([frame.min(), frame.max()]) fig.canvas.draw() def find_paws(data, smooth_radius=5, threshold=0.0001): """Detects and isolates contiguous regions in the input array""" # Blur the input data a bit so the paws have a continous footprint data = sp.ndimage.uniform_filter(data, smooth_radius) # Threshold the blurred data (this needs to be a bit > 0 due to the blur) thresh = data > threshold # Fill any interior holes in the paws to get cleaner regions... filled = sp.ndimage.morphology.binary_fill_holes(thresh) # Label each contiguous paw coded_paws, num_paws = sp.ndimage.label(filled) # Isolate the extent of each paw data_slices = sp.ndimage.find_objects(coded_paws) return data_slices def paw_file(filename): """Returns a iterator that yields the time and data in each frame The infile is an ascii file of timesteps formatted similar to this: Frame 0 (0.00 ms) 0.0 0.0 0.0 0.0 0.0 0.0 Frame 1 (0.53 ms) 0.0 0.0 0.0 0.0 0.0 0.0 ... """ with open(filename) as infile: while True: try: time, data = read_frame(infile) yield time, data except StopIteration: break def read_frame(infile): """Reads a frame from the infile.""" frame_header = infile.next().strip().split() time = float(frame_header[-2][1:]) data = [] while True: line = infile.next().strip().split() if line == []: break data.append(line) return time, np.array(data, dtype=np.float) if __name__ == "__main__": animate("Overlapping paws.bin") animate("Grouped up paws.bin") animate("Normal measurement.bin")

Update: As far as identifying which paw is in contact with the sensor at what times, the simplest solution is to just do the same analysis, but use all of the data at once. (i.e. stack the input into a 3D array, and work with it, instead of the individual time frames.) Because SciPy"s ndimage functions are meant to work with n-dimensional arrays, we don"t have to modify the original paw-finding function at all.

# This uses functions (and imports) in the previous code example!! def paw_regions(infile): # Read in and stack all data together into a 3D array data, time = [], [] for t, frame in paw_file(infile): time.append(t) data.append(frame) data = np.dstack(data) time = np.asarray(time) # Find and label the paw impacts data_slices, coded_paws = find_paws(data, smooth_radius=4) # Sort by time of initial paw impact... This way we can determine which # paws are which relative to the first paw with a simple modulo 4. # (Assuming a 4-legged dog, where all 4 paws contacted the sensor) data_slices.sort(key=lambda dat_slice: dat_slice[2].start) # Plot up a simple analysis fig = plt.figure() ax1 = fig.add_subplot(2,1,1) annotate_paw_prints(time, data, data_slices, ax=ax1) ax2 = fig.add_subplot(2,1,2) plot_paw_impacts(time, data_slices, ax=ax2) fig.suptitle(infile) def plot_paw_impacts(time, data_slices, ax=None): if ax is None: ax = plt.gca() # Group impacts by paw... for i, dat_slice in enumerate(data_slices): dx, dy, dt = dat_slice paw = i%4 + 1 # Draw a bar over the time interval where each paw is in contact ax.barh(bottom=paw, width=time[dt].ptp(), height=0.2, left=time[dt].min(), align="center", color="red") ax.set_yticks(range(1, 5)) ax.set_yticklabels(["Paw 1", "Paw 2", "Paw 3", "Paw 4"]) ax.set_xlabel("Time (ms) Since Beginning of Experiment") ax.yaxis.grid(True) ax.set_title("Periods of Paw Contact") def annotate_paw_prints(time, data, data_slices, ax=None): if ax is None: ax = plt.gca() # Display all paw impacts (sum over time) ax.imshow(data.sum(axis=2).T) # Annotate each impact with which paw it is # (Relative to the first paw to hit the sensor) x, y = [], [] for i, region in enumerate(data_slices): dx, dy, dz = region # Get x,y center of slice... x0 = 0.5 * (dx.start + dx.stop) y0 = 0.5 * (dy.start + dy.stop) x.append(x0); y.append(y0) # Annotate the paw impacts ax.annotate("Paw %i" % (i%4 +1), (x0, y0), color="red", ha="center", va="bottom") # Plot line connecting paw impacts ax.plot(x,y, "-wo") ax.axis("image") ax.set_title("Order of Steps")

Answer #4

General idea

Option 1: Load both images as arrays (scipy.misc.imread) and calculate an element-wise (pixel-by-pixel) difference. Calculate the norm of the difference.

Option 2: Load both images. Calculate some feature vector for each of them (like a histogram). Calculate distance between feature vectors rather than images.

However, there are some decisions to make first.

Questions

You should answer these questions first:

Are images of the same shape and dimension?

If not, you may need to resize or crop them. PIL library will help to do it in Python.

If they are taken with the same settings and the same device, they are probably the same.

Are images well-aligned?

If not, you may want to run cross-correlation first, to find the best alignment first. SciPy has functions to do it.

If the camera and the scene are still, the images are likely to be well-aligned.

Is exposure of the images always the same? (Is lightness/contrast the same?)

If not, you may want to normalize images.

But be careful, in some situations this may do more wrong than good. For example, a single bright pixel on a dark background will make the normalized image very different.

Is color information important?

If you want to notice color changes, you will have a vector of color values per point, rather than a scalar value as in gray-scale image. You need more attention when writing such code.

Are there distinct edges in the image? Are they likely to move?

If yes, you can apply edge detection algorithm first (e.g. calculate gradient with Sobel or Prewitt transform, apply some threshold), then compare edges on the first image to edges on the second.

Is there noise in the image?

All sensors pollute the image with some amount of noise. Low-cost sensors have more noise. You may wish to apply some noise reduction before you compare images. Blur is the most simple (but not the best) approach here.

What kind of changes do you want to notice?

This may affect the choice of norm to use for the difference between images.

Consider using Manhattan norm (the sum of the absolute values) or zero norm (the number of elements not equal to zero) to measure how much the image has changed. The former will tell you how much the image is off, the latter will tell only how many pixels differ.

Example

I assume your images are well-aligned, the same size and shape, possibly with different exposure. For simplicity, I convert them to grayscale even if they are color (RGB) images.

You will need these imports:

import sys from scipy.misc import imread from scipy.linalg import norm from scipy import sum, average

Main function, read two images, convert to grayscale, compare and print results:

def main(): file1, file2 = sys.argv[1:1+2] # read images as 2D arrays (convert to grayscale for simplicity) img1 = to_grayscale(imread(file1).astype(float)) img2 = to_grayscale(imread(file2).astype(float)) # compare n_m, n_0 = compare_images(img1, img2) print "Manhattan norm:", n_m, "/ per pixel:", n_m/img1.size print "Zero norm:", n_0, "/ per pixel:", n_0*1.0/img1.size

How to compare. img1 and img2 are 2D SciPy arrays here:

def compare_images(img1, img2): # normalize to compensate for exposure difference, this may be unnecessary # consider disabling it img1 = normalize(img1) img2 = normalize(img2) # calculate the difference and its norms diff = img1 - img2 # elementwise for scipy arrays m_norm = sum(abs(diff)) # Manhattan norm z_norm = norm(diff.ravel(), 0) # Zero norm return (m_norm, z_norm)

If the file is a color image, imread returns a 3D array, average RGB channels (the last array axis) to obtain intensity. No need to do it for grayscale images (e.g. .pgm):

def to_grayscale(arr): "If arr is a color image (3D array), convert it to grayscale (2D array)." if len(arr.shape) == 3: return average(arr, -1) # average over the last axis (color channels) else: return arr

Normalization is trivial, you may choose to normalize to [0,1] instead of [0,255]. arr is a SciPy array here, so all operations are element-wise:

def normalize(arr): rng = arr.max()-arr.min() amin = arr.min() return (arr-amin)*255/rng

Run the main function:

if __name__ == "__main__": main()

Now you can put this all in a script and run against two images. If we compare image to itself, there is no difference:

$ python compare.py one.jpg one.jpg Manhattan norm: 0.0 / per pixel: 0.0 Zero norm: 0 / per pixel: 0.0

If we blur the image and compare to the original, there is some difference:

$ python compare.py one.jpg one-blurred.jpg Manhattan norm: 92605183.67 / per pixel: 13.4210411116 Zero norm: 6900000 / per pixel: 1.0

P.S. Entire compare.py script.

Update: relevant techniques

As the question is about a video sequence, where frames are likely to be almost the same, and you look for something unusual, I"d like to mention some alternative approaches which may be relevant:

background subtraction and segmentation (to detect foreground objects)

sparse optical flow (to detect motion)

comparing histograms or some other statistics instead of images

I strongly recommend taking a look at ‚ÄúLearning OpenCV‚Äù book, Chapters 9 (Image parts and segmentation) and 10 (Tracking and motion). The former teaches to use Background subtraction method, the latter gives some info on optical flow methods. All methods are implemented in OpenCV library. If you use Python, I suggest to use OpenCV ‚â• 2.3, and its cv2 Python module.

The most simple version of the background subtraction:

learn the average value Œº and standard deviation œÉ for every pixel of the background

compare current pixel values to the range of (Œº-2œÉ,Œº+2œÉ) or (Œº-œÉ,Œº+œÉ)

More advanced versions make take into account time series for every pixel and handle non-static scenes (like moving trees or grass).

The idea of optical flow is to take two or more frames, and assign velocity vector to every pixel (dense optical flow) or to some of them (sparse optical flow). To estimate sparse optical flow, you may use Lucas-Kanade method (it is also implemented in OpenCV). Obviously, if there is a lot of flow (high average over max values of the velocity field), then something is moving in the frame, and subsequent images are more different.

Comparing histograms may help to detect sudden changes between consecutive frames. This approach was used in Courbon et al, 2010:

Similarity of consecutive frames. The distance between two consecutive frames is measured. If it is too high, it means that the second frame is corrupted and thus the image is eliminated. The Kullback‚ÄìLeibler distance, or mutual entropy, on the histograms of the two frames:

where p and q are the histograms of the frames is used. The threshold is fixed on 0.2.

Answer #5

Nikie"s answer solved my problem, but his answer was in Mathematica. So I thought I should give its OpenCV adaptation here. But after implementing I could see that OpenCV code is much bigger than nikie"s mathematica code. And also, I couldn"t find interpolation method done by nikie in OpenCV ( although it can be done using scipy, i will tell it when time comes.)

1. Image PreProcessing ( closing operation )

import cv2 import numpy as np img = cv2.imread("dave.jpg") img = cv2.GaussianBlur(img,(5,5),0) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) mask = np.zeros((gray.shape),np.uint8) kernel1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(11,11)) close = cv2.morphologyEx(gray,cv2.MORPH_CLOSE,kernel1) div = np.float32(gray)/(close) res = np.uint8(cv2.normalize(div,div,0,255,cv2.NORM_MINMAX)) res2 = cv2.cvtColor(res,cv2.COLOR_GRAY2BGR)

Result :

2. Finding Sudoku Square and Creating Mask Image

thresh = cv2.adaptiveThreshold(res,255,0,1,19,2) contour,hier = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) max_area = 0 best_cnt = None for cnt in contour: area = cv2.contourArea(cnt) if area > 1000: if area > max_area: max_area = area best_cnt = cnt cv2.drawContours(mask,[best_cnt],0,255,-1) cv2.drawContours(mask,[best_cnt],0,0,2) res = cv2.bitwise_and(res,mask)

Result :

3. Finding Vertical lines

kernelx = cv2.getStructuringElement(cv2.MORPH_RECT,(2,10)) dx = cv2.Sobel(res,cv2.CV_16S,1,0) dx = cv2.convertScaleAbs(dx) cv2.normalize(dx,dx,0,255,cv2.NORM_MINMAX) ret,close = cv2.threshold(dx,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) close = cv2.morphologyEx(close,cv2.MORPH_DILATE,kernelx,iterations = 1) contour, hier = cv2.findContours(close,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) for cnt in contour: x,y,w,h = cv2.boundingRect(cnt) if h/w > 5: cv2.drawContours(close,[cnt],0,255,-1) else: cv2.drawContours(close,[cnt],0,0,-1) close = cv2.morphologyEx(close,cv2.MORPH_CLOSE,None,iterations = 2) closex = close.copy()

Result :

4. Finding Horizontal Lines

kernely = cv2.getStructuringElement(cv2.MORPH_RECT,(10,2)) dy = cv2.Sobel(res,cv2.CV_16S,0,2) dy = cv2.convertScaleAbs(dy) cv2.normalize(dy,dy,0,255,cv2.NORM_MINMAX) ret,close = cv2.threshold(dy,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) close = cv2.morphologyEx(close,cv2.MORPH_DILATE,kernely) contour, hier = cv2.findContours(close,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) for cnt in contour: x,y,w,h = cv2.boundingRect(cnt) if w/h > 5: cv2.drawContours(close,[cnt],0,255,-1) else: cv2.drawContours(close,[cnt],0,0,-1) close = cv2.morphologyEx(close,cv2.MORPH_DILATE,None,iterations = 2) closey = close.copy()

Result :

Of course, this one is not so good.

5. Finding Grid Points

res = cv2.bitwise_and(closex,closey)

Result :

6. Correcting the defects

Here, nikie does some kind of interpolation, about which I don"t have much knowledge. And i couldn"t find any corresponding function for this OpenCV. (may be it is there, i don"t know).

Check out this SOF which explains how to do this using SciPy, which I don"t want to use : Image transformation in OpenCV

So, here I took 4 corners of each sub-square and applied warp Perspective to each.

For that, first we find the centroids.

contour, hier = cv2.findContours(res,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) centroids = [] for cnt in contour: mom = cv2.moments(cnt) (x,y) = int(mom["m10"]/mom["m00"]), int(mom["m01"]/mom["m00"]) cv2.circle(img,(x,y),4,(0,255,0),-1) centroids.append((x,y))

But resulting centroids won"t be sorted. Check out below image to see their order:

So we sort them from left to right, top to bottom.

centroids = np.array(centroids,dtype = np.float32) c = centroids.reshape((100,2)) c2 = c[np.argsort(c[:,1])] b = np.vstack([c2[i*10:(i+1)*10][np.argsort(c2[i*10:(i+1)*10,0])] for i in xrange(10)]) bm = b.reshape((10,10,2))

Now see below their order :

Finally we apply the transformation and create a new image of size 450x450.

output = np.zeros((450,450,3),np.uint8) for i,j in enumerate(b): ri = i/10 ci = i%10 if ci != 9 and ri!=9: src = bm[ri:ri+2, ci:ci+2 , :].reshape((4,2)) dst = np.array( [ [ci*50,ri*50],[(ci+1)*50-1,ri*50],[ci*50,(ri+1)*50-1],[(ci+1)*50-1,(ri+1)*50-1] ], np.float32) retval = cv2.getPerspectiveTransform(src,dst) warp = cv2.warpPerspective(res2,retval,(450,450)) output[ri*50:(ri+1)*50-1 , ci*50:(ci+1)*50-1] = warp[ri*50:(ri+1)*50-1 , ci*50:(ci+1)*50-1].copy()

Result :

The result is almost same as nikie"s, but code length is large. May be, better methods are available out there, but until then, this works OK.

Regards ARK.

OpenCV | Blur in Python: StackOverflow Questions

How can I open multiple files using "with open" in Python?

I want to change a couple of files at one time, iff I can write to all of them. I"m wondering if I somehow can combine the multiple open calls with the with statement:

try: with open("a", "w") as a and open("b", "w") as b: do_something() except IOError as e: print "Operation failed: %s" % e.strerror

If that"s not possible, what would an elegant solution to this problem look like?

open() in Python does not create a file if it doesn"t exist

What is the best way to open a file as read/write if it exists, or if it does not, then create it and open it as read/write? From what I read, file = open("myfile.dat", "rw") should do this, right?

It is not working for me (Python 2.6.2) and I"m wondering if it is a version problem, or not supposed to work like that or what.

The bottom line is, I just need a solution for the problem. I am curious about the other stuff, but all I need is a nice way to do the opening part.

The enclosing directory was writeable by user and group, not other (I"m on a Linux system... so permissions 775 in other words), and the exact error was:

IOError: no such file or directory.

Difference between modes a, a+, w, w+, and r+ in built-in open function?

In the python built-in open function, what is the exact difference between the modes w, a, w+, a+, and r+?

In particular, the documentation implies that all of these will allow writing to the file, and says that it opens the files for "appending", "writing", and "updating" specifically, but does not define what these terms mean.

Simple Digit Recognition OCR in OpenCV-Python

I am trying to implement a "Digit Recognition OCR" in OpenCV-Python (cv2). It is just for learning purposes. I would like to learn both KNearest and SVM features in OpenCV.

I have 100 samples (i.e. images) of each digit. I would like to train with them.

There is a sample letter_recog.py that comes with OpenCV sample. But I still couldn"t figure out on how to use it. I don"t understand what are the samples, responses etc. Also, it loads a txt file at first, which I didn"t understand first.

Later on searching a little bit, I could find a letter_recognition.data in cpp samples. I used it and made a code for cv2.KNearest in the model of letter_recog.py (just for testing):

import numpy as np import cv2 fn = "letter-recognition.data" a = np.loadtxt(fn, np.float32, delimiter=",", converters={ 0 : lambda ch : ord(ch)-ord("A") }) samples, responses = a[:,1:], a[:,0] model = cv2.KNearest() retval = model.train(samples,responses) retval, results, neigh_resp, dists = model.find_nearest(samples, k = 10) print results.ravel()

It gave me an array of size 20000, I don"t understand what it is.

Questions:

1) What is letter_recognition.data file? How to build that file from my own data set?

2) What does results.reval() denote?

3) How we can write a simple digit recognition tool using letter_recognition.data file (either KNearest or SVM)?

Does reading an entire file leave the file handle open?

If you read an entire file with content = open("Path/to/file", "r").read() is the file handle left open until the script exits? Is there a more concise method to read a whole file?

Store output of subprocess.Popen call in a string

I"m trying to make a system call in Python and store the output to a string that I can manipulate in the Python program.

#!/usr/bin/python import subprocess p2 = subprocess.Popen("ntpq -p")

I"ve tried a few things including some of the suggestions here:

Retrieving the output of subprocess.call()

but without any luck.

"Unicode Error "unicodeescape" codec can"t decode bytes... Cannot open text files in Python 3

I am using Python 3.1 on a Windows 7 machine. Russian is the default system language, and utf-8 is the default encoding.

Looking at the answer to a previous question, I have attempting using the "codecs" module to give me a little luck. Here"s a few examples:

>>> g = codecs.open("C:UsersEricDesktopeeline.txt", "r", encoding="utf-8") SyntaxError: (unicode error) "unicodeescape" codec can"t decode bytes in position 2-4: truncated UXXXXXXXX escape (<pyshell#39>, line 1)

>>> g = codecs.open("C:UsersEricDesktopSite.txt", "r", encoding="utf-8") SyntaxError: (unicode error) "unicodeescape" codec can"t decode bytes in position 2-4: truncated UXXXXXXXX escape (<pyshell#40>, line 1)

>>> g = codecs.open("C:Python31Notes.txt", "r", encoding="utf-8") SyntaxError: (unicode error) "unicodeescape" codec can"t decode bytes in position 11-12: malformed N character escape (<pyshell#41>, line 1)

>>> g = codecs.open("C:UsersEricDesktopSite.txt", "r", encoding="utf-8") SyntaxError: (unicode error) "unicodeescape" codec can"t decode bytes in position 2-4: truncated UXXXXXXXX escape (<pyshell#44>, line 1)

My last idea was, I thought it might have been the fact that Windows "translates" a few folders, such as the "users" folder, into Russian (though typing "users" is still the correct path), so I tried it in the Python31 folder. Still, no luck. Any ideas?

Python subprocess/Popen with a modified environment

I believe that running an external command with a slightly modified environment is a very common case. That"s how I tend to do it:

import subprocess, os my_env = os.environ my_env["PATH"] = "/usr/sbin:/sbin:" + my_env["PATH"] subprocess.Popen(my_command, env=my_env)

I"ve got a gut feeling that there"s a better way; does it look alright?

Cannot find module cv2 when using OpenCV

I have installed OpenCV on the Occidentalis operating system (a variant of Raspbian) on a Raspberry Pi, using jayrambhia"s script found here. It installed version 2.4.5.

When I try import cv2 in a Python program, I get the following message:

[email protected]~$ python cam.py Traceback (most recent call last) File "cam.py", line 1, in <module> import cv2 ImportError: No module named cv2

The file cv2.so is stored in /usr/local/lib/python2.7/site-packages/...

There are also folders in /usr/local/lib called python3.2 and python2.6, which could be a problem but I"m not sure.

Is this a path error perhaps? Any help is appreciated, I am new to Linux.

How to crop an image in OpenCV using Python

How can I crop images, like I"ve done before in PIL, using OpenCV.

Working example on PIL

im = Image.open("0.png").convert("L") im = im.crop((1, 1, 98, 33)) im.save("_0.png")

But how I can do it on OpenCV?

This is what I tried:

im = cv.imread("0.png", cv.CV_LOAD_IMAGE_GRAYSCALE) (thresh, im_bw) = cv.threshold(im, 128, 255, cv.THRESH_OTSU) im = cv.getRectSubPix(im_bw, (98, 33), (1, 1)) cv.imshow("Img", im) cv.waitKey(0)

But it doesn"t work.

I think I incorrectly used getRectSubPix. If this is the case, please explain how I can correctly use this function.

Answer #1

Since this question was asked in 2010, there has been real simplification in how to do simple multithreading with Python with map and pool.

The code below comes from an article/blog post that you should definitely check out (no affiliation) - Parallelism in one line: A Better Model for Day to Day Threading Tasks. I"ll summarize below - it ends up being just a few lines of code:

from multiprocessing.dummy import Pool as ThreadPool pool = ThreadPool(4) results = pool.map(my_function, my_array)

Which is the multithreaded version of:

results = [] for item in my_array: results.append(my_function(item))

Description

Map is a cool little function, and the key to easily injecting parallelism into your Python code. For those unfamiliar, map is something lifted from functional languages like Lisp. It is a function which maps another function over a sequence.

Map handles the iteration over the sequence for us, applies the function, and stores all of the results in a handy list at the end.

Implementation

Parallel versions of the map function are provided by two libraries:multiprocessing, and also its little known, but equally fantastic step child:multiprocessing.dummy.

multiprocessing.dummy is exactly the same as multiprocessing module, but uses threads instead (an important distinction - use multiple processes for CPU-intensive tasks; threads for (and during) I/O):

multiprocessing.dummy replicates the API of multiprocessing, but is no more than a wrapper around the threading module.

import urllib2 from multiprocessing.dummy import Pool as ThreadPool urls = [ "http://www.python.org", "http://www.python.org/about/", "http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html", "http://www.python.org/doc/", "http://www.python.org/download/", "http://www.python.org/getit/", "http://www.python.org/community/", "https://wiki.python.org/moin/", ] # Make the Pool of workers pool = ThreadPool(4) # Open the URLs in their own threads # and return the results results = pool.map(urllib2.urlopen, urls) # Close the pool and wait for the work to finish pool.close() pool.join()

And the timing results:

Single thread: 14.4 seconds 4 Pool: 3.1 seconds 8 Pool: 1.4 seconds 13 Pool: 1.3 seconds

Passing multiple arguments (works like this only in Python 3.3 and later):

To pass multiple arrays:

results = pool.starmap(function, zip(list_a, list_b))

Or to pass a constant and an array:

results = pool.starmap(function, zip(itertools.repeat(constant), list_a))

If you are using an earlier version of Python, you can pass multiple arguments via this workaround).

(Thanks to user136036 for the helpful comment.)

Answer #2

os.listdir() - list in the current directory

With listdir in os module you get the files and the folders in the current dir

import os arr = os.listdir() print(arr) >>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]

Looking in a directory

arr = os.listdir("c:\files")

glob from glob

with glob you can specify a type of file to list like this

import glob txtfiles = [] for file in glob.glob("*.txt"): txtfiles.append(file)

glob in a list comprehension

mylist = [f for f in glob.glob("*.txt")]

get the full path of only files in the current directory

import os from os import listdir from os.path import isfile, join cwd = os.getcwd() onlyfiles = [os.path.join(cwd, f) for f in os.listdir(cwd) if os.path.isfile(os.path.join(cwd, f))] print(onlyfiles) ["G:\getfilesname\getfilesname.py", "G:\getfilesname\example.txt"]

Getting the full path name with os.path.abspath

You get the full path in return

import os files_path = [os.path.abspath(x) for x in os.listdir()] print(files_path) ["F:\documentiapplications.txt", "F:\documenticollections.txt"]

Walk: going through sub directories

os.walk returns the root, the directories list and the files list, that is why I unpacked them in r, d, f in the for loop; it, then, looks for other files and directories in the subfolders of the root and so on until there are no subfolders.

import os # Getting the current work directory (cwd) thisdir = os.getcwd() # r=root, d=directories, f = files for r, d, f in os.walk(thisdir): for file in f: if file.endswith(".docx"): print(os.path.join(r, file))

os.listdir(): get files in the current directory (Python 2)

In Python 2, if you want the list of the files in the current directory, you have to give the argument as "." or os.getcwd() in the os.listdir method.

import os arr = os.listdir(".") print(arr) >>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]

To go up in the directory tree

# Method 1 x = os.listdir("..") # Method 2 x= os.listdir("/")

Get files: os.listdir() in a particular directory (Python 2 and 3)

import os arr = os.listdir("F:\python") print(arr) >>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]

Get files of a particular subdirectory with os.listdir()

import os x = os.listdir("./content")

os.walk(".") - current directory

import os arr = next(os.walk("."))[2] print(arr) >>> ["5bs_Turismo1.pdf", "5bs_Turismo1.pptx", "esperienza.txt"]

next(os.walk(".")) and os.path.join("dir", "file")

import os arr = [] for d,r,f in next(os.walk("F:\_python")): for file in f: arr.append(os.path.join(r,file)) for f in arr: print(files) >>> F:\_python\dict_class.py >>> F:\_python\programmi.txt

next(os.walk("F:\") - get the full path - list comprehension

[os.path.join(r,file) for r,d,f in next(os.walk("F:\_python")) for file in f] >>> ["F:\_python\dict_class.py", "F:\_python\programmi.txt"]

os.walk - get full path - all files in sub dirs**

x = [os.path.join(r,file) for r,d,f in os.walk("F:\_python") for file in f] print(x) >>> ["F:\_python\dict.py", "F:\_python\progr.txt", "F:\_python\readl.py"]

os.listdir() - get only txt files

arr_txt = [x for x in os.listdir() if x.endswith(".txt")] print(arr_txt) >>> ["work.txt", "3ebooks.txt"]

Using glob to get the full path of the files

If I should need the absolute path of the files:

from path import path from glob import glob x = [path(f).abspath() for f in glob("F:\*.txt")] for f in x: print(f) >>> F:acquistionline.txt >>> F:acquisti_2018.txt >>> F:ootstrap_jquery_ecc.txt

Using os.path.isfile to avoid directories in the list

import os.path listOfFiles = [f for f in os.listdir() if os.path.isfile(f)] print(listOfFiles) >>> ["a simple game.py", "data.txt", "decorator.py"]

Using pathlib from Python 3.4

import pathlib flist = [] for p in pathlib.Path(".").iterdir(): if p.is_file(): print(p) flist.append(p) >>> error.PNG >>> exemaker.bat >>> guiprova.mp3 >>> setup.py >>> speak_gui2.py >>> thumb.PNG

With list comprehension:

flist = [p for p in pathlib.Path(".").iterdir() if p.is_file()]

Alternatively, use pathlib.Path() instead of pathlib.Path(".")

Use glob method in pathlib.Path()

import pathlib py = pathlib.Path().glob("*.py") for file in py: print(file) >>> stack_overflow_list.py >>> stack_overflow_list_tkinter.py

Get all and only files with os.walk

import os x = [i[2] for i in os.walk(".")] y=[] for t in x: for f in t: y.append(f) print(y) >>> ["append_to_list.py", "data.txt", "data1.txt", "data2.txt", "data_180617", "os_walk.py", "READ2.py", "read_data.py", "somma_defaltdic.py", "substitute_words.py", "sum_data.py", "data.txt", "data1.txt", "data_180617"]

Get only files with next and walk in a directory

import os x = next(os.walk("F://python"))[2] print(x) >>> ["calculator.bat","calculator.py"]

Get only directories with next and walk in a directory

import os next(os.walk("F://python"))[1] # for the current dir use (".") >>> ["python3","others"]

Get all the subdir names with walk

for r,d,f in os.walk("F:\_python"): for dirs in d: print(dirs) >>> .vscode >>> pyexcel >>> pyschool.py >>> subtitles >>> _metaprogramming >>> .ipynb_checkpoints

os.scandir() from Python 3.5 and greater

import os x = [f.name for f in os.scandir() if f.is_file()] print(x) >>> ["calculator.bat","calculator.py"] # Another example with scandir (a little variation from docs.python.org) # This one is more efficient than os.listdir. # In this case, it shows the files only in the current directory # where the script is executed. import os with os.scandir() as i: for entry in i: if entry.is_file(): print(entry.name) >>> ebookmaker.py >>> error.PNG >>> exemaker.bat >>> guiprova.mp3 >>> setup.py >>> speakgui4.py >>> speak_gui2.py >>> speak_gui3.py >>> thumb.PNG

Examples:

Ex. 1: How many files are there in the subdirectories?

In this example, we look for the number of files that are included in all the directory and its subdirectories.

import os def count(dir, counter=0): "returns number of files in dir and subdirs" for pack in os.walk(dir): for f in pack[2]: counter += 1 return dir + " : " + str(counter) + "files" print(count("F:\python")) >>> "F:\python" : 12057 files"

Ex.2: How to copy all files from a directory to another?

A script to make order in your computer finding all files of a type (default: pptx) and copying them in a new folder.

import os import shutil from path import path destination = "F:\file_copied" # os.makedirs(destination) def copyfile(dir, filetype="pptx", counter=0): "Searches for pptx (or other - pptx is the default) files and copies them" for pack in os.walk(dir): for f in pack[2]: if f.endswith(filetype): fullpath = pack[0] + "\" + f print(fullpath) shutil.copy(fullpath, destination) counter += 1 if counter > 0: print("-" * 30) print(" ==> Found in: `" + dir + "` : " + str(counter) + " files ") for dir in os.listdir(): "searches for folders that starts with `_`" if dir[0] == "_": # copyfile(dir, filetype="pdf") copyfile(dir, filetype="txt") >>> _compiti18Compito Contabilit√† 1conti.txt >>> _compiti18Compito Contabilit√† 1modula4.txt >>> _compiti18Compito Contabilit√† 1moduloa4.txt >>> ------------------------ >>> ==> Found in: `_compiti18` : 3 files

Ex. 3: How to get all the files in a txt file

In case you want to create a txt file with all the file names:

import os mylist = "" with open("filelist.txt", "w", encoding="utf-8") as file: for eachfile in os.listdir(): mylist += eachfile + " " file.write(mylist)

Example: txt with all the files of an hard drive

""" We are going to save a txt file with all the files in your directory. We will use the function walk() """ import os # see all the methods of os # print(*dir(os), sep=", ") listafile = [] percorso = [] with open("lista_file.txt", "w", encoding="utf-8") as testo: for root, dirs, files in os.walk("D:\"): for file in files: listafile.append(file) percorso.append(root + "\" + file) testo.write(file + " ") listafile.sort() print("N. of files", len(listafile)) with open("lista_file_ordinata.txt", "w", encoding="utf-8") as testo_ordinato: for file in listafile: testo_ordinato.write(file + " ") with open("percorso.txt", "w", encoding="utf-8") as file_percorso: for file in percorso: file_percorso.write(file + " ") os.system("lista_file.txt") os.system("lista_file_ordinata.txt") os.system("percorso.txt")

All the file of C: in one text file

This is a shorter version of the previous code. Change the folder where to start finding the files if you need to start from another position. This code generate a 50 mb on text file on my computer with something less then 500.000 lines with files with the complete path.

import os with open("file.txt", "w", encoding="utf-8") as filewrite: for r, d, f in os.walk("C:\"): for file in f: filewrite.write(f"{r + file} ")

How to write a file with all paths in a folder of a type

With this function you can create a txt file that will have the name of a type of file that you look for (ex. pngfile.txt) with all the full path of all the files of that type. It can be useful sometimes, I think.

import os def searchfiles(extension=".ttf", folder="H:\"): "Create a txt file with all the file of a type" with open(extension[1:] + "file.txt", "w", encoding="utf-8") as filewrite: for r, d, f in os.walk(folder): for file in f: if file.endswith(extension): filewrite.write(f"{r + file} ") # looking for png file (fonts) in the hard disk H: searchfiles(".png", "H:\") >>> H:4bs_18Dolphins5.png >>> H:4bs_18Dolphins6.png >>> H:4bs_18Dolphins7.png >>> H:5_18marketing htmlassetsimageslogo2.png >>> H:7z001.png >>> H:7z002.png

(New) Find all files and open them with tkinter GUI

I just wanted to add in this 2019 a little app to search for all files in a dir and be able to open them by doubleclicking on the name of the file in the list.

import tkinter as tk import os def searchfiles(extension=".txt", folder="H:\"): "insert all files in the listbox" for r, d, f in os.walk(folder): for file in f: if file.endswith(extension): lb.insert(0, r + "\" + file) def open_file(): os.startfile(lb.get(lb.curselection()[0])) root = tk.Tk() root.geometry("400x400") bt = tk.Button(root, text="Search", command=lambda:searchfiles(".png", "H:\")) bt.pack() lb = tk.Listbox(root) lb.pack(fill="both", expand=1) lb.bind("<Double-Button>", lambda x: open_file()) root.mainloop()

Answer #3

I just used the following which was quite simple. First open a console then cd to where you"ve downloaded your file like some-package.whl and use

pip install some-package.whl

Note: if pip.exe is not recognized, you may find it in the "Scripts" directory from where python has been installed. If pip is not installed, this page can help: How do I install pip on Windows?

Note: for clarification
If you copy the *.whl file to your local drive (ex. C:some-dirsome-file.whl) use the following command line parameters --

pip install C:/some-dir/some-file.whl

Answer #4

This is the behaviour to adopt when the referenced object is deleted. It is not specific to Django; this is an SQL standard. Although Django has its own implementation on top of SQL. (1)

There are seven possible actions to take when such event occurs:

CASCADE: When the referenced object is deleted, also delete the objects that have references to it (when you remove a blog post for instance, you might want to delete comments as well). SQL equivalent: CASCADE.

PROTECT: Forbid the deletion of the referenced object. To delete it you will have to delete all objects that reference it manually. SQL equivalent: RESTRICT.

RESTRICT: (introduced in Django 3.1) Similar behavior as PROTECT that matches SQL"s RESTRICT more accurately. (See django documentation example)

SET_NULL: Set the reference to NULL (requires the field to be nullable). For instance, when you delete a User, you might want to keep the comments he posted on blog posts, but say it was posted by an anonymous (or deleted) user. SQL equivalent: SET NULL.

SET_DEFAULT: Set the default value. SQL equivalent: SET DEFAULT.

SET(...): Set a given value. This one is not part of the SQL standard and is entirely handled by Django.

DO_NOTHING: Probably a very bad idea since this would create integrity issues in your database (referencing an object that actually doesn"t exist). SQL equivalent: NO ACTION. (2)

Source: Django documentation

See also the documentation of PostgreSQL for instance.

In most cases, CASCADE is the expected behaviour, but for every ForeignKey, you should always ask yourself what is the expected behaviour in this situation. PROTECT and SET_NULL are often useful. Setting CASCADE where it should not, can potentially delete all of your database in cascade, by simply deleting a single user.

Additional note to clarify cascade direction

It"s funny to notice that the direction of the CASCADE action is not clear to many people. Actually, it"s funny to notice that only the CASCADE action is not clear. I understand the cascade behavior might be confusing, however you must think that it is the same direction as any other action. Thus, if you feel that CASCADE direction is not clear to you, it actually means that on_delete behavior is not clear to you.

In your database, a foreign key is basically represented by an integer field which value is the primary key of the foreign object. Let"s say you have an entry comment_A, which has a foreign key to an entry article_B. If you delete the entry comment_A, everything is fine. article_B used to live without comment_A and don"t bother if it"s deleted. However, if you delete article_B, then comment_A panics! It never lived without article_B and needs it, and it"s part of its attributes (article=article_B, but what is article_B???). This is where on_delete steps in, to determine how to resolve this integrity error, either by saying:

"No! Please! Don"t! I can"t live without you!" (which is said PROTECT or RESTRICT in Django/SQL)

"All right, if I"m not yours, then I"m nobody"s" (which is said SET_NULL)

"Good bye world, I can"t live without article_B" and commit suicide (this is the CASCADE behavior).

"It"s OK, I"ve got spare lover, and I"ll reference article_C from now" (SET_DEFAULT, or even SET(...)).

"I can"t face reality, and I"ll keep calling your name even if that"s the only thing left to me!" (DO_NOTHING)

I hope it makes cascade direction clearer. :)

Footnotes

(1) Django has its own implementation on top of SQL. And, as mentioned by @JoeMjr2 in the comments below, Django will not create the SQL constraints. If you want the constraints to be ensured by your database (for instance, if your database is used by another application, or if you hang in the database console from time to time), you might want to set the related constraints manually yourself. There is an open ticket to add support for database-level on delete constrains in Django.

(2) Actually, there is one case where DO_NOTHING can be useful: If you want to skip Django"s implementation and implement the constraint yourself at the database-level.

Answer #5

Running brew reinstall [email protected] didn"t work for my existing Python 2.7 virtual environments. Inside them there were still ERROR:root:code for hash sha1 was not found errors.

I encountered this problem after I ran brew upgrade openssl. And here"s the fix:

$ ls /usr/local/Cellar/openssl

...which shows

1.0.2t

According to the existing version, run:

$ brew switch openssl 1.0.2t

...which shows

Cleaning /usr/local/Cellar/openssl/1.0.2t Opt link created for /usr/local/Cellar/openssl/1.0.2t

After that, run the following command in a Python 2.7 virtualenv:

(my-venv) $ python -c "import hashlib;m=hashlib.md5();print(m.hexdigest())"

...which shows

d41d8cd98f00b204e9800998ecf8427e

No more errors.

Answer #6

You opened the file in binary mode:

with open(fname, "rb") as f:

This means that all data read from the file is returned as bytes objects, not str. You cannot then use a string in a containment test:

if "some-pattern" in tmp: continue

You"d have to use a bytes object to test against tmp instead:

if b"some-pattern" in tmp: continue

or open the file as a textfile instead by replacing the "rb" mode with "r".

Answer #7

‚ö°Ô∏è TL;DR ‚Äî One line solution.

All you have to do is:

sudo easy_install pip

2019: ‚ö†Ô∏èeasy_install has been deprecated. Check Method #2 below for preferred installation!

Details:

‚ö°Ô∏è OK, I read the solutions given above, but here"s an EASY solution to install pip.

MacOS comes with Python installed. But to make sure that you have Python installed open the terminal and run the following command.

python --version

If this command returns a version number that means Python exists. Which also means that you already have access to easy_install considering you are using macOS/OSX.

‚ÑπÔ∏è Now, all you have to do is run the following command.

sudo easy_install pip

After that, pip will be installed and you"ll be able to use it for installing other packages.

Let me know if you have any problems installing pip this way.

Cheers!

P.S. I ended up blogging a post about it. QuickTip: How Do I Install pip on macOS or OS X?

‚úÖ UPDATE (Jan 2019): METHOD #2: Two line solution ‚Äî

easy_install has been deprecated. Please use get-pip.py instead.

First of all download the get-pip file

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py

Now run this file to install pip

python get-pip.py

That should do it.

Another gif you said? Here ya go!

Answer #8

I noticed that every now and then I need to Google fopen all over again, just to build a mental image of what the primary differences between the modes are. So, I thought a diagram will be faster to read next time. Maybe someone else will find that helpful too.

Answer #9

It helps to install a python package foo on your machine (can also be in virtualenv) so that you can import the package foo from other projects and also from [I]Python prompts.

It does the similar job of pip, easy_install etc.,

Using setup.py

Let"s start with some definitions:

Package - A folder/directory that contains __init__.py file.
Module - A valid python file with .py extension.
Distribution - How one package relates to other packages and modules.

Let"s say you want to install a package named foo. Then you do,

$ git clone https://github.com/user/foo $ cd foo $ python setup.py install

Instead, if you don"t want to actually install it but still would like to use it. Then do,

$ python setup.py develop

This command will create symlinks to the source directory within site-packages instead of copying things. Because of this, it is quite fast (particularly for large packages).

Creating setup.py

If you have your package tree like,

foo ‚îú‚îÄ‚îÄ foo ‚îÇ¬†¬† ‚îú‚îÄ‚îÄ data_struct.py ‚îÇ¬†¬† ‚îú‚îÄ‚îÄ __init__.py ‚îÇ¬†¬† ‚îî‚îÄ‚îÄ internals.py ‚îú‚îÄ‚îÄ README ‚îú‚îÄ‚îÄ requirements.txt ‚îî‚îÄ‚îÄ setup.py

Then, you do the following in your setup.py script so that it can be installed on some machine:

from setuptools import setup setup( name="foo", version="1.0", description="A useful module", author="Man Foo", author_email="[email protected]", packages=["foo"], #same as name install_requires=["wheel", "bar", "greek"], #external packages as dependencies )

Instead, if your package tree is more complex like the one below:

foo ‚îú‚îÄ‚îÄ foo ‚îÇ¬†¬† ‚îú‚îÄ‚îÄ data_struct.py ‚îÇ¬†¬† ‚îú‚îÄ‚îÄ __init__.py ‚îÇ¬†¬† ‚îî‚îÄ‚îÄ internals.py ‚îú‚îÄ‚îÄ README ‚îú‚îÄ‚îÄ requirements.txt ‚îú‚îÄ‚îÄ scripts ‚îÇ¬†¬† ‚îú‚îÄ‚îÄ cool ‚îÇ¬†¬† ‚îî‚îÄ‚îÄ skype ‚îî‚îÄ‚îÄ setup.py

Then, your setup.py in this case would be like:

from setuptools import setup setup( name="foo", version="1.0", description="A useful module", author="Man Foo", author_email="[email protected]", packages=["foo"], #same as name install_requires=["wheel", "bar", "greek"], #external packages as dependencies scripts=[ "scripts/cool", "scripts/skype", ] )

Add more stuff to (setup.py) & make it decent:

from setuptools import setup with open("README", "r") as f: long_description = f.read() setup( name="foo", version="1.0", description="A useful module", license="MIT", long_description=long_description, author="Man Foo", author_email="[email protected]", url="http://www.foopackage.com/", packages=["foo"], #same as name install_requires=["wheel", "bar", "greek"], #external packages as dependencies scripts=[ "scripts/cool", "scripts/skype", ] )

The long_description is used in pypi.org as the README description of your package.

And finally, you"re now ready to upload your package to PyPi.org so that others can install your package using pip install yourpackage.

At this point there are two options.

publish in the temporary test.pypi.org server to make oneself familiarize with the procedure, and then publish it on the permanent pypi.org server for the public to use your package.

publish straight away on the permanent pypi.org server, if you are already familiar with the procedure and have your user credentials (e.g., username, password, package name)

Once your package name is registered in pypi.org, nobody can claim or use it. Python packaging suggests the twine package for uploading purposes (of your package to PyPi). Thus,

(1) the first step is to locally build the distributions using:

# prereq: wheel (pip install wheel) $ python setup.py sdist bdist_wheel

(2) then using twine for uploading either to test.pypi.org or pypi.org:

$ twine upload --repository testpypi dist/* username: *** password: ***

It will take few minutes for the package to appear on test.pypi.org. Once you"re satisfied with it, you can then upload your package to the real & permanent index of pypi.org simply with:

$ twine upload dist/*

Optionally, you can also sign the files in your package with a GPG by:

$ twine upload dist/* --sign

Bonus Reading:

See a sample setup.py from a real project here: torchvision-setup.py

PEP 517, setuptools

why twine? using twine

Answer #10

tl;dr / quick fix

Don"t decode/encode willy nilly

Don"t assume your strings are UTF-8 encoded

Try to convert strings to Unicode strings as soon as possible in your code

Fix your locale: How to solve UnicodeDecodeError in Python 3.6?

Don"t be tempted to use quick reload hacks

Unicode Zen in Python 2.x - The Long Version

Without seeing the source it"s difficult to know the root cause, so I"ll have to speak generally.

UnicodeDecodeError: "ascii" codec can"t decode byte generally happens when you try to convert a Python 2.x str that contains non-ASCII to a Unicode string without specifying the encoding of the original string.

In brief, Unicode strings are an entirely separate type of Python string that does not contain any encoding. They only hold Unicode point codes and therefore can hold any Unicode point from across the entire spectrum. Strings contain encoded text, beit UTF-8, UTF-16, ISO-8895-1, GBK, Big5 etc. Strings are decoded to Unicode and Unicodes are encoded to strings. Files and text data are always transferred in encoded strings.

The Markdown module authors probably use unicode() (where the exception is thrown) as a quality gate to the rest of the code - it will convert ASCII or re-wrap existing Unicodes strings to a new Unicode string. The Markdown authors can"t know the encoding of the incoming string so will rely on you to decode strings to Unicode strings before passing to Markdown.

Unicode strings can be declared in your code using the u prefix to strings. E.g.

>>> my_u = u"my √ºnic√¥d√© strƒØng" >>> type(my_u) <type "unicode">

Unicode strings may also come from file, databases and network modules. When this happens, you don"t need to worry about the encoding.

Gotchas

Conversion from str to Unicode can happen even when you don"t explicitly call unicode().

The following scenarios cause UnicodeDecodeError exceptions:

# Explicit conversion without encoding unicode("‚Ç¨") # New style format string into Unicode string # Python will try to convert value string to Unicode first u"The currency is: {}".format("‚Ç¨") # Old style format string into Unicode string # Python will try to convert value string to Unicode first u"The currency is: %s" % "‚Ç¨" # Append string to Unicode # Python will try to convert string to Unicode first u"The currency is: " + "‚Ç¨"

Examples

In the following diagram, you can see how the word caf√© has been encoded in either "UTF-8" or "Cp1252" encoding depending on the terminal type. In both examples, caf is just regular ascii. In UTF-8, √© is encoded using two bytes. In "Cp1252", √© is 0xE9 (which is also happens to be the Unicode point value (it"s no coincidence)). The correct decode() is invoked and conversion to a Python Unicode is successfull:

In this diagram, decode() is called with ascii (which is the same as calling unicode() without an encoding given). As ASCII can"t contain bytes greater than 0x7F, this will throw a UnicodeDecodeError exception:

The Unicode Sandwich

It"s good practice to form a Unicode sandwich in your code, where you decode all incoming data to Unicode strings, work with Unicodes, then encode to strs on the way out. This saves you from worrying about the encoding of strings in the middle of your code.

Input / Decode

Source code

If you need to bake non-ASCII into your source code, just create Unicode strings by prefixing the string with a u. E.g.

u"Z√ºrich"

To allow Python to decode your source code, you will need to add an encoding header to match the actual encoding of your file. For example, if your file was encoded as "UTF-8", you would use:

# encoding: utf-8

This is only necessary when you have non-ASCII in your source code.

Files

Usually non-ASCII data is received from a file. The io module provides a TextWrapper that decodes your file on the fly, using a given encoding. You must use the correct encoding for the file - it can"t be easily guessed. For example, for a UTF-8 file:

import io with io.open("my_utf8_file.txt", "r", encoding="utf-8") as my_file: my_unicode_string = my_file.read()

my_unicode_string would then be suitable for passing to Markdown. If a UnicodeDecodeError from the read() line, then you"ve probably used the wrong encoding value.

CSV Files

The Python 2.7 CSV module does not support non-ASCII characters üò©. Help is at hand, however, with https://pypi.python.org/pypi/backports.csv.

Use it like above but pass the opened file to it:

from backports import csv import io with io.open("my_utf8_file.txt", "r", encoding="utf-8") as my_file: for row in csv.reader(my_file): yield row

Databases

Most Python database drivers can return data in Unicode, but usually require a little configuration. Always use Unicode strings for SQL queries.
MySQL
In the connection string add:

charset="utf8", use_unicode=True

E.g.

>>> db = MySQLdb.connect(host="localhost", user="root", passwd="passwd", db="sandbox", use_unicode=True, charset="utf8")
PostgreSQL
Add:

psycopg2.extensions.register_type(psycopg2.extensions.UNICODE) psycopg2.extensions.register_type(psycopg2.extensions.UNICODEARRAY)

HTTP

Web pages can be encoded in just about any encoding. The Content-type header should contain a charset field to hint at the encoding. The content can then be decoded manually against this value. Alternatively, Python-Requests returns Unicodes in response.text.

Manually

If you must decode strings manually, you can simply do my_string.decode(encoding), where encoding is the appropriate encoding. Python 2.x supported codecs are given here: Standard Encodings. Again, if you get UnicodeDecodeError then you"ve probably got the wrong encoding.

The meat of the sandwich

Work with Unicodes as you would normal strs.

Output

stdout / printing

print writes through the stdout stream. Python tries to configure an encoder on stdout so that Unicodes are encoded to the console"s encoding. For example, if a Linux shell"s locale is en_GB.UTF-8, the output will be encoded to UTF-8. On Windows, you will be limited to an 8bit code page.

An incorrectly configured console, such as corrupt locale, can lead to unexpected print errors. PYTHONIOENCODING environment variable can force the encoding for stdout.

Files

Just like input, io.open can be used to transparently convert Unicodes to encoded byte strings.

Database

The same configuration for reading will allow Unicodes to be written directly.

Python 3

Python 3 is no more Unicode capable than Python 2.x is, however it is slightly less confused on the topic. E.g the regular str is now a Unicode string and the old str is now bytes.

The default encoding is UTF-8, so if you .decode() a byte string without giving an encoding, Python 3 uses UTF-8 encoding. This probably fixes 50% of people"s Unicode problems.

Further, open() operates in text mode by default, so returns decoded str (Unicode ones). The encoding is derived from your locale, which tends to be UTF-8 on Un*x systems or an 8-bit code page, such as windows-1251, on Windows boxes.

Why you shouldn"t use sys.setdefaultencoding("utf8")

It"s a nasty hack (there"s a reason you have to use reload) that will only mask problems and hinder your migration to Python 3.x. Understand the problem, fix the root cause and enjoy Unicode zen. See Why should we NOT use sys.setdefaultencoding("utf-8") in a py script? for further details