nom global « EIPD » est pas défini en python (Pi 3 B)

Question

Lorsque j'ai couru cet exemple de code dans la fenêtre avec opencv 3.0.0, sift = cv2.SIFT() œuvres, et quand je courais le même code avec le changement de sift = cv2.xfeatures2d.SIFT_create() dans Raspberry Pi avec opencv 3.2.0, il montre cette erreur,

"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] 

Answer 1

Dans OpenCV 3.2.0, le module xfeatures2d est déplacé vers opencv_contrib. Vous pouvez vérifier si votre installation RPIcv2 l'a ou non par help(cv.xfeatures2d) et help(cv2.xfeatures2d.SIFT_create()). Si c'est le cas, vous devez trouver un binaire OpenCV qui a été construit avec les modules opencv_contrib si vous ne l'avez pas construit vous-même.