全局名称 'SIFT' 未在 python(Pi 3 B) 中定义
global name 'SIFT' is not defined in python(Pi 3 B)
当我 运行 window 上的此示例代码与 opencv 3.0.0、sift = cv2.SIFT() 一起工作时，当我 运行 将相同的代码更改为 sift = cv2.xfeatures2d.SIFT_create() in Raspberry Pi with opencv 3.2.0, 显示这个错误,
"global name 'SIFT' is not defined"
#!/usr/bin/env python
# Copyright 2014 Jarmo Puttonen <jarmo.puttonen@gmail.com>. All rights reserved.
# Use of this source code is governed by a MIT-style
# licence that can be found in the LICENCE file.
"""Detect speed limits from webcam feed"""
import re
import os
import sys
import cv2
import threading
import numpy as np
import argparse
#from gps import gps, WATCH_ENABLE
def onTrackbarChange(_):
"""Pass any trackbar changes"""
class GPSInfo(threading.Thread):
"""Thread that takes care of updating GPS Speed info."""
def __init__(self):
threading.Thread.__init__(self)
self.gpsd = gps(mode=WATCH_ENABLE)
self.running = True
self.speed = 0
def run(self):
while self.running:
self.gpsd.next()
self.speed = self.gpsd.fix.speed*3.6 #from m/s to km/h
def read_paths(path):
"""Returns a list of files in given path"""
images = \[\[\] for _ in range(2)\]
for dirname, dirnames, _ in os.walk(path):
for subdirname in dirnames:
filepath = os.path.join(dirname, subdirname)
for filename in os.listdir(filepath):
try:
imgpath = str(os.path.join(filepath, filename))
images\[0\].append(imgpath)
limit = re.findall('\[0-9\]+', filename)
images\[1\].append(limit\[0\])
except IOError, (errno, strerror):
print "I/O error({0}): {1}".format(errno, strerror)
except:
print "Unexpected error:", sys.exc_info()\[0\]
raise
return images
def load_images(imgpath):
"""Loads images in given path and returns
a list containing image and keypoints"""
images = read_paths(imgpath)
imglist = \[\[\], \[\], \[\], \[\]\]
cur_img = 0
SIFT = cv2.xfeatures2d.SIFT_create()
for i in images\[0\]:
img = cv2.imread(i, 0)
imglist\[0\].append(img)
imglist\[1\].append(images\[1\]\[cur_img\])
cur_img += 1
keypoints, des = SIFT.detectAndCompute(img, None)
imglist\[2\].append(keypoints)
imglist\[3\].append(des)
return imglist
def run_flann(img):
"""Run FLANN-detector for given image with given image list"""
# Find the keypoint descriptors with SIFT
_, des = SIFT.detectAndCompute(img, None)
if des is None:
return "Unknown", 0
if len(des) < ARGS.MINKP:
return "Unknown", 0
biggest_amnt = 0
biggest_speed = 0
cur_img = 0
try:
for _ in IMAGES\[0\]:
des2 = IMAGES\[3\]\[cur_img\]
matches = FLANN.knnMatch(des2, des, k=2)
matchamnt = 0
# Find matches with Lowe's ratio test
for _, (moo, noo) in enumerate(matches):
if moo.distance < ARGS.FLANNTHRESHOLD*noo.distance:
matchamnt += 1
if matchamnt > biggest_amnt:
biggest_amnt = matchamnt
biggest_speed = IMAGES\[1\]\[cur_img\]
cur_img += 1
if biggest_amnt > ARGS.MINKP:
return biggest_speed, biggest_amnt
else:
return "Unknown", 0
except Exception, exept:
print exept
return "Unknown", 0
IMAGES = load_images("data")
def run_logic():
"""Run TSR and ISA"""
lastlimit = "00"
lastdetect = "00"
downscale = ARGS.DOWNSCALE
matches = 0
possiblematch = "00"
try:
if CAP.isOpened():
rval, frame = CAP.read()
print("Camera opened and frame read")
else:
rval = False
print("Camera not opened")
while rval:
origframe = frame
if ARGS.MORPH:
frame = cv2.morphologyEx(
frame,
cv2.MORPH_OPEN,
cv2.getStructuringElement(cv2.MORPH_RECT,(2,2))
)
frame = cv2.morphologyEx(
frame,
cv2.MORPH_CLOSE,
cv2.getStructuringElement(cv2.MORPH_RECT,(2,2))
)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if ARGS.EQ:
cv2.equalizeHist(frame, frame)
if ARGS.TRACKBARS:
ARGS.MINKP = cv2.getTrackbarPos('MINKEYPOINTS','preview')
downscale = cv2.getTrackbarPos('DOWNSCALE','preview')
ARGS.FLANNTHRESHOLD = float(
cv2.getTrackbarPos('FLANNTHRESHOLD','preview')
)/10
ARGS.CHECKS = cv2.getTrackbarPos('FLANNCHECKS','preview')
ARGS.TREES = cv2.getTrackbarPos('FLANNTREES','preview')
scaledsize = (frame.shape\[1\]/downscale, frame.shape\[0\]/downscale)
scaledframe = cv2.resize(frame, scaledsize)
# Detect signs in downscaled frame
signs = CLASSIFIER.detectMultiScale(
scaledframe,
1.1,
5,
0,
(10, 10),
(200, 200))
for sign in signs:
xpos, ypos, width, height = \[ i*downscale for i in sign \]
crop_img = frame\[ypos:ypos+height, xpos:xpos+width\]
sized = cv2.resize(crop_img, (128, 128))
comp = "Unknown"
comp, amnt = run_flann(sized)
if comp != "Unknown":
if comp != lastlimit:
# Require two consecutive hits for new limit.
if comp == lastdetect:
possiblematch = comp
matches = matches + 1
if matches >= ARGS.matches:
print "New speed limit: "+possiblematch
lastlimit = possiblematch
matches = 0
else:
possiblematch = "00"
matches = 0
cv2.rectangle(
origframe,
(xpos, ypos),
(xpos+width, ypos+height),
(0, 0, 255))
cv2.putText(
origframe,
"Speed limit: "+comp+" KP: "+str(amnt),
(xpos,ypos-5),
cv2.FONT_HERSHEY_SIMPLEX,
0.4,
(0,0,255),
1,)
else:
cv2.rectangle(
origframe,
(xpos,ypos),
(xpos+width,ypos+height),
(255,0,0))
cv2.putText(
origframe,
"UNKNOWN SPEED LIMIT",
(xpos,ypos-5),
cv2.FONT_HERSHEY_SIMPLEX,
0.4,
(255,0,0),
1,)
comp = lastdetect
lastdetect = comp
cv2.putText(
origframe,
"Current speed limit: "+str(lastlimit)+" km/h.",
(5,50),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(0,0,0),
2
)
cv2.imshow("preview", origframe)
if ARGS.PREVIEW:
cv2.imshow("preview", origframe)
_ = cv2.waitKey(20)
rval, frame = CAP.read()
except (KeyboardInterrupt, Exception), exept:
print exept
## if ARGS.GPS:
## print "Killing GPS"
## GPSP.running = False
## GPSP.join()
## print "Shutting down!"
# Preload all classes used in detection
sift = cv2.xfeatures2d.SIFT_create()
INDEX_PARAMS = None
SEARCH_PARAMS = None
FLANN = None
## Webcam logic starts
CAP = None
ARGS = None
if __name__ == "__main__":
PARSER = argparse.ArgumentParser(
description="Traffic sign recognition and intelligent speed assist.",
)
PARSER.add_argument("-d", "--device", dest="SOURCE", default=0,
help="Index of used video device. Default: 0 (/dev/video0).")
PARSER.add_argument("-g", "--gps",
dest="GPS", action="store_true", default=False,
help="Enable over speeding detection.")
PARSER.add_argument("-o", "--overspeed",
dest="COMMAND", default="false",
help="Command used in overspeed warning." \
" Default: echo OVERSPEEDING!.")
PARSER.add_argument("-c", "--cascade",
dest="CASCADE", default="lbpCascade.xml",
help="Cascade used in speed sign detection." \
" Default: lbpCascade.xml.")
PARSER.add_argument("-k", "--keypoints",
dest="MINKP", default=5,
help="Min amount of keypoints required in" \
" limit recognition. Default: 5.")
PARSER.add_argument("-D", "--downscale",
dest="DOWNSCALE", default=1,
help="Multiplier for downscaling frame before" \
" detecting signs. Default: 1.")
PARSER.add_argument("-f", "--flann",
dest="FLANNTHRESHOLD", default=0.8,
help="Threshold multiplier for accepting FLANN matches." \
" Default: 0.8.")
PARSER.add_argument("-F", "--flannchecks",
dest="CHECKS", default=50,
help="How many checks will be done in FLANN matching." \
" Default: 50.")
PARSER.add_argument("-t", "--flanntrees",
dest="TREES", default=5,
help="How many trees will be used in FLANN matching." \
" Default: 5.")
PARSER.add_argument("-m", "--matches",
dest="matches", default=2,
help="How many consecutive keypoint matches are needed" \
" before setting new limit. Default: 2.")
PARSER.add_argument("-e", "--disable-eq",
dest="EQ", action="store_false", default=True,
help="Disable histogram equalization.")
PARSER.add_argument("-M", "--morphopenclose",
dest="MORPH", action="store_true", default=False,
help="Enable morphological open/close used in removing" \
" noise from image.")
PARSER.add_argument("-T", "--trackbars",
dest="TRACKBARS", action="store_true", default=False,
help="Enable debug trackbars.")
PARSER.add_argument("-s", "--showvid",
dest="PREVIEW", action="store_true", default=False,
help="Show output video with detections.")
ARGS = PARSER.parse_args()
CAP = cv2.VideoCapture(0)
CLASSIFIER = cv2.CascadeClassifier(ARGS.CASCADE)
INDEX_PARAMS = dict(algorithm = 0, trees = ARGS.TREES)
SEARCH_PARAMS = dict(checks=ARGS.CHECKS) # or pass empty dictionary
FLANN = cv2.FlannBasedMatcher(INDEX_PARAMS, SEARCH_PARAMS)
if ARGS.PREVIEW:
cv2.namedWindow("preview")
if ARGS.GPS:
GPSP = GPSInfo()
GPSP.start()
if ARGS.TRACKBARS:
cv2.createTrackbar(
'MINKEYPOINTS',
'preview',
ARGS.MINKP,
100,
onTrackbarChange)
cv2.createTrackbar(
'DOWNSCALE',
'preview',
int(ARGS.DOWNSCALE),
20,
onTrackbarChange)
cv2.createTrackbar(
'FLANNTHRESHOLD',
'preview',
8,
10,
onTrackbarChange)
cv2.createTrackbar(
'FLANNCHECKS',
'preview',
ARGS.CHECKS,
1000,
onTrackbarChange)
cv2.createTrackbar(
'FLANNTREES',
'preview',
ARGS.TREES,
50,
onTrackbarChange)
run_logic()][1]
在OpenCV 3.2.0中，xfeatures2d模块移动到opencv_contrib。您可以通过 help(cv.xfeatures2d) 和 help(cv2.xfeatures2d.SIFT_create()) 验证您的 RPI cv2 安装是否有它。如果是这种情况，如果您不是自己构建的，则需要找到 OpenCV 二进制文件已使用 opencv_contrib 模块构建。
当我 运行 window 上的此示例代码与 opencv 3.0.0、sift = cv2.SIFT() 一起工作时，当我 运行 将相同的代码更改为 sift = cv2.xfeatures2d.SIFT_create() in Raspberry Pi with opencv 3.2.0, 显示这个错误,
"global name 'SIFT' is not defined"
#!/usr/bin/env python
# Copyright 2014 Jarmo Puttonen <jarmo.puttonen@gmail.com>. All rights reserved.
# Use of this source code is governed by a MIT-style
# licence that can be found in the LICENCE file.
"""Detect speed limits from webcam feed"""
import re
import os
import sys
import cv2
import threading
import numpy as np
import argparse
#from gps import gps, WATCH_ENABLE
def onTrackbarChange(_):
"""Pass any trackbar changes"""
class GPSInfo(threading.Thread):
"""Thread that takes care of updating GPS Speed info."""
def __init__(self):
threading.Thread.__init__(self)
self.gpsd = gps(mode=WATCH_ENABLE)
self.running = True
self.speed = 0
def run(self):
while self.running:
self.gpsd.next()
self.speed = self.gpsd.fix.speed*3.6 #from m/s to km/h
def read_paths(path):
"""Returns a list of files in given path"""
images = \[\[\] for _ in range(2)\]
for dirname, dirnames, _ in os.walk(path):
for subdirname in dirnames:
filepath = os.path.join(dirname, subdirname)
for filename in os.listdir(filepath):
try:
imgpath = str(os.path.join(filepath, filename))
images\[0\].append(imgpath)
limit = re.findall('\[0-9\]+', filename)
images\[1\].append(limit\[0\])
except IOError, (errno, strerror):
print "I/O error({0}): {1}".format(errno, strerror)
except:
print "Unexpected error:", sys.exc_info()\[0\]
raise
return images
def load_images(imgpath):
"""Loads images in given path and returns
a list containing image and keypoints"""
images = read_paths(imgpath)
imglist = \[\[\], \[\], \[\], \[\]\]
cur_img = 0
SIFT = cv2.xfeatures2d.SIFT_create()
for i in images\[0\]:
img = cv2.imread(i, 0)
imglist\[0\].append(img)
imglist\[1\].append(images\[1\]\[cur_img\])
cur_img += 1
keypoints, des = SIFT.detectAndCompute(img, None)
imglist\[2\].append(keypoints)
imglist\[3\].append(des)
return imglist
def run_flann(img):
"""Run FLANN-detector for given image with given image list"""
# Find the keypoint descriptors with SIFT
_, des = SIFT.detectAndCompute(img, None)
if des is None:
return "Unknown", 0
if len(des) < ARGS.MINKP:
return "Unknown", 0
biggest_amnt = 0
biggest_speed = 0
cur_img = 0
try:
for _ in IMAGES\[0\]:
des2 = IMAGES\[3\]\[cur_img\]
matches = FLANN.knnMatch(des2, des, k=2)
matchamnt = 0
# Find matches with Lowe's ratio test
for _, (moo, noo) in enumerate(matches):
if moo.distance < ARGS.FLANNTHRESHOLD*noo.distance:
matchamnt += 1
if matchamnt > biggest_amnt:
biggest_amnt = matchamnt
biggest_speed = IMAGES\[1\]\[cur_img\]
cur_img += 1
if biggest_amnt > ARGS.MINKP:
return biggest_speed, biggest_amnt
else:
return "Unknown", 0
except Exception, exept:
print exept
return "Unknown", 0
IMAGES = load_images("data")
def run_logic():
"""Run TSR and ISA"""
lastlimit = "00"
lastdetect = "00"
downscale = ARGS.DOWNSCALE
matches = 0
possiblematch = "00"
try:
if CAP.isOpened():
rval, frame = CAP.read()
print("Camera opened and frame read")
else:
rval = False
print("Camera not opened")
while rval:
origframe = frame
if ARGS.MORPH:
frame = cv2.morphologyEx(
frame,
cv2.MORPH_OPEN,
cv2.getStructuringElement(cv2.MORPH_RECT,(2,2))
)
frame = cv2.morphologyEx(
frame,
cv2.MORPH_CLOSE,
cv2.getStructuringElement(cv2.MORPH_RECT,(2,2))
)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if ARGS.EQ:
cv2.equalizeHist(frame, frame)
if ARGS.TRACKBARS:
ARGS.MINKP = cv2.getTrackbarPos('MINKEYPOINTS','preview')
downscale = cv2.getTrackbarPos('DOWNSCALE','preview')
ARGS.FLANNTHRESHOLD = float(
cv2.getTrackbarPos('FLANNTHRESHOLD','preview')
)/10
ARGS.CHECKS = cv2.getTrackbarPos('FLANNCHECKS','preview')
ARGS.TREES = cv2.getTrackbarPos('FLANNTREES','preview')
scaledsize = (frame.shape\[1\]/downscale, frame.shape\[0\]/downscale)
scaledframe = cv2.resize(frame, scaledsize)
# Detect signs in downscaled frame
signs = CLASSIFIER.detectMultiScale(
scaledframe,
1.1,
5,
0,
(10, 10),
(200, 200))
for sign in signs:
xpos, ypos, width, height = \[ i*downscale for i in sign \]
crop_img = frame\[ypos:ypos+height, xpos:xpos+width\]
sized = cv2.resize(crop_img, (128, 128))
comp = "Unknown"
comp, amnt = run_flann(sized)
if comp != "Unknown":
if comp != lastlimit:
# Require two consecutive hits for new limit.
if comp == lastdetect:
possiblematch = comp
matches = matches + 1
if matches >= ARGS.matches:
print "New speed limit: "+possiblematch
lastlimit = possiblematch
matches = 0
else:
possiblematch = "00"
matches = 0
cv2.rectangle(
origframe,
(xpos, ypos),
(xpos+width, ypos+height),
(0, 0, 255))
cv2.putText(
origframe,
"Speed limit: "+comp+" KP: "+str(amnt),
(xpos,ypos-5),
cv2.FONT_HERSHEY_SIMPLEX,
0.4,
(0,0,255),
1,)
else:
cv2.rectangle(
origframe,
(xpos,ypos),
(xpos+width,ypos+height),
(255,0,0))
cv2.putText(
origframe,
"UNKNOWN SPEED LIMIT",
(xpos,ypos-5),
cv2.FONT_HERSHEY_SIMPLEX,
0.4,
(255,0,0),
1,)
comp = lastdetect
lastdetect = comp
cv2.putText(
origframe,
"Current speed limit: "+str(lastlimit)+" km/h.",
(5,50),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(0,0,0),
2
)
cv2.imshow("preview", origframe)
if ARGS.PREVIEW:
cv2.imshow("preview", origframe)
_ = cv2.waitKey(20)
rval, frame = CAP.read()
except (KeyboardInterrupt, Exception), exept:
print exept
## if ARGS.GPS:
## print "Killing GPS"
## GPSP.running = False
## GPSP.join()
## print "Shutting down!"
# Preload all classes used in detection
sift = cv2.xfeatures2d.SIFT_create()
INDEX_PARAMS = None
SEARCH_PARAMS = None
FLANN = None
## Webcam logic starts
CAP = None
ARGS = None
if __name__ == "__main__":
PARSER = argparse.ArgumentParser(
description="Traffic sign recognition and intelligent speed assist.",
)
PARSER.add_argument("-d", "--device", dest="SOURCE", default=0,
help="Index of used video device. Default: 0 (/dev/video0).")
PARSER.add_argument("-g", "--gps",
dest="GPS", action="store_true", default=False,
help="Enable over speeding detection.")
PARSER.add_argument("-o", "--overspeed",
dest="COMMAND", default="false",
help="Command used in overspeed warning." \
" Default: echo OVERSPEEDING!.")
PARSER.add_argument("-c", "--cascade",
dest="CASCADE", default="lbpCascade.xml",
help="Cascade used in speed sign detection." \
" Default: lbpCascade.xml.")
PARSER.add_argument("-k", "--keypoints",
dest="MINKP", default=5,
help="Min amount of keypoints required in" \
" limit recognition. Default: 5.")
PARSER.add_argument("-D", "--downscale",
dest="DOWNSCALE", default=1,
help="Multiplier for downscaling frame before" \
" detecting signs. Default: 1.")
PARSER.add_argument("-f", "--flann",
dest="FLANNTHRESHOLD", default=0.8,
help="Threshold multiplier for accepting FLANN matches." \
" Default: 0.8.")
PARSER.add_argument("-F", "--flannchecks",
dest="CHECKS", default=50,
help="How many checks will be done in FLANN matching." \
" Default: 50.")
PARSER.add_argument("-t", "--flanntrees",
dest="TREES", default=5,
help="How many trees will be used in FLANN matching." \
" Default: 5.")
PARSER.add_argument("-m", "--matches",
dest="matches", default=2,
help="How many consecutive keypoint matches are needed" \
" before setting new limit. Default: 2.")
PARSER.add_argument("-e", "--disable-eq",
dest="EQ", action="store_false", default=True,
help="Disable histogram equalization.")
PARSER.add_argument("-M", "--morphopenclose",
dest="MORPH", action="store_true", default=False,
help="Enable morphological open/close used in removing" \
" noise from image.")
PARSER.add_argument("-T", "--trackbars",
dest="TRACKBARS", action="store_true", default=False,
help="Enable debug trackbars.")
PARSER.add_argument("-s", "--showvid",
dest="PREVIEW", action="store_true", default=False,
help="Show output video with detections.")
ARGS = PARSER.parse_args()
CAP = cv2.VideoCapture(0)
CLASSIFIER = cv2.CascadeClassifier(ARGS.CASCADE)
INDEX_PARAMS = dict(algorithm = 0, trees = ARGS.TREES)
SEARCH_PARAMS = dict(checks=ARGS.CHECKS) # or pass empty dictionary
FLANN = cv2.FlannBasedMatcher(INDEX_PARAMS, SEARCH_PARAMS)
if ARGS.PREVIEW:
cv2.namedWindow("preview")
if ARGS.GPS:
GPSP = GPSInfo()
GPSP.start()
if ARGS.TRACKBARS:
cv2.createTrackbar(
'MINKEYPOINTS',
'preview',
ARGS.MINKP,
100,
onTrackbarChange)
cv2.createTrackbar(
'DOWNSCALE',
'preview',
int(ARGS.DOWNSCALE),
20,
onTrackbarChange)
cv2.createTrackbar(
'FLANNTHRESHOLD',
'preview',
8,
10,
onTrackbarChange)
cv2.createTrackbar(
'FLANNCHECKS',
'preview',
ARGS.CHECKS,
1000,
onTrackbarChange)
cv2.createTrackbar(
'FLANNTREES',
'preview',
ARGS.TREES,
50,
onTrackbarChange)
run_logic()][1]
在OpenCV 3.2.0中，xfeatures2d模块移动到opencv_contrib。您可以通过 help(cv.xfeatures2d) 和 help(cv2.xfeatures2d.SIFT_create()) 验证您的 RPI cv2 安装是否有它。如果是这种情况，如果您不是自己构建的，则需要找到 OpenCV 二进制文件已使用 opencv_contrib 模块构建。