Tips for Python beginners to use the Scikit-image example for themselves 2 Handle multiple files
Tips for Python beginners to use the Scikit-image example for themselves
Next, I will write the content for Python beginners to play with the example of Scikit-image with a slight modification.
Let's continue with Normalized Cut as an example.
Consider processing this example for different images. First of all, this example is shown as it is.
.py:plot_ncut.py
from skimage import data, io, segmentation, color
from skimage.future import graph
from matplotlib import pyplot as plt
img = data.coffee()
labels1 = segmentation.slic(img, compactness=30, n_segments=400)
out1 = color.label2rgb(labels1, img, kind='avg')
g = graph.rag_mean_color(img, labels1, mode='similarity')
labels2 = graph.cut_normalized(labels1, g)
out2 = color.label2rgb(labels2, img, kind='avg')
plt.figure()
io.imshow(out1)
plt.figure()
io.imshow(out2)
io.show()
** Hint: Learn how to create a function. ** **
If you want to process this process for various input images, Think about what to input, what to do, and what to return. For the time being, let's make the part to be processed for the img image a function. Japanese translation of Python standard documentation 4.6. Defining functions
An example after functionalization
.py:plot_ncut_ex1.py
from skimage import data, io, segmentation, color
from skimage.future import graph
from matplotlib import pyplot as plt
def plotNcut(img):
labels1 = segmentation.slic(img, compactness=30, n_segments=400)
out1 = color.label2rgb(labels1, img, kind='avg')
g = graph.rag_mean_color(img, labels1, mode='similarity')
labels2 = graph.cut_normalized(labels1, g)
out2 = color.label2rgb(labels2, img, kind='avg')
plt.figure()
io.imshow(out1)
plt.figure()
io.imshow(out2)
io.show()
img = data.coffee()
plotNcut(img)
How about that? For functionalization def function name (argument): Indented code
If you write like, you can make it a function for the time being. The fact that indentation is part of control syntax can be confusing to a complete beginner in Python. You don't need to write a return return value unless you return a value. ,
** Hint: Use for name in glob.glob ("* .jpg "): to work with multiple files. ** **
Script after rewriting
.py:plot_ncut_ex2.py
from skimage import data, io, segmentation, color
from skimage.future import graph
from matplotlib import pyplot as plt
def plotNcut(img):
labels1 = segmentation.slic(img, compactness=30, n_segments=400)
out1 = color.label2rgb(labels1, img, kind='avg')
g = graph.rag_mean_color(img, labels1, mode='similarity')
labels2 = graph.cut_normalized(labels1, g)
out2 = color.label2rgb(labels2, img, kind='avg')
plt.figure(1)
io.imshow(out1)
plt.figure(2)
io.imshow(out2)
io.show()
import cv2
import glob
for name in glob.glob("*.png "):
img = cv2.imread(name)
plotNcut(img)
cv2.waitKey(100)
The for statement is a control syntax for processing data such as lists one by one.
python
for name in names:
print name
And so on. Please see the following library for details. Python standard library 4.2. For statement
Python standard library glob — Unix-like pathname pattern expansion
Since the function is defined with def plotNcut (img) :, the description in the for statement is simple. cv2.waitKey (100) gives a waiting time to make the display easier to read.
** Hint: Let's take out the frame of the video and process it **
768x576.avi is the video data included in the OpenCV distribution. Let's run Normalized Cut using this video. OpenCV-Python has cv2.VideoCapture (), which can extract the image of each frame from the video. (If you give the camera number as an argument, you can import the image from the USB camera.)
First, let's check if the video playback is made with an existing script. OpenCV-Tutorials Getting Started with Videos If you try this and the video doesn't play, it's likely that cap.isOpend () is False from the beginning. A common situation is that the python interpreter has not found a DLL such as opencv_ffmpeg2411.dll.
Note Make sure proper versions of ffmpeg or gstreamer is installed. Sometimes, it is a headache to work with Video Capture mostly due to wrong installation of ffmpeg/gstreamer.
There is, so please check the solution on the web. (If you use OpenCV 2.4.11, openCV 2.4.11 cv2.pyd in (Python directory) /Lib/site-packages/ Replace with. Also, copy opencv_ffmpeg2411.dll and paste it in the place where the script is, so that ffmpeg has a path. )
Script after rewriting
python
from skimage import data, io, segmentation, color
from skimage.future import graph
from matplotlib import pyplot as plt
import cv2
def plotNcut(img):
labels1 = segmentation.slic(img, compactness=30, n_segments=200)
out1 = color.label2rgb(labels1, img, kind='avg')
g = graph.rag_mean_color(img, labels1, mode='similarity')
labels2 = graph.cut_normalized(labels1, g)
out2 = color.label2rgb(labels2, img, kind='avg')
return out1, out2
name = "768x576.avi"
cap = cv2.VideoCapture(name)
i = -1
while cap.isOpened():
i += 1
ret, img = cap.read()
if i % 10 != 0:
continue
if ret != True:
break
[h, w] = img.shape[:2]
img = cv2.resize(img, (w/2, h/2))
out1, out2 = plotNcut(img)
cv2.imshow("img", out1)
cv2.waitKey(100)
print i
Notice the part related to the variable cap set by cap = cv2.VideoCapture (name). The methods of isOpened () and read () are OpenCV-Python Tutorials Getting Started with Videos As it is written in the example of.
An example of input image and result image
Challenge: Using cv2.VideoCapture (), Edge operators Let's write a program that processes the example of.
development
In addition to glob.glob (file pattern), there is os.walk () as a way to find the file to process. You can search for files while moving directories in a hierarchical directory structure. (This is a feature I often use) os.walk(top, topdown=True, onerror=None, followlinks=False) Python standard library os — miscellaneous operating system interfaces
** Addendum: File list shuffle **
Another pattern for processing multiple files is to process from a file list. Depending on the process, it may be necessary to select the file with a random number. In the following example, shuffle the lines from the file list and select 100 lines from it. Can be processed.
python
lines = open("filelist.txt", "rt").readlines()
random.shuffle(lines)
for line in lines[:100]:
p = line.strip()
print p
If you have Python 3.4 or later, you should throw away os.path and use pathlib
Recommended Posts