我正在使用覆盆子卡进行最后的项目研究，我想同时使用两个摄像头并使用两个摄像头运行相同的python程序拍照并进行处理以了解产品 我如何使用多线程
来做到这一点from os import listdir
from os.path import isfile, join
import numpy
import cv2
import os
import sys
def match_images(img1, img2):
detector = cv2.SURF(200, 1, 1)
matcher = cv2.BFMatcher(cv2.NORM_L2)
kp1, desc1 = detector.detectAndCompute(img1, None)
kp2, desc2 = detector.detectAndCompute(img2, None)
print 'img1 %d features, img2 %d features' % (len(kp1), len(kp2))
raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2)
kp_pairs = filter_matches(kp1, kp2, raw_matches)
return kp_pairs
def filter_matches(kp1, kp2, matches, ratio = 0.75):
mkp1, mkp2 = [], []
for m in matches:
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
m = m[0]
mkp1.append( kp1[m.queryIdx] )
mkp2.append( kp2[m.trainIdx] )
kp_pairs = zip(mkp1, mkp2)
return kp_pairs
def explore_match(win, img1, img2, kp_pairs, status = None, H = None):
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
vis = numpy.zeros((max(h1, h2), w1+w2), numpy.uint8)
vis[:h1, :w1] = img1
vis[:h2, w1:w1+w2] = img2
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
if H is not None:
corners = numpy.float32([[0, 0], [w1, 0], [w1, h1], [0, h1]])
corners = numpy.int32( cv2.perspectiveTransform(corners.reshape(1, -1, 2), H).reshape(-1, 2) + (w1, 0) )
cv2.polylines(vis, [corners], True, (255, 255, 255))
if status is None:
status = numpy.ones(len(kp_pairs), numpy.bool_)
p1 = numpy.int32([kpp[0].pt for kpp in kp_pairs])
p2 = numpy.int32([kpp[1].pt for kpp in kp_pairs]) + (w1, 0)
green = (0, 255, 0)
red = (0, 0, 255)
white = (255, 255, 255)
kp_color = (51, 103, 236)
for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
if inlier:
col = green
cv2.circle(vis, (x1, y1), 2, col, -1)
cv2.circle(vis, (x2, y2), 2, col, -1)
else:
col = red
r = 2
thickness = 3
cv2.line(vis, (x1-r, y1-r), (x1+r, y1+r), col, thickness)
cv2.line(vis, (x1-r, y1+r), (x1+r, y1-r), col, thickness)
cv2.line(vis, (x2-r, y2-r), (x2+r, y2+r), col, thickness)
cv2.line(vis, (x2-r, y2+r), (x2+r, y2-r), col, thickness)
vis0 = vis.copy()
for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
if inlier:
cv2.line(vis, (x1, y1), (x2, y2), green)
cv2.imshow(win, vis)
def draw_matches(window_name, kp_pairs, img1, img2):
mkp1, mkp2 = zip(*kp_pairs)
p1 = numpy.float32([kp.pt for kp in mkp1])
p2 = numpy.float32([kp.pt for kp in mkp2])
if len(kp_pairs) >= 100:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
print '%d / %d inliers/matched' % (numpy.sum(status), len(status))
import RPI.GPIO as GPIO
import time
GPIO.setup(LEDPin, GPIO.OUT)
GPIO.output(LEDPin, True)
print("LED ON")
time.sleep(5)
GPIO.output(LEDPin, False)
if len(p1):
explore_match(window_name, img1, img2, kp_pairs, status, H)
if __name__ == '__main__':
import cv2
import os
camera_port = 1
ramp_frames = 2
camera = cv2.VideoCapture(camera_port)
def get_image():
retval, im = camera.read()
return im
for i in xrange(ramp_frames):
temp = get_image()
print("Taking image...")
camera_capture = get_image()
file = "C:/Users/oussema/Desktop/projet/scripts/test_image.png"
cv2.imwrite(file, camera_capture)
del(camera)
img2 = cv2.imread('test_image.png', 0)
img1 = cv2.imread('1.jpg', 0)
if img1 is None:
print 'Failed to load img1:'
sys.exit(1)
if img2 is None:
print 'Failed to load img2:'
sys.exit(1)
kp_pairs = match_images(img1, img2)
if len(kp_pairs) >= 100:
draw_matches('find_obj', kp_pairs, img1, img2)
cv2.waitKey()
cv2.destroyAllWindows()
elif len(kp_pairs) <= 100:
print '%d matches found, not enough for homography estimation' % len(kp_pairs)
img1 = cv2.imread('2.jpg', 0)
kp_pairs = match_images(img1, img2)
if len(kp_pairs) >= 100:
draw_matches('find_obj', kp_pairs, img1, img2)
cv2.waitKey()
cv2.destroyAllWindows()
elif len(kp_pairs) <= 100:
print '%d matches found, not enough for homography estimation' % len(kp_pairs)
img1 = cv2.imread('3.jpg', 0)
kp_pairs = match_images(img1, img2)
if len(kp_pairs) >= 100:
draw_matches('find_obj', kp_pairs, img1, img2)
cv2.waitKey()
cv2.destroyAllWindows()
elif len(kp_pairs) <= 100:
print '%d matches found, not enough for homography estimation' % len(kp_pairs)
img1 = cv2.imread('4.jpg', 0)
kp_pairs = match_images(img1, img2)
if len(kp_pairs) >= 100:
draw_matches('find_obj', kp_pairs, img1, img2)
cv2.waitKey()
cv2.destroyAllWindows()
elif len(kp_pairs) <= 100:
print '%d matches found, not enough for homography estimation' % len(kp_pairs)
img1 = cv2.imread('5.jpg', 0)
kp_pairs = match_images(img1, img2)
if len(kp_pairs) >= 100:
draw_matches('find_obj', kp_pairs, img1, img2)
cv2.waitKey()
cv2.destroyAllWindows()
elif len(kp_pairs) <= 100:
print '%d matches found, not enough for homography estimation' % len(kp_pairs)
img1 = cv2.imread('6.jpg', 0)
kp_pairs = match_images(img1, img2)
if len(kp_pairs) >= 100:
draw_matches('find_obj', kp_pairs, img1, img2)
cv2.waitKey()
cv2.destroyAllWindows()
elif len(kp_pairs) <= 100:
print '%d matches found, not enough for homography estimation' % len(kp_pairs)
答案 0 :(得分：1)
由于GIL，我认为你不能同时运行2个主题。所以，您可以尝试multiprocessing模块。
Jut尝试：
from multiprocessing import Process
def Camera(name):
print('hello', name)
if __name__ == '__main__':
p1 = Process(target=Camera, args=('bob',))
p2 = Process(target=Camera, args=('jack',))
p1.start()
p2.start()
p1.join()
p2.join()
答案 1 :(得分：0)
以下是一个例子，我为您留下了放置相机代码的空间。
import thread
import time
# Define a function for the thread
def camera(threadName, delay):
# Your Code to take a picture, or whatever
# Create two threads as follows
try:
thread.start_new_thread( camera, ("camera-1", 2, ) )
thread.start_new_thread( camera, ("camera-2", 4, ) )
except:
print "Error: unable to start thread"
while 1:
pass
答案 2 :(得分：0)
from os import listdir
from os.path import isfile, join
import numpy
import cv2
import os
import sys
def match_images(img1, img2):
detector = cv2.SURF(200, 1, 1)
matcher = cv2.BFMatcher(cv2.NORM_L2)
kp1, desc1 = detector.detectAndCompute(img1, None)
kp2, desc2 = detector.detectAndCompute(img2, None)
print 'img1 %d features, img2 %d features' % (len(kp1), len(kp2))
raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2)
kp_pairs = filter_matches(kp1, kp2, raw_matches)
return kp_pairs
def filter_matches(kp1, kp2, matches, ratio = 0.75):
mkp1, mkp2 = [], []
for m in matches:
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
m = m[0]
mkp1.append( kp1[m.queryIdx] )
mkp2.append( kp2[m.trainIdx] )
kp_pairs = zip(mkp1, mkp2)
return kp_pairs
def explore_match(win, img1, img2, kp_pairs, status = None, H = None):
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
vis = numpy.zeros((max(h1, h2), w1+w2), numpy.uint8)
vis[:h1, :w1] = img1
vis[:h2, w1:w1+w2] = img2
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
if H is not None:
corners = numpy.float32([[0, 0], [w1, 0], [w1, h1], [0, h1]])
corners = numpy.int32( cv2.perspectiveTransform(corners.reshape(1, -1, 2), H).reshape(-1, 2) + (w1, 0) )
cv2.polylines(vis, [corners], True, (255, 255, 255))
if status is None:
status = numpy.ones(len(kp_pairs), numpy.bool_)
p1 = numpy.int32([kpp[0].pt for kpp in kp_pairs])
p2 = numpy.int32([kpp[1].pt for kpp in kp_pairs]) + (w1, 0)
green = (0, 255, 0)
red = (0, 0, 255)
white = (255, 255, 255)
kp_color = (51, 103, 236)
for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
if inlier:
col = green
cv2.circle(vis, (x1, y1), 2, col, -1)
cv2.circle(vis, (x2, y2), 2, col, -1)
else:
col = red
r = 2
thickness = 3
cv2.line(vis, (x1-r, y1-r), (x1+r, y1+r), col, thickness)
cv2.line(vis, (x1-r, y1+r), (x1+r, y1-r), col, thickness)
cv2.line(vis, (x2-r, y2-r), (x2+r, y2+r), col, thickness)
cv2.line(vis, (x2-r, y2+r), (x2+r, y2-r), col, thickness)
vis0 = vis.copy()
for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
if inlier:
cv2.line(vis, (x1, y1), (x2, y2), green)
cv2.imshow(win, vis)
def draw_matches(window_name, kp_pairs, img1, img2):
mkp1, mkp2 = zip(*kp_pairs)
p1 = numpy.float32([kp.pt for kp in mkp1])
p2 = numpy.float32([kp.pt for kp in mkp2])
if len(kp_pairs) >= 100:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
print '%d / %d inliers/matched' % (numpy.sum(status), len(status))
import RPI.GPIO as GPIO
import time
GPIO.setup(LEDPin, GPIO.OUT)
GPIO.output(LEDPin, True)
print("LED ON")
time.sleep(5)
GPIO.output(LEDPin, False)
if len(p1):
explore_match(window_name, img1, img2, kp_pairs, status, H)
if __name__ == '__main__':
import cv2
import os
camera_port = 1
ramp_frames = 2
camera = cv2.VideoCapture(camera_port)
def get_image():
retval, im = camera.read()
return im
for i in xrange(ramp_frames):
temp = get_image()
print("Taking image...")
camera_capture = get_image()
file = "C:/Users/oussema/Desktop/projet/scripts/test_image.png"
cv2.imwrite(file, camera_capture)
del(camera)
img2 = cv2.imread('test_image.png', 0)
img1 = cv2.imread('1.jpg', 0)
if img1 is None:
print 'Failed to load img1:'
sys.exit(1)
if img2 is None:
print 'Failed to load img2:'
sys.exit(1)
kp_pairs = match_images(img1, img2)
if len(kp_pairs) >= 100:
draw_matches('find_obj', kp_pairs, img1, img2)
cv2.waitKey()
cv2.destroyAllWindows()
elif len(kp_pairs) <= 100:
print '%d matches found, not enough for homography estimation' % len(kp_pairs)
img1 = cv2.imread('2.jpg', 0)
kp_pairs = match_images(img1, img2)
if len(kp_pairs) >= 100:
draw_matches('find_obj', kp_pairs, img1, img2)
cv2.waitKey()
cv2.destroyAllWindows()
elif len(kp_pairs) <= 100:
print '%d matches found, not enough for homography estimation' % len(kp_pairs)
img1 = cv2.imread('3.jpg', 0)
kp_pairs = match_images(img1, img2)
if len(kp_pairs) >= 100:
draw_matches('find_obj', kp_pairs, img1, img2)
cv2.waitKey()
cv2.destroyAllWindows()
elif len(kp_pairs) <= 100:
print '%d matches found, not enough for homography estimation' % len(kp_pairs)
img1 = cv2.imread('4.jpg', 0)
kp_pairs = match_images(img1, img2)
if len(kp_pairs) >= 100:
draw_matches('find_obj', kp_pairs, img1, img2)
cv2.waitKey()
cv2.destroyAllWindows()
elif len(kp_pairs) <= 100:
print '%d matches found, not enough for homography estimation' % len(kp_pairs)
img1 = cv2.imread('5.jpg', 0)
kp_pairs = match_images(img1, img2)
if len(kp_pairs) >= 100:
draw_matches('find_obj', kp_pairs, img1, img2)
cv2.waitKey()
cv2.destroyAllWindows()
elif len(kp_pairs) <= 100:
print '%d matches found, not enough for homography estimation' % len(kp_pairs)
img1 = cv2.imread('6.jpg', 0)``
kp_pairs = match_images(img1, img2)
if len(kp_pairs) >= 100:
draw_matches('find_obj', kp_pairs, img1, img2)
cv2.waitKey()
cv2.destroyAllWindows()
elif len(kp_pairs) <= 100:
print '%d matches found, not enough for homography estimation' % len(kp_pairs)
答案 3 :(得分：0)
我使用2台相机，因为当产品在垫子上时我遇到了问题