http://qiita.com/northriver/items/d6b73da79a13bf3526e2 Now that you know about opencv, let's challenge towards the title
First of all, understand by using something that seems to be helpful
I found a code that is tracking the green ball, so I'll give it a try. This seems to be detected by color http://www.pyimagesearch.com/2015/09/14/ball-tracking-with-opencv/
#Import required packages
from collections import deque
import numpy as np
import argparse
import imutils
import cv2
#Make an argument to execute. "Python xxx.py -v test.mov "-About v
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",help="path to the (optional) video file")
ap.add_argument("-b", "--buffer", type=int, default=64,help="max buffer size")
args = vars(ap.parse_args())
Click here for an explanation of argparse http://python.civic-apps.com/argparse/
#Define the color of the chasing ball"green" (HSV)
greenLower = (29, 86, 6)
greenUpper = (64, 255, 255)
pts = deque(maxlen=args["buffer"])
HSV color space http://www.peko-step.com/html/hsv.html
#If there is an argument, the path of the file, if not, the webcam
if not args.get("video", False):
camera = cv2.VideoCapture(0)
else:
camera = cv2.VideoCapture(args["video"])
while True:
#Camera, take video
(grabbed, frame) = camera.read()
#If you can't, break
if args.get("video") and not grabbed:
break
# resize
frame = imutils.resize(frame, width=600)
#Convert to hsv
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
#Extract only the green part and perform morphology conversion
mask = cv2.inRange(hsv, greenLower, greenUpper)
mask = cv2.erode(mask, None, iterations=2)
mask = cv2.dilate(mask, None, iterations=2)
I am doing morphology conversion. The morphology transformation extracts the characteristic part of the object and removes the rest. I am trying to reduce it. cv2.erode is eroded to leave characteristic parts, and cv2.dilate is eroded to restore only the important parts. http://labs.eecs.tottori-u.ac.jp/sd/Member/oyamada/OpenCV/html/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.html
#Extract contours from binary black and white data
# (x, y) center of the ball
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2]
center = None
Extract contours from binary black and white data with cv2.findContours. RETR_EXTERNAL is a mode that extracts only the outermost part, and CHAIN_APPROX_SIMPLE is a method of contour approximation method. http://opencv.jp/opencv-2.1/cpp/structural_analysis_and_shape_descriptors.html
if len(cnts) > 0:
#Calculate the area occupied by the area and find the one with the largest area
c = max(cnts, key=cv2.contourArea)
#Find the smallest circle
((x, y), radius) = cv2.minEnclosingCircle(c)
#Moment(Basic values such as area and center of gravity)And get the coordinates of the center of the circle
M = cv2.moments(c)
center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
#Draw a circle
if radius > 10:
cv2.circle(frame, (int(x), int(y)), int(radius),
(0, 255, 255), 2)
cv2.circle(frame, center, 5, (0, 0, 255), -1)
http://labs.eecs.tottori-u.ac.jp/sd/Member/oyamada/OpenCV/html/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.html
The following is to follow the trajectory, so you don't have to
# update the points queue
pts.appendleft(center)
# loop over the set of tracked points
for i in xrange(1, len(pts)):
# if either of the tracked points are None, ignore
# them
if pts[i - 1] is None or pts[i] is None:
continue
# otherwise, compute the thickness of the line and
# draw the connecting lines
thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 2.5)
cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)
The rest is just output, this is the same as the tutorial
# show the frame to our screen
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the 'q' key is pressed, stop the loop
if key == ord("q"):
break
# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()
Connect from start to finish
# import the necessary packages
from collections import deque
import numpy as np
import argparse
import imutils
import cv2
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
help="path to the (optional) video file")
ap.add_argument("-b", "--buffer", type=int, default=64,
help="max buffer size")
args = vars(ap.parse_args())
# define the lower and upper boundaries of the "green"
# ball in the HSV color space, then initialize the
# list of tracked points
greenLower = (29, 86, 6)
greenUpper = (64, 255, 255)
pts = deque(maxlen=args["buffer"])
# if a video path was not supplied, grab the reference
# to the webcam
if not args.get("video", False):
camera = cv2.VideoCapture(0)
# otherwise, grab a reference to the video file
else:
camera = cv2.VideoCapture(args["video"])
# keep looping
while True:
# grab the current frame
(grabbed, frame) = camera.read()
# if we are viewing a video and we did not grab a frame,
# then we have reached the end of the video
if args.get("video") and not grabbed:
break
# resize the frame, blur it, and convert it to the HSV
# color space
frame = imutils.resize(frame, width=600)
# blurred = cv2.GaussianBlur(frame, (11, 11), 0)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# construct a mask for the color "green", then perform
# a series of dilations and erosions to remove any small
# blobs left in the mask
mask = cv2.inRange(hsv, greenLower, greenUpper)
mask = cv2.erode(mask, None, iterations=2)
mask = cv2.dilate(mask, None, iterations=2)
# find contours in the mask and initialize the current
# (x, y) center of the ball
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[-2]
center = None
# only proceed if at least one contour was found
if len(cnts) > 0:
# find the largest contour in the mask, then use
# it to compute the minimum enclosing circle and
# centroid
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"]))
# only proceed if the radius meets a minimum size
if radius > 10:
# draw the circle and centroid on the frame,
# then update the list of tracked points
cv2.circle(frame, (int(x), int(y)), int(radius),
(0, 255, 255), 2)
cv2.circle(frame, center, 5, (0, 0, 255), -1)
# update the points queue
pts.appendleft(center)
# loop over the set of tracked points
for i in xrange(1, len(pts)):
# if either of the tracked points are None, ignore
# them
if pts[i - 1] is None or pts[i] is None:
continue
# otherwise, compute the thickness of the line and
# draw the connecting lines
thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 2.5)
cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)
# show the frame to our screen
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the 'q' key is pressed, stop the loop
if key == ord("q"):
break
# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()
Well, I tried so far, but when I put in a basketball video, I could not detect it ... There is a color problem
greenLower = (7,100,100)
greenUpper = (11,255,255)
In the case of basketball, the balls are similar in color to the gymnasium, and the old balls are black. Therefore, it is necessary to use a recognition method other than color detection.
http://labs.eecs.tottori-u.ac.jp/sd/Member/oyamada/OpenCV/html/py_tutorials/py_video/py_table_of_contents_video/py_table_of_contents_video.html#py-table-of-content-video I also found an improved version http://answers.opencv.org/question/17637/backgroundsubtractormog-with-python/ For the time being, there is also a method of detecting motion by background subtraction, so I tried it, but in the case of basketball, the camera moves, so there is no background ... Subtle ...
I can't come up with a good idea ... Please let me know if you would like to do this.
this? http://qiita.com/olympic2020/items/3d8973f855e963c9d999
I tried it, but it didn't work ...
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