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Created June 21, 2019 14:18
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Testing the OpenCV Optical Flow tutorial on the Raspberry Pi Zero
import io
import time
import picamera
#from PIL import Image
import cv2 as cv
import numpy as np
NoI = 200
# params for ShiTomasi corner detection
feature_params = dict( maxCorners = 100,
qualityLevel = 0.3,
minDistance = 7,
blockSize = 7 )
# Parameters for lucas kanade optical flow
lk_params = dict( winSize = (15,15),
maxLevel = 2,
criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))
# Create some random colors
color = np.random.randint(0,255,(100,3))
def outputs(old_gray, p0, mask):
for i in range(NoI):
# This returns the stream for the camera to capture to
yield stream
# Once the capture is complete, the loop continues here
# (read up on generator functions in Python to understand
# the yield statement). Here you could do some processing
# on the image...
# Construct a numpy array from the stream
data = np.fromstring(stream.getvalue(), dtype=np.uint8)
# "Decode" the image from the array, preserving colour
frame = cv.imdecode(data, 1)
# OpenCV returns an array with data in BGR order. If you want RGB instead
# use the following...
frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
# calculate optical flow
p1, st, err = cv.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
# Select good points
good_new = p1[st==1]
good_old = p0[st==1]
# draw the tracks
for i,(new,old) in enumerate(zip(good_new, good_old)):
a,b = new.ravel()
c,d = old.ravel()
mask = cv.line(mask, (a,b),(c,d), color[i].tolist(), 2)
frame = cv.circle(frame,(a,b),5,color[i].tolist(),-1)
img = cv.add(frame,mask)
# Now update the previous frame and previous points
old_gray = frame_gray.copy()
p0 = good_new.reshape(-1,1,2)
# Finally, reset the stream for the next capture
stream.seek(0)
stream.truncate()
# Create the in-memory stream
stream = io.BytesIO()
with picamera.PiCamera() as camera:
camera.resolution = (640, 480)
camera.framerate = 80
time.sleep(2)
# Take first frame and find corners in it
camera.capture(stream, format='jpeg')
# Construct a numpy array from the stream
data = np.fromstring(stream.getvalue(), dtype=np.uint8)
# "Decode" the image from the array, preserving colour
old_frame = cv.imdecode(data, 1)
# OpenCV returns an array with data in BGR order. If you want RGB instead
# use the following...
old_gray = cv.cvtColor(old_frame, cv.COLOR_BGR2GRAY)
p0 = cv.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)
start = time.time()
camera.capture_sequence(outputs(old_gray, p0, mask), 'jpeg', use_video_port=True)
#camera.capture_sequence(outputs(old_gray, p0, mask), 'jpeg', use_video_port=True)
finish = time.time()
print('Processed {} images at {:.2f}fps'.format(NoI, NoI / (finish - start)))
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