Make OpenFace work with Python 3 (#231)

* Make OpenFace Python 3 compatible, retaining Python 2 compatibility. Tests could be parameterised to test for either Python 2 or 3. Tests currently fail due to different library calculation outcomes? * Tiny change to remove the dependency on nosetest3. * Dockerfile updates for OpenFace + Python3 + Dlib19.2 + OpenCV3.2 * Revert "Dockerfile updates for OpenFace + Python3 + Dlib19.2 + OpenCV3.2" This reverts commit ec08fc9226. * Change for python2 backwards compatibility * Change for python2 backwards compatibility * Change to ensure python2 backwards compatibility * Change to ensure python2 backwards compatibility
2017-04-25 07:50:16 +10:00 · 2017-04-25 07:50:16 +10:00 · 71ee6ee747
parent 9c02f78a86
commit 71ee6ee747
11 changed files with 85 additions and 67 deletions
--- a/demos/classifier.py
+++ b/demos/classifier.py
@ -26,6 +26,7 @@ import argparse
 import cv2
 import os
 import pickle
+import sys

 from operator import itemgetter

@ -171,8 +172,11 @@ def train(args):


 def infer(args, multiple=False):
-    with open(args.classifierModel, 'r') as f:
-        (le, clf) = pickle.load(f)
+    with open(args.classifierModel, 'rb') as f:
+        if sys.version_info[0] < 3:
+                (le, clf) = pickle.load(f)
+        else:
+                (le, clf) = pickle.load(f, encoding='latin1')

    for img in args.imgs:
        print("\n=== {} ===".format(img))
@ -190,10 +194,10 @@ def infer(args, multiple=False):
            if args.verbose:
                print("Prediction took {} seconds.".format(time.time() - start))
            if multiple:
-                print("Predict {} @ x={} with {:.2f} confidence.".format(person, bbx,
+                print("Predict {} @ x={} with {:.2f} confidence.".format(person.decode('utf-8'), bbx,
                                                                         confidence))
            else:
-                print("Predict {} with {:.2f} confidence.".format(person, confidence))
+                print("Predict {} with {:.2f} confidence.".format(person.decode('utf-8'), confidence))
            if isinstance(clf, GMM):
                dist = np.linalg.norm(rep - clf.means_[maxI])
                print("  + Distance from the mean: {}".format(dist))
--- a/demos/classifier_webcam.py
+++ b/demos/classifier_webcam.py
@ -33,6 +33,7 @@ import argparse
 import cv2
 import os
 import pickle
+import sys

 import numpy as np
 np.set_printoptions(precision=2)
@ -97,13 +98,16 @@ def getRep(bgrImg):
        print("Neural network forward pass took {} seconds.".format(
            time.time() - start))

-    # print reps
+    # print (reps)
    return reps


 def infer(img, args):
    with open(args.classifierModel, 'r') as f:
-        (le, clf) = pickle.load(f)  # le - label and clf - classifer
+        if sys.version_info[0] < 3:
+                (le, clf) = pickle.load(f)  # le - label and clf - classifer
+        else:
+                (le, clf) = pickle.load(f, encoding='latin1')  # le - label and clf - classifer

    reps = getRep(img)
    persons = []
@ -112,21 +116,21 @@ def infer(img, args):
        try:
            rep = rep.reshape(1, -1)
        except:
-            print "No Face detected"
+            print ("No Face detected")
            return (None, None)
        start = time.time()
        predictions = clf.predict_proba(rep).ravel()
-        # print predictions
+        # print (predictions)
        maxI = np.argmax(predictions)
        # max2 = np.argsort(predictions)[-3:][::-1][1]
        persons.append(le.inverse_transform(maxI))
-        # print str(le.inverse_transform(max2)) + ": "+str( predictions [max2])
+        # print (str(le.inverse_transform(max2)) + ": "+str( predictions [max2]))
        # ^ prints the second prediction
        confidences.append(predictions[maxI])
        if args.verbose:
            print("Prediction took {} seconds.".format(time.time() - start))
            pass
-        # print("Predict {} with {:.2f} confidence.".format(person, confidence))
+        # print("Predict {} with {:.2f} confidence.".format(person.decode('utf-8'), confidence))
        if isinstance(clf, GMM):
            dist = np.linalg.norm(rep - clf.means_[maxI])
            print("  + Distance from the mean: {}".format(dist))
@ -185,7 +189,7 @@ if __name__ == '__main__':
    while True:
        ret, frame = video_capture.read()
        persons, confidences = infer(frame, args)
-        print "P: " + str(persons) + " C: " + str(confidences)
+        print ("P: " + str(persons) + " C: " + str(confidences))
        try:
            # append with two floating point precision
            confidenceList.append('%.2f' % confidences[0])
--- a/evaluation/lfw-classification-unknown.py
+++ b/evaluation/lfw-classification-unknown.py
@ -181,10 +181,13 @@ def getRep(imgPath):

 def inferFromTest(args):
    for clfChoice in clfChoices:
-        print "==============="
-        print "Using the classifier: " + clfChoice
+        print ("===============")
+        print ("Using the classifier: " + clfChoice)
        with open(os.path.join(args.featureFolder[0], clfChoice + ".pkl"), 'r') as f_clf:
-            (le, clf) = pickle.load(f_clf)
+            if sys.version_info[0] < 3:
+                (le, clf) = pickle.load(f_clf)
+            else:
+                (le, clf) = pickle.load(f_clf, encoding='latin1')

        correctPrediction = 0
        inCorrectPrediction = 0
@ -204,7 +207,7 @@ def inferFromTest(args):
                try:
                    rep = getRep(img).reshape(1, -1)
                except Exception as e:
-                    print e
+                    print (e)
                    continue
                start = time.time()
                predictions = clf.predict_proba(rep).ravel()
@ -218,7 +221,7 @@ def inferFromTest(args):
                if args.verbose:
                    print(
                        "Predict {} with {:.2f} confidence.".format(
-                            person, confidence))
+                            person.decode('utf-8'), confidence))

                sumConfidence += confidence

@ -234,11 +237,11 @@ def inferFromTest(args):
                    dist = np.linalg.norm(rep - clf.means_[maxI])
                    print("  + Distance from the mean: {}".format(dist))

-        print "Results for the classifier: " + clfChoice
-        print "Correct Prediction :" + str(correctPrediction)
-        print "In-correct Prediction: " + str(inCorrectPrediction)
-        print "Accuracy :" + str(float(correctPrediction) / (correctPrediction + inCorrectPrediction))
-        print "Avg Confidence: " + str(float(sumConfidence) / (correctPrediction + inCorrectPrediction))
+        print ("Results for the classifier: " + clfChoice)
+        print ("Correct Prediction :" + str(correctPrediction))
+        print ("In-correct Prediction: " + str(inCorrectPrediction))
+        print ("Accuracy :" + str(float(correctPrediction) / (correctPrediction + inCorrectPrediction)))
+        print ("Avg Confidence: " + str(float(sumConfidence) / (correctPrediction + inCorrectPrediction)))


 def preprocess(args):
@ -256,8 +259,8 @@ def preprocess(args):
        try:
            noOfImages.append(len(os.listdir(folder)))
            folderName.append(folder.split('/')[-1:][0])
-            # print folder.split('/')[-1:][0] +": " +
-            # str(len(os.listdir(folder)))
+            # print (folder.split('/')[-1:][0] +": " +
+            # str(len(os.listdir(folder))))
        except:
            pass

@ -269,7 +272,7 @@ def preprocess(args):
        for f, n in zip(folderName_sorted, noOfImages_sorted):
            text_file.write("{} : {} \n".format(f, n))
    if args.verbose:
-        print "Sorting lfw dataset took {} seconds.".format(time.time() - start)
+        print ("Sorting lfw dataset took {} seconds.".format(time.time() - start))
        start = time.time()

    # Copy known train dataset
@ -289,7 +292,7 @@ def preprocess(args):
                    print('Directory not copied. Error: %s' % e)

    if args.verbose:
-        print "Copying train dataset from lfw took {} seconds.".format(time.time() - start)
+        print ("Copying train dataset from lfw took {} seconds.".format(time.time() - start))
        start = time.time()

    # Take 10% images from train dataset as test dataset for known
@ -309,8 +312,8 @@ def preprocess(args):
                destPath, 'test_known_raw', folder.split('/')[-1:][0])):
            os.makedirs(os.path.join(destPath, 'test_known_raw',
                                     folder.split('/')[-1:][0]))
-            # print "Created {}".format(os.path.join(destPath,
-            # 'test_known_raw', folder.split('/')[-1:][0]))
+            # print ("Created {}".format(os.path.join(destPath,
+            # 'test_known_raw', folder.split('/')[-1:][0])))
        for i in range(int(0.9 * len(images)), len(images)):
            destFile = os.path.join(destPath, 'test_known_raw', folder.split(
                '/')[-1:][0], images[i].split('/')[-1:][0])
@ -319,7 +322,7 @@ def preprocess(args):
            except:
                pass
    if args.verbose:
-        print "Spliting lfw dataset took {} seconds.".format(time.time() - start)
+        print ("Spliting lfw dataset took {} seconds.".format(time.time() - start))
        start = time.time()

    # Copy unknown test dataset
@ -339,7 +342,7 @@ def preprocess(args):
                    print('Directory not copied. Error: %s' % e)

    if args.verbose:
-        print "Copying test dataset from lfw took {} seconds.".format(time.time() - start)
+        print ("Copying test dataset from lfw took {} seconds.".format(time.time() - start))
        start = time.time()

    class Args():
@ -380,7 +383,7 @@ def preprocess(args):
        p.join()

    if args.verbose:
-        print "Aligning the raw train data took {} seconds.".format(time.time() - start)
+        print ("Aligning the raw train data took {} seconds.".format(time.time() - start))
        start = time.time()

    os.system(
@ -394,7 +397,7 @@ def preprocess(args):
            'train_known_aligned'))

    if args.verbose:
-        print "Extracting features from aligned train data took {} seconds.".format(time.time() - start)
+        print ("Extracting features from aligned train data took {} seconds.".format(time.time() - start))
        start = time.time()


--- a/evaluation/lfw-classification.py
+++ b/evaluation/lfw-classification.py
@ -125,7 +125,10 @@ def cacheToFile(file_name):
    def decorator(original_func):
        global cache
        try:
-            cache = pickle.load(open(file_name, 'rb'))
+            if sys.version_info[0] < 3:
+                    cache = pickle.load(open(file_name, 'rb'))
+            else:
+                    cache = pickle.load(open(file_name, 'rb'), encoding='latin1')
        except:
            cache = None

--- a/openface/init.py
+++ b/openface/init.py
@ -1,7 +1,9 @@
 # flake8: noqa

+from __future__ import absolute_import
+
 from .align_dlib import AlignDlib
 from .torch_neural_net import TorchNeuralNet

-import data
-import helper
+from . import data
+from . import helper
--- a/openface/torch_neural_net.py
+++ b/openface/torch_neural_net.py
@ -81,7 +81,7 @@ class TorchNeuralNet:
                    '-model', model, '-imgDim', str(imgDim)]
        if cuda:
            self.cmd.append('-cuda')
-        self.p = Popen(self.cmd, stdin=PIPE, stdout=PIPE, bufsize=0)
+        self.p = Popen(self.cmd, stdin=PIPE, stdout=PIPE, bufsize=0, universal_newlines=True)

        def exitHandler():
            if self.p.poll() is None:
@ -160,7 +160,7 @@ cmd: {}
 stdout: {}
 """.format(self.cmd, self.p.stdout.read()))

-        self.p.stdin.write(imgPath + "\n")
+        self.p.stdin.write(imgPath + '\n')
        output = self.p.stdout.readline()
        try:
            rep = [float(x) for x in output.strip().split(',')]
--- a/run-tests.sh
+++ b/run-tests.sh
@ -4,4 +4,4 @@ set -e

 cd $(dirname $0)

-nosetests-2.7 -v
+nosetests -v
--- a/tests/openface_api_tests.py
+++ b/tests/openface_api_tests.py
@ -52,6 +52,8 @@ def test_pipeline():
    # assert np.isclose(norm(rgbImg), 11.1355)

    bb = align.getLargestFaceBoundingBox(rgbImg)
+    print ("Bounding box found was: ")
+    print (bb)
    assert bb.left() == 341
    assert bb.right() == 1006
    assert bb.top() == 193
--- a/tests/openface_batch_represent_tests.py
+++ b/tests/openface_batch_represent_tests.py
@ -40,19 +40,19 @@ def test_batch_represent():

    workDir = tempfile.mkdtemp(prefix='OpenFaceBatchRep-')

-    cmd = ['python2', os.path.join(openfaceDir, 'util', 'align-dlib.py'),
+    cmd = ['python3', os.path.join(openfaceDir, 'util', 'align-dlib.py'),
           os.path.join(lfwSubset, 'raw'), 'align', 'outerEyesAndNose',
           os.path.join(workDir, 'aligned')]
-    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)
    assert p.returncode == 0

-    cmd = ['python2', os.path.join(openfaceDir, 'util', 'align-dlib.py'),
+    cmd = ['python3', os.path.join(openfaceDir, 'util', 'align-dlib.py'),
           os.path.join(lfwSubset, 'raw'), 'align', 'outerEyesAndNose',
           os.path.join(workDir, 'aligned')]
-    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)
@ -61,7 +61,7 @@ def test_batch_represent():
    cmd = ['th', './batch-represent/main.lua',
           '-data', os.path.join(workDir, 'aligned'),
           '-outDir', os.path.join(workDir, 'reps')]
-    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)
--- a/tests/openface_demo_tests.py
+++ b/tests/openface_demo_tests.py
@ -28,61 +28,61 @@ lfwSubset = os.path.join(openfaceDir, 'data', 'lfw-subset')


 def test_compare_demo():
-    cmd = ['python2', os.path.join(openfaceDir, 'demos', 'compare.py'),
+    cmd = ['python3', os.path.join(openfaceDir, 'demos', 'compare.py'),
           os.path.join(exampleImages, 'lennon-1.jpg'),
           os.path.join(exampleImages, 'lennon-2.jpg')]
-    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)
-    assert "0.763" in out
+    assert '0.763' in out


 def test_classification_demo_pretrained():
-    cmd = ['python2', os.path.join(openfaceDir, 'demos', 'classifier.py'),
+    cmd = ['python3', os.path.join(openfaceDir, 'demos', 'classifier.py'),
           'infer',
           os.path.join(openfaceDir, 'models', 'openface',
                        'celeb-classifier.nn4.small2.v1.pkl'),
           os.path.join(exampleImages, 'carell.jpg')]
-    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)
-    assert "Predict SteveCarell with 0.97 confidence." in out
+    assert 'Predict SteveCarell with 0.97 confidence.' in out


 def test_classification_demo_pretrained_multi():
-    cmd = ['python2', os.path.join(openfaceDir, 'demos', 'classifier.py'),
+    cmd = ['python3', os.path.join(openfaceDir, 'demos', 'classifier.py'),
           'infer', '--multi',
           os.path.join(openfaceDir, 'models', 'openface',
                        'celeb-classifier.nn4.small2.v1.pkl'),
           os.path.join(exampleImages, 'longoria-cooper.jpg')]
-    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)
-    assert "Predict EvaLongoria @ x=91 with 0.99 confidence." in out
-    assert "Predict BradleyCooper @ x=191 with 0.99 confidence." in out
+    assert 'Predict EvaLongoria @ x=91 with 0.99 confidence.' in out
+    assert 'Predict BradleyCooper @ x=191 with 0.99 confidence.' in out


 def test_classification_demo_training():
-    assert os.path.isdir(lfwSubset), "Get lfw-subset by running ./data/download-lfw-subset.sh"
+    assert os.path.isdir(lfwSubset), 'Get lfw-subset by running ./data/download-lfw-subset.sh'

    workDir = tempfile.mkdtemp(prefix='OpenFaceCls-')

-    cmd = ['python2', os.path.join(openfaceDir, 'util', 'align-dlib.py'),
+    cmd = ['python3', os.path.join(openfaceDir, 'util', 'align-dlib.py'),
           os.path.join(lfwSubset, 'raw'), 'align', 'outerEyesAndNose',
           os.path.join(workDir, 'aligned')]
-    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)
    assert p.returncode == 0

-    cmd = ['python2', os.path.join(openfaceDir, 'util', 'align-dlib.py'),
+    cmd = ['python3', os.path.join(openfaceDir, 'util', 'align-dlib.py'),
           os.path.join(lfwSubset, 'raw'), 'align', 'outerEyesAndNose',
           os.path.join(workDir, 'aligned')]
-    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)
@ -91,26 +91,26 @@ def test_classification_demo_training():
    cmd = ['th', './batch-represent/main.lua',
           '-data', os.path.join(workDir, 'aligned'),
           '-outDir', os.path.join(workDir, 'reps')]
-    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)
    assert p.returncode == 0

-    cmd = ['python2', os.path.join(openfaceDir, 'demos', 'classifier.py'),
+    cmd = ['python3', os.path.join(openfaceDir, 'demos', 'classifier.py'),
           'train',
           os.path.join(workDir, 'reps')]
-    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)
    assert p.returncode == 0

-    cmd = ['python2', os.path.join(openfaceDir, 'demos', 'classifier.py'),
+    cmd = ['python3', os.path.join(openfaceDir, 'demos', 'classifier.py'),
           'infer',
           os.path.join(workDir, 'reps', 'classifier.pkl'),
           os.path.join(lfwSubset, 'raw', 'Adrien_Brody', 'Adrien_Brody_0001.jpg')]
-    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)
--- a/tests/openface_neural_net_training_tests.py
+++ b/tests/openface_neural_net_training_tests.py
@ -37,19 +37,19 @@ def test_dnn_training():
        lfwSubset), "Get lfw-subset by running ./data/download-lfw-subset.sh"

    imgWorkDir = tempfile.mkdtemp(prefix='OpenFaceTrainingTest-Img-')
-    cmd = ['python2', os.path.join(openfaceDir, 'util', 'align-dlib.py'),
+    cmd = ['python3', os.path.join(openfaceDir, 'util', 'align-dlib.py'),
           os.path.join(lfwSubset, 'raw'), 'align', 'outerEyesAndNose',
           os.path.join(imgWorkDir, 'aligned')]
-    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)
    assert p.returncode == 0

-    cmd = ['python2', os.path.join(openfaceDir, 'util', 'align-dlib.py'),
+    cmd = ['python3', os.path.join(openfaceDir, 'util', 'align-dlib.py'),
           os.path.join(lfwSubset, 'raw'), 'align', 'outerEyesAndNose',
           os.path.join(imgWorkDir, 'aligned')]
-    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)
@ -69,7 +69,7 @@ def test_dnn_training():
           '-cuda', '-cudnn', '-testing',
           '-nDonkeys', '-1']
    p = Popen(cmd, stdout=PIPE, stderr=PIPE,
-              cwd=os.path.join(openfaceDir, 'training'))
+              cwd=os.path.join(openfaceDir, 'training'), universal_newlines=True)
    (out, err) = p.communicate()
    print(out)
    print(err)