Develop Reference

How to give user inputs to detect a specific color from video in opencv?

I have written a code in python to detect a red color in OpenCV. I am getting proper output,but I want to give user input in the code to detect a specific color. Eg: If I will give blue as a user input, it should show only blue on the output. I also want to add few attributes as a output, such as time of that object get detected and location of the live video. I am new to python and opencv, it would be great if I will get some guidance.
My existing code is as below:
import cv2
import numpy as np
# Capture the input frame from webcam
def get_frame(cap, scaling_factor):
# Capture the frame from video capture object
ret, frame = cap.read()
# Resize the input frame
frame = cv2.resize(frame, None, fx=scaling_factor,
fy=scaling_factor, interpolation=cv2.INTER_AREA)
return frame
if __name__=='__main__':
cap = cv2.VideoCapture(0)
scaling_factor = 0.5
# Iterate until the user presses ESC key
while True:
frame = get_frame(cap, scaling_factor)
# Convert the HSV colorspace
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# Define 'blue' range in HSV colorspace
lower = np.array([60,100,100])
upper = np.array([180,255,255])
# Threshold the HSV image to get only blue color
mask = cv2.inRange(hsv, lower, upper)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(frame, frame, mask=mask)
res = cv2.medianBlur(res, 5)
cv2.imshow('Original image', frame)
cv2.imshow('Color Detector', res)
# Check if the user pressed ESC key
c = cv2.waitKey(5)
if c == 27:
break
cv2.destroyAllWindows()

Your code seems to work correctly. Looking at the equations about converting from RGB to HSV bearing in mind that boundaries in OpenCV are between 0-180 for H channel and 0-255 for S and V channels, we can generalize the code to work for all colors.
import cv2
import numpy as np
# Capture the input frame from webcam
def get_frame(cap, scaling_factor):
# Capture the frame from video capture object
ret, frame = cap.read()
# Resize the input frame
frame = cv2.resize(frame, None, fx=scaling_factor,
fy=scaling_factor, interpolation=cv2.INTER_AREA)
return frame
# Define the color range in HSV colorspace
lower1 = np.array([0,100,100])
upper1 = np.array([60,255,255])
lower2 = np.array([120,100,100])
upper2 = np.array([180,255,255])
def color_range(degree):
# Degree is between 0-360
# OpenCV uses 0-180 range for H channel
degree = int(degree/2);
global lower1, upper1, lower2, upper2
if degree < 60:
lower1 = np.array([int(0),100,100])
upper1 = np.array([degree+60,255,255])
lower2 = np.array([degree+120,100,100])
upper2 = np.array([int(180),255,255])
elif degree > 120:
lower1 = np.array([degree-60,100,100])
upper1 = np.array([int(180),255,255])
lower2 = np.array([int(0),100,100])
upper2 = np.array([degree-120,255,255])
else:
lower1 = np.array([degree-60,100,100])
upper1 = np.array([degree+60,255,255])
# ineffective variables
lower2 = np.array([181,100,100])
upper2 = np.array([181,255,255])
if __name__=='__main__':
cap = cv2.VideoCapture(0)
scaling_factor = 0.5
# create trackbar for color change
winname = 'Color Detector'
cv2.namedWindow(winname)
cv2.createTrackbar('Color', winname, 0, 360, color_range)
# Iterate until the user presses ESC key
while True:
frame = get_frame(cap, scaling_factor)
# Convert the HSV colorspace
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# Threshold the HSV image to get only desired color
mask1 = cv2.inRange(hsv, lower1, upper1)
mask2 = cv2.inRange(hsv, lower2, upper2)
mask = cv2.bitwise_or(mask1, mask2)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(frame, frame, mask=mask)
cv2.imshow('Original image', frame)
cv2.imshow(winname, res)
# Check if the user pressed ESC key
c = cv2.waitKey(1) & 0xFF
if c == 27:
break
cv2.destroyAllWindows()
cap.release()

AR Drone feedback

thanks for taking the time to read, and hopefully help me.
I have an AR.Drone 2.0 that I have already started to program/develop. I am using python for coding with opencv for the image processing. I want to be able to feedback this code to the drone. I was thinking about obtaining frames from the video stream and have the AR.Drone perform some tasks based upon the images. I, however, don't know where to start. It would be helpful for me if someone can point me in the right direction.
import numpy as np
import cv2
# open the camera
cap = cv2.VideoCapture('tcp://192.168.1.1:5555')
def nothing(x):
pass
cv2.namedWindow('result')
# Starting with 100's to prevent error while masking
h,s,v = 100,100,100
# Creating track bar
cv2.createTrackbar('h', 'result',0,179,nothing)
cv2.createTrackbar('s', 'result',0,255,nothing)
cv2.createTrackbar('v', 'result',0,255,nothing)
while True:
#read the image from the camera
ret, frame = cap.read()
#You will need this later
frame = cv2.cvtColor(frame, 35)
#converting to HSV
hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
# get info from track bar and appy to result
h = cv2.getTrackbarPos('h','result')
s = cv2.getTrackbarPos('s','result')
v = cv2.getTrackbarPos('v','result')
# Normal masking algorithm
lower_blue = np.array([h,s,v])
upper_blue = np.array([180,255,255])
mask = cv2.inRange(hsv,lower_blue, upper_blue)
result = cv2.bitwise_and(frame,frame,mask = mask)
cv2.imshow('result',result)
#find center
cnts=cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2]
center=None
if len(cnts)>0:
c=max(cnts, key=cv2.contourArea)
((x,y),radius)=cv2.minEnclosingCircle(c)
M=cv2.moments(c)
center=(int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
if radius>10:
#cv2.circle(frame, (int(x),int(y)), int(radius), 2)
cv2.circle(frame, center,5,(0,0,255),-1)
# color detection limits
lB = 5
lG = 50
lR = 50
hB = 15
hG = 255
hR = 255
lowerLimits = np.array([lB, lG, lR])
upperLimits = np.array([hB, hG, hR])
# Our operations on the frame come here
thresholded = cv2.inRange(frame, lowerLimits, upperLimits)
outimage = cv2.bitwise_and(frame, frame, mask = thresholded)
cv2.imshow('original', frame)
# Display the resulting frame
cv2.imshow('processed',outimage)
# Quit the program when Q is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
print 'closing program'
cap.release()
cv2.destroyAllWindows()'

Creating Bounding Box across an object in a video

Opencv with Python!
I am trying to create bounding boxes across objects in a video. I have already used the background subtraction function. I am using finContour function. Now the code detects the edges of the 'bus' in the video and creates a bounding box, but it also detects the edges of the windows of the bus and creates a bonding box for each of the window. I just need to get a bounding box across the bus only.
import numpy as np
import cv2
cap = cv2.VideoCapture("C:\\Python27\\clip1.avi")
fgbg = cv2.BackgroundSubtractorMOG()
while(1):
ret, frame = cap.read()
fgmask = fgbg.apply(frame)
# res,thresh = cv2.threshold(fgmask,127,255,0)
kernel = np.ones((10,10),np.uint8)
dilation = cv2.dilate(fgmask,kernel,iterations = 1)
erosion = cv2.erode(fgmask,kernel,iterations = 1)
contours,hierarchy = cv2.findContours(fgmask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
for i in range(0, len(contours)):
if (i % 1 == 0):
cnt = contours[i]
x,y,w,h = cv2.boundingRect(cnt)
cv2.drawContours(fgmask ,contours, -1, (255,255,0), 3)
cv2.rectangle(fgmask,(x,y),(x+w,y+h),(255,0,0),2)
cv2.imshow('frame',fgmask)
cv2.imshow("original",frame)
if cv2.waitKey(30) == ord('a'):
break
cap.release()
cv2.destroyAllWindows()

import cv2
import numpy as np
#img.png is the fgmask
img=cv2.imread('img.png')
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,th1 = cv2.threshold(gray,25,255,cv2.THRESH_BINARY)
_,contours,hierarchy = cv2.findContours(th1, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
x,y,w,h = cv2.boundingRect(cnt)
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
cv2.imshow('image1',img)
cv2.waitKey(0)
cv2.destoryAllWindows(0)
RESULTS

OpenCV Python: Error with using Mask parameter in GoodFeatureToDetect

I was trying to make a facial detection program in Python that combines Haar Cascade Classification and Lucas Kanade. But I am getting error saying something like this:
Error:
Traceback (most recent call last):
File "/home/anthony/Documents/Programming/Python/Computer-Vision/OpenCV-Doc/optical-flow-and-haar-detection-test.py", line 80, in <module>
corners_t = cv2.goodFeaturesToTrack(gray, mask = mask_use, **feature_params)
error: /build/buildd/opencv-2.4.8+dfsg1/modules/imgproc/src/featureselect.cpp:63: error: (-215) mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()) in function goodFeaturesToTrack
How my program works:
My program uses Haar Cascade to get coordinates of a detected face, copy whatever is in that area created by the coordinates (in this case, the face), take an image with nothing but black colors (all pixels are set to zero via numpy), and paste the copied face into the black background. By setting the new face with black background into the mask parameter, this would force Lucas Kanade (goodFeaturesToDetect) to create feature points on the face which will be tracked by optical flow.
Code:
from matplotlib import pyplot as plt
import numpy as np
import cv2
rectangle_x = 0
face_classifier = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
#cap = cv2.VideoCapture('video/sample.mov')
cap = cv2.VideoCapture(0)
# params for ShiTomasi corner detection
feature_params = dict( maxCorners = 200,
qualityLevel = 0.01,
minDistance = 10,
blockSize = 7 )
# Parameters for lucas kanade optical flow
lk_params = dict( winSize = (15,15),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
# Create some random colors
color = np.random.randint(0,255,(100,3))
# Take first frame and find corners in it
ret, old_frame = cap.read()
#old_frame = cv2.imread('images/webcam-first-frame-two.png')
######Adding my code###
cv2.imshow('Old_Frame', old_frame)
cv2.waitKey(0)
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
restart = True
#while restart == True:
face = face_classifier.detectMultiScale(old_gray, 1.2, 4)
if len(face) == 0:
print "This is empty"
for (x,y,w,h) in face:
focused_face = old_frame[y: y+h, x: x+w]
#cv2.rectangle(old_frame, (x,y), (x+w, y+h), (0,255,0),2)
#initalize all pixels to zero (picture completely black)
mask_use = np.zeros(old_frame.shape,np.uint8)
#Crop old_frame coordinates and paste it on the black mask)
mask_use[y:y+h,x:x+w] = old_frame[y:y+h,x:x+w]
height, width, depth = mask_use.shape
print "Height: ", height
print "Width: ", width
print "Depth: ", depth
height, width, depth = old_frame.shape
print "Height: ", height
print "Width: ", width
print "Depth: ", depth
cv2.imshow('Stuff', mask_use)
cv2.imshow('Old_Frame', old_frame)
#cv2.imshow('Zoom in', focused_face)
face_gray = cv2.cvtColor(old_frame,cv2.COLOR_BGR2GRAY)
gray = cv2.cvtColor(focused_face,cv2.COLOR_BGR2GRAY)
corners_t = cv2.goodFeaturesToTrack(gray, mask = mask_use, **feature_params)
corners = np.int0(corners_t)
#print corners
for i in corners:
ix,iy = i.ravel()
cv2.circle(focused_face,(ix,iy),3,255,-1)
cv2.circle(old_frame,(x+ix,y+iy),3,255,-1)
print ix, " ", iy
plt.imshow(old_frame),plt.show()
"""
print "X: ", x
print "Y: ", y
print "W: ", w
print "H: ", h
#face_array = [x,y,w,h]
"""
#############################
p0 = cv2.goodFeaturesToTrack(old_gray, mask = old_gray, **feature_params)
#############################
# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)
while(1):
ret,frame = cap.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# calculate optical flow
p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
# Select good points
good_new = p1[st==1]
###print "Good_New"
###print good_new
good_old = p0[st==1]
# draw the tracks
for i,(new,old) in enumerate(zip(good_new,good_old)):
#print i
#print color[i]
a,b = new.ravel()
c,d = old.ravel()
cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
cv2.circle(frame,(a, b),5,color[i].tolist(),-1)
if i == 99:
break
#For circle, maybe replace (a,b) with (c,d)?
#img = cv2.add(frame,mask)
cv2.imshow('frame',frame)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
# Now update the previous frame and previous points
old_gray = frame_gray.copy()
p0 = good_new.reshape(-1,1,2)
cv2.destroyAllWindows()
cap.release()
Can anyone see the problem and tell me how to fix it? Thanks.

I've had this error caused by using arrays that aren't the same size.
You have a for loop that dynamically assigns values to focused_face but the good_features to track uses a static size (= to the last instance of focused_face). Old_frame looks like it uses the shape of the first instance of focused_face.
Make sure you are using image and mask arrays of the same shape in goodFeaturesToTrack.

track a image in video and replace with another image using opencv

I have to track a window in a video and need to paste an image on window,I have used camshift to track the window, but it did not track it correct.My window is in brown color so I have given the following color range.
np.array((110,0,0)--lower
np.array((130,255,255)--higher..
I have red many documents in opencv but not able to figure out which method to follow.I am using opencv2.4.9 with python.
Below is the code which I tried.Please help me out to figure out the exact location of window.emphasized text
#!/usr/bin/env python
import numpy as np
import cv2
cap = cv2.VideoCapture("myvideo.mp4")
# take first frame of the video
ret,frame = cap.read()
#print frame
#print ret
# setup initial location of window
r,h,c,w = 157,40,337,40
track_window = (c,r,w,h)
# set up the ROI for tracking
roi = frame[r:r+h, c:c+w]
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, np.array((110,0,0)), np.array((130,255,255)))
roi_hist = cv2.calcHist([hsv_roi],[0],mask,[255],[0,255])
cv2.imshow('img2',roi_hist)
#print roi_hist
cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX)
# Setup the termination criteria, either 10 iteration or move by at least 1 pt
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
i = 1
while(1):
ret ,frame = cap.read()
if ret == True:
i += 1
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,255],1)
# apply meanshift to get the new location
ret, track_window = cv2.CamShift(dst, track_window, term_crit)
#print track_window
# Draw it on image
x,y,w,h = track_window
img2 = cv2.rectangle(frame, (x,y), (x+w,y+h), 255,2)
cv2.imshow('img2',frame)
k = cv2.waitKey(200) & 0xff
if k == 27:
break
else:
# print "comes here2";
cv2.imwrite(str(i)+"test.jpg",frame)
#break
else:
break
cv2.destroyAllWindows()
cap.release()