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Tag: opencv

Image histogram

A histogram is collected counts of data organized into a set of bins. Every bin shows the frequency. OpenCV can generate histograms for both color and gray scale images. You may want to use histograms for computer vision tasks.


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Histogram example
Given an image we can generate a histogram for the blue, green and red values.

Histogram_Calculation Histogram Calculation

We use the function cv.CalcHist(image, channel, mask, histSize, range)

Parameters:


  • image:  should be in brackets,  the source image of type uint8 or float32

  • channel:  the color channel to select. for grayscale use [0]. color image has blue, green and red channels

  • mask:  None if you want a histogram of the full image, otherwise a region.

  • histSize:  the number of bins

  • range:  color range:


Histogram for a color image:

# draw histogram in python.
import cv2
import numpy as np

img = cv2.imread('image.jpg')
h = np.zeros((300,256,3))

bins = np.arange(256).reshape(256,1)
color = [ (255,0,0),(0,255,0),(0,0,255) ]

for ch, col in enumerate(color):
hist_item = cv2.calcHist([img],[ch],None,[256],[0,255])
cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX)
hist=np.int32(np.around(hist_item))
pts = np.column_stack((bins,hist))
cv2.polylines(h,[pts],False,col)

h=np.flipud(h)

cv2.imshow('colorhist',h)
cv2.waitKey(0)

 

Image data and operations

OpenCV (cv2) can be used to extract data from images and do operations on them. We demonstrate some examples of that below:

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Image properties
We can extract the width, height and color depth using the code below:

import cv2
import numpy as np

# read image into matrix.
m = cv2.imread("python.png")

# get image properties.
h,w,bpp = np.shape(m)

# print image properties.
print "width: " + str(w)
print "height: " + str(h)
print "bpp: " + str(bpp)

Access pixel data
We can access the pixel data of an image directly using the matrix, example:

import cv2
import numpy as np

# read image into matrix.
m = cv2.imread("python.png")

# get image properties.
h,w,bpp = np.shape(m)

# print pixel value
y = 1
x = 1
print m[y][x]

To iterate over all pixels in the image you can use:

import cv2
import numpy as np

# read image into matrix.
m = cv2.imread("python.png")

# get image properties.
h,w,bpp = np.shape(m)

# iterate over the entire image.
for py in range(0,h):
for px in range(0,w):
print m[py][px]

Image manipulation
You can modify the pixels and pixel channels (r,g,b) directly. In the example below we remove one color channel:

import cv2
import numpy as np

# read image into matrix.
m = cv2.imread("python.png")

# get image properties.
h,w,bpp = np.shape(m)

# iterate over the entire image.
for py in range(0,h):
for px in range(0,w):
m[py][px][0] = 0

# display image
cv2.imshow('matrix', m)
cv2.waitKey(0)

To change the entire image, you’ll have to change all channels:   m[py][px][0], m[py][px][1], m[py][px][2].

Save image
You can save a modified image to the disk using:

cv2.imwrite('filename.png',m)

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Object detection with templates

Template matching is a technique for finding areas of an image that are similar to a patch (template).
Its application may be robotics or manufacturing.

Related courses:
Python for Computer Vision with OpenCV and Deep Learning
Master Computer Vision OpenCV3 in Python & Machine Learning

Introduction
A patch is a small image with certain features. The goal of template matching is to find the patch/template in an image.

<caption id=”attachment_4826” align=”alignright” width=”948”]template matching opencv Template matching with OpenCV and Python. Template (left), result image (right)

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To find them we need both:


  • Source Image (S) : The space to find the matches in

  • Template Image (T) : The template image


The template image T is slided over the source image S (moved over the source image), and the program tries to find matches using statistics.

Template matching example


Lets have a look at the code:

import numpy as np
import cv2

image = cv2.imread('photo.jpg')
template = cv2.imread('template.jpg')

# resize images
image = cv2.resize(image, (0,0), fx=0.5, fy=0.5)
template = cv2.resize(template, (0,0), fx=0.5, fy=0.5)

# Convert to grayscale
imageGray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
templateGray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)

# Find template
result = cv2.matchTemplate(imageGray,templateGray, cv2.TM_CCOEFF)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
top_left = max_loc
h,w = templateGray.shape
bottom_right = (top_left[0] + w, top_left[1] + h)
cv2.rectangle(image,top_left, bottom_right,(0,0,255),4)

# Show result
cv2.imshow("Template", template)
cv2.imshow("Result", image)

cv2.moveWindow("Template", 10, 50);
cv2.moveWindow("Result", 150, 50);

cv2.waitKey(0)

Explanation


First we load both the source image and template image with imread().  We resize themand convert them to grayscale for faster detection:


image = cv2.imread('photo.jpg')
template = cv2.imread('template.jpg')
image = cv2.resize(image, (0,0), fx=0.5, fy=0.5)
template = cv2.resize(template, (0,0), fx=0.5, fy=0.5)
imageGray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
templateGray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)

We use the cv2.matchTemplate(image,template,method) method to find the most similar area in the image.  The third argument is the statistical method.

Template Matching Pick the right statistical method for your application. TM_CCOEFF (right), TM_SQDIFF(left)

This method has six matching methods: CV_TM_SQDIFF, CV_TM_SQDIFF_NORMED, CV_TM_CCORR, CV_TM_CCORR_NORMED, CV_TM_CCOEFF and CV_TM_CCOEFF_NORMED.
which are simply different statistical comparison methods

Finally, we get the rectangle variables and display the image.

Limitations


Template matching is not scale invariant nor is it rotation invariant. It is a very basic and straightforward method where we find the most correlating area. Thus, this method of object detection depends on the kind of application you want to build. For non scale and rotation changing input, this method works great.

You may like: Robotics or Car tracking with cascades.

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Face detection in Google Hangouts video

In this tutorial you will learn how to apply face detection with Python. As input video we will use a Google Hangouts video. There are tons of Google Hangouts videos around the web and in these videos the face is usually large enough for the software to detect the faces.

Detection of faces is achieved using the OpenCV (Open Computer Vision) library. The most common face detection method is to extract cascades. This technique is known to work well with face detection. You need to have the cascade files (included in OpenCV) in the same directory as your program.

Related course
Python for Computer Vision with OpenCV and Deep Learning
Master Computer Vision OpenCV3 in Python & Machine Learning

Video with Python OpenCV


To analyse the input video we extract each frame.  Each frame is shown for a brief period of time. Start with this basic program:

#! /usr/bin/python

import cv2

vc = cv2.VideoCapture('video.mp4')
c=1
fps = 24

if vc.isOpened():
rval , frame = vc.read()
else:
rval = False

while rval:
rval, frame = vc.read()
cv2.imshow("Result",frame)
cv2.waitKey(1000 / fps);
vc.release()

Upon execution you will see the video played without sound. (OpenCV does not support sound). Inside the while loop we have every video frame inside the variable frame. 

Face detection with OpenCV


We will display a rectangle on top of the face. To avoid flickering of the rectangle, we will show it at it latest known position if the face is not detected.

#! /usr/bin/python

import cv2

face_cascade = cv2.CascadeClassifier('lbpcascade_frontalface.xml')
vc = cv2.VideoCapture('video.mp4')

if vc.isOpened():
rval , frame = vc.read()
else:
rval = False

roi = [0,0,0,0]

while rval:
rval, frame = vc.read()

# resize frame for speed.
frame = cv2.resize(frame, (300,200))

# face detection.
faces = face_cascade.detectMultiScale(frame, 1.8, 2)
nfaces = 0
for (x,y,w,h) in faces:
cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2)
nfaces = nfaces + 1
roi = [x,y,w,h]

# undetected face, show old on position.
if nfaces == 0:
cv2.rectangle(frame,(roi[0],roi[1]),(roi[0]+roi[2],roi[1]+roi[3]),(0,0,255),2)

# show result
cv2.imshow("Result",frame)
cv2.waitKey(1);
vc.release()

In this program we simply assumed there is one face in the video screen. We reduced the size of the screen to speed up the processing time. This is fine in most cases because detection will work fine in lower resolutions.  If you want to execute the face detection in “real time”, keeping the computational cycle short is mandatory. An alternative to this implementation is to process first and display later.

A limitation of this technique is that it does not always detect faces and faces that are very small or occluded may not be detected. It may show false positives such as a bag detected as face.  This technique works quite well on certain type of input videos.

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Car tracking with cascades

Car Tracking with OpenCV Car Tracking with OpenCV

In this tutorial we will look at vehicle tracking using haar features. We have a haar cascade file trained on cars.

The program will detect regions of interest, classify them as cars and show rectangles around them.

Related courses:

Detecting with cascades


Lets start with the basic cascade detection program:

#! /usr/bin/python

import cv2

face_cascade = cv2.CascadeClassifier('cars.xml')
vc = cv2.VideoCapture('road.avi')

if vc.isOpened():
rval , frame = vc.read()
else:
rval = False

while rval:
rval, frame = vc.read()

# car detection.
cars = face_cascade.detectMultiScale(frame, 1.1, 2)

ncars = 0
for (x,y,w,h) in cars:
cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2)
ncars = ncars + 1

# show result
cv2.imshow("Result",frame)
cv2.waitKey(1);
vc.release()

This will detect cars in the screen but also noise and the screen will be jittering sometimes. To avoid all of these, we have to improve our car tracking algorithm.  We decided to come up with a simple solution.

Car tracking algorithm


For every frame:


  • Detect potential regions of interest

  • Filter detected regions based on vertical,horizontal similarity

  • If its a new region, add to the collection

  • Clear collection every 30 frames


 
Removing false positives
The mean square error function is used to remove false positives. We compare vertical and horizontal sides of the images. If the difference is to large or to small it cannot be a car.

ROI detection
A car may not be detected in every frame. If a new car is detected, its added to the collection.
We keep this collection for 30 frames, then clear it.


#! /usr/bin/python

import cv2
import numpy as np

def diffUpDown(img):
# compare top and bottom size of the image
# 1. cut image in two
# 2. flip the top side
# 3. resize to same size
# 4. compare difference
height, width, depth = img.shape
half = height/2
top = img[0:half, 0:width]
bottom = img[half:half+half, 0:width]
top = cv2.flip(top,1)
bottom = cv2.resize(bottom, (32, 64))
top = cv2.resize(top, (32, 64))
return ( mse(top,bottom) )

def diffLeftRight(img):
# compare left and right size of the image
# 1. cut image in two
# 2. flip the right side
# 3. resize to same size
# 4. compare difference
height, width, depth = img.shape
half = width/2
left = img[0:height, 0:half]
right = img[0:height, half:half + half-1]
right = cv2.flip(right,1)
left = cv2.resize(left, (32, 64))
right = cv2.resize(right, (32, 64))
return ( mse(left,right) )

def mse(imageA, imageB):
err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)
err /= float(imageA.shape[0] * imageA.shape[1])
return err

def isNewRoi(rx,ry,rw,rh,rectangles):
for r in rectangles:
if abs(r[0] - rx) &lt; 40 and abs(r[1] - ry) &lt; 40:
return False
return True

def detectRegionsOfInterest(frame, cascade):
scaleDown = 2
frameHeight, frameWidth, fdepth = frame.shape
# Resize
frame = cv2.resize(frame, (frameWidth/scaleDown, frameHeight/scaleDown))
frameHeight, frameWidth, fdepth = frame.shape

# haar detection.
cars = cascade.detectMultiScale(frame, 1.2, 1)

newRegions = []
minY = int(frameHeight*0.3)

# iterate regions of interest
for (x,y,w,h) in cars:
roi = [x,y,w,h]
roiImage = frame[y:y+h, x:x+w]

carWidth = roiImage.shape[0]
if y > minY:
diffX = diffLeftRight(roiImage)
diffY = round(diffUpDown(roiImage))
if diffX > 1600 and diffX < 3000 and diffY > 12000:
rx,ry,rw,rh = roi
newRegions.append( [rx*scaleDown,ry*scaleDown,rw*scaleDown,rh*scaleDown] )

return newRegions

def detectCars(filename):
rectangles = []
cascade = cv2.CascadeClassifier('cars.xml')
vc = cv2.VideoCapture(filename)

if vc.isOpened():
rval , frame = vc.read()
else:
rval = False

roi = [0,0,0,0]
frameCount = 0

while rval:
rval, frame = vc.read()
frameHeight, frameWidth, fdepth = frame.shape

newRegions = detectRegionsOfInterest(frame, cascade)
for region in newRegions:
if isNewRoi(region[0],region[1],region[2],region[3],rectangles):
rectangles.append(region)

for r in rectangles:
cv2.rectangle(frame,(r[0],r[1]),(r[0]+r[2],r[1]+r[3]),(0,0,255),3)

frameCount = frameCount + 1
if frameCount > 30:
frameCount = 0
rectangles = []

# show result
cv2.imshow("Result",frame)
cv2.waitKey(1);
vc.release()

detectCars('road.avi')


Final notes
The cascades are not  rotation invariant, scale and translation invariant. In addition, Detecting vehicles with haar cascades may work reasonably well, but there is gain with other algorithms (salient points).

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