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Я попытался изменить параметры переменных.Отслеживание движения с использованием малины pi 3, OpenCV и Python
Ошибка говорит мне «слишком много значений для распаковки».
Этот код написан для OpenCV 2.0, однако я использую OpenCV3.1. Я сталкиваюсь с проблемой обратной совместимости здесь или это что-то более тривиальное?
Вот мое сообщение об ошибке
File "/home/pi/motion-track/motion-track.py", line 219, in <module>
motion_track()
File "/home/pi/motion-track/motion-track.py", line 174, in motion_track
contours, hierarchy = cv2.findContours(thresholdimage,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
ValueError: too many values to unpack
Here is my code:
#!/usr/bin/env python
progname = "motion_track.py"
ver = "version 0.95"
"""
motion-track ver 0.95 written by Claude Pageau [email protected]
Raspberry (Pi) - python opencv2 motion tracking using picamera module
It will detect motion in the field of view and use opencv to calculate the
largest contour and return its x,y coordinate. I will be using this for
a simple RPI robotics project, but thought the code would be useful for
other users as a starting point for a project. I did quite a bit of
searching on the internet, github, etc but could not find a similar
implementation that returns x,y coordinates of the most dominate moving
object in the frame.
sudo apt-get update
sudo apt-get upgrade
sudo apt-get install python-opencv python-picamera
sudo apt-get install libgl1-mesa-dri
"""
print("%s %s using python2 and OpenCV2" % (progname, ver))
print("Loading Please Wait ....")
# import the necessary packages
import io
import time
import cv2
from picamera.array import PiRGBArray
from picamera import PiCamera
from threading import Thread
# Display Settings
debug = True # Set to False for no data display
window_on = False # Set to True displays opencv windows (GUI desktop reqd)
SHOW_CIRCLE = True # show a circle otherwise show bounding rectancle on window
CIRCLE_SIZE = 8 # diameter of circle to show motion location in window
LINE_THICKNESS = 1 # thickness of bounding line in pixels
WINDOW_BIGGER = 1 # Resize multiplier for Movement Status Window
# if gui_window_on=True then makes opencv window bigger
# Note if the window is larger than 1 then a reduced frame rate will occur
# Camera Settings
CAMERA_WIDTH = 320
CAMERA_HEIGHT = 240
big_w = int(CAMERA_WIDTH * WINDOW_BIGGER)
big_h = int(CAMERA_HEIGHT * WINDOW_BIGGER)
CAMERA_HFLIP = False
CAMERA_VFLIP = True
CAMERA_ROTATION=180
CAMERA_FRAMERATE = 35
FRAME_COUNTER = 1000
# Motion Tracking Settings
MIN_AREA = 200 # excludes all contours less than or equal to this Area
THRESHOLD_SENSITIVITY = 25
BLUR_SIZE = 10
#-----------------------------------------------------------------------------------------------
class PiVideoStream:
def __init__(self, resolution=(CAMERA_WIDTH, CAMERA_HEIGHT), framerate=CAMERA_FRAMERATE, rotation=0, hflip=False, vflip=False):
# initialize the camera and stream
self.camera = PiCamera()
self.camera.resolution = resolution
self.camera.rotation = rotation
self.camera.framerate = framerate
self.camera.hflip = hflip
self.camera.vflip = vflip
self.rawCapture = PiRGBArray(self.camera, size=resolution)
self.stream = self.camera.capture_continuous(self.rawCapture,
format="bgr", use_video_port=True)
# initialize the frame and the variable used to indicate
# if the thread should be stopped
self.frame = None
self.stopped = False
def start(self):
# start the thread to read frames from the video stream
t = Thread(target=self.update, args=())
t.daemon = True
t.start()
return self
def update(self):
# keep looping infinitely until the thread is stopped
for f in self.stream:
# grab the frame from the stream and clear the stream in
# preparation for the next frame
self.frame = f.array
self.rawCapture.truncate(0)
# if the thread indicator variable is set, stop the thread
# and resource camera resources
if self.stopped:
self.stream.close()
self.rawCapture.close()
self.camera.close()
return
def read(self):
# return the frame most recently read
return self.frame
def stop(self):
# indicate that the thread should be stopped
self.stopped = True
#-----------------------------------------------------------------------------------------------
def show_FPS(start_time,frame_count):
if debug:
if frame_count >= FRAME_COUNTER:
duration = float(time.time() - start_time)
FPS = float(frame_count/duration)
print("Processing at %.2f fps last %i frames" %(FPS, frame_count))
frame_count = 0
start_time = time.time()
else:
frame_count += 1
return start_time, frame_count
#-----------------------------------------------------------------------------------------------
def motion_track():
print("Initializing Camera ....")
# Save images to an in-program stream
# Setup video stream on a processor Thread for faster speed
vs = PiVideoStream().start()
vs.camera.rotation = CAMERA_ROTATION
vs.camera.hflip = CAMERA_HFLIP
vs.camera.vflip = CAMERA_VFLIP
time.sleep(2.0)
if window_on:
print("press q to quit opencv display")
else:
print("press ctrl-c to quit")
print("Start Motion Tracking ....")
cx = 0
cy = 0
cw = 0
ch = 0
frame_count = 0
start_time = time.time()
# initialize image1 using image2 (only done first time)
image2 = vs.read()
image1 = image2
grayimage1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
first_image = False
still_scanning = True
while still_scanning:
image2 = vs.read()
start_time, frame_count = show_FPS(start_time, frame_count)
# initialize variables
motion_found = False
biggest_area = MIN_AREA
# At this point the image is available as stream.array
# Convert to gray scale, which is easier
grayimage2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
# Get differences between the two greyed, blurred images
differenceimage = cv2.absdiff(grayimage1, grayimage2)
differenceimage = cv2.blur(differenceimage,(BLUR_SIZE,BLUR_SIZE))
# Get threshold of difference image based on THRESHOLD_SENSITIVITY variable
retval, thresholdimage = cv2.threshold(differenceimage,THRESHOLD_SENSITIVITY,255,cv2.THRESH_BINARY)
# Get all the contours found in the thresholdimage
contours, hierarchy = cv2.findContours(thresholdimage,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
total_contours = len(contours)
# save grayimage2 to grayimage1 ready for next image2
grayimage1 = grayimage2
# find contour with biggest area
for c in contours:
# get area of next contour
found_area = cv2.contourArea(c)
# find the middle of largest bounding rectangle
if found_area > biggest_area:
motion_found = True
biggest_area = found_area
(x, y, w, h) = cv2.boundingRect(c)
cx = int(x + w/2) # put circle in middle of width
cy = int(y + h/6) # put circle closer to top
cw = w
ch = h
if motion_found:
# Do Something here with motion data
if window_on:
# show small circle at motion location
if SHOW_CIRCLE:
cv2.circle(image2,(cx,cy),CIRCLE_SIZE,(0,255,0), LINE_THICKNESS)
else:
cv2.rectangle(image2,(cx,cy),(x+cw,y+ch),(0,255,0), LINE_THICKNESS)
if debug:
print("Motion at cx=%3i cy=%3i total_Contours=%2i biggest_area:%3ix%3i=%5i" % (cx ,cy, total_contours, cw, ch, biggest_area))
if window_on:
# cv2.imshow('Difference Image',differenceimage)
cv2.imshow('OpenCV Threshold', thresholdimage)
if WINDOW_BIGGER > 1: # Note setting a bigger window will slow the FPS
image2 = cv2.resize(image2,(big_w, big_h))
cv2.imshow('Movement Status (Press q in Window to Quit)', image2)
# Close Window if q pressed while movement status window selected
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
print("End Motion Tracking")
still_scanning = False
#-----------------------------------------------------------------------------------------------
if __name__ == '__main__':
try:
motion_track()
finally:
print("")
print("+++++++++++++++++++++++++++++++++++")
print("%s %s - Exiting" % (progname, ver))
print("+++++++++++++++++++++++++++++++++++")
print("")
Да, это проблема, одно из основных изменений в cv2-cv3 - это типы возврата различных функций. Ошибка означает, что функция возвращает несколько значений, в которых вы ожидаете только 1 –