def predict(sound_file, prototxt, model, output_path):
image_files = wav_to_images(sound_file, output_path)
caffe.set_mode_cpu()
net = caffe.Classifier(
prototxt,
model,
#image_dims=(224, 224)
#channel_swap=(2,1,0),
raw_scale=255 # convert 0..255 values into range 0..1
#caffe.TEST
)
input_images = np.array([
caffe.io.load_image(image_file, color=False)
for image_file in image_files["melfilter"]
])
#input_images = np.swapaxes(input_images, 1, 3)
#prediction = net.forward_all(data=input_images)["prob"]
prediction = net.predict(
input_images, False
) # predict takes any number of images, and formats them for the Caffe net automatically
print(prediction)
print('prediction shape:', prediction[0].shape)
print('predicted class:', prediction[0].argmax())
print(image_files)
return prediction
def predict(sound_file, prototxt, model, output_path):
image_files = wav_to_images(sound_file, output_path)
caffe.set_mode_cpu()
net = caffe.Classifier(
prototxt,
model,
# image_dims=(224, 224)
# channel_swap=(2,1,0),
raw_scale=255 # convert 0..255 values into range 0..1
# caffe.TEST
)
input_images = np.array([caffe.io.load_image(image_file, color=False) for image_file in image_files["melfilter"]])
# input_images = np.swapaxes(input_images, 1, 3)
# prediction = net.forward_all(data=input_images)["prob"]
prediction = net.predict(
input_images, False
) # predict takes any number of images, and formats them for the Caffe net automatically
print prediction
print "prediction shape:", prediction[0].shape
print "predicted class:", prediction[0].argmax()
print image_files
return prediction
def evaluate(input_csv, proto, model):
''' Run evaluation on a list of WAV, label files '''
if not os.path.isdir("tmp"):
os.mkdir("tmp")
correct_files = []
incorrect_files = []
skipped_files = []
caffe.set_mode_cpu()
net = caffe.Classifier(proto, model, raw_scale=255) # convert 0..255 values into range 0..1
reader = csv.reader(file(input_csv, 'rU'))
for filename, label in reader:
# Convert WAV to images
image_files = wav_to_images(filename, "tmp")
# some files fail during the conversion, so skip them
if len(image_files["melfilter"]) == 0:
skipped_files.append(filename)
continue
# Call Caffe and do predicition
input_images = np.array([caffe.io.load_image(image_file, color=False) for image_file in image_files["melfilter"]])
prediction = net.predict(input_images, False)
mean_prediction = np.mean(prediction, axis=0)
# Evaluation
best_label = mean_prediction.argmax()
if best_label == np.int64(label):
correct_files.append(filename)
else:
incorrect_files.append(filename)
shutil.rmtree("tmp")
# Stats
num_correct = len(correct_files)
num_incorrect = len(incorrect_files)
print "Correctly Classified: {0} ({1:.2f}%)".format(num_correct, num_correct / float(num_correct + num_incorrect) * 100)
print "Incorrectly Classified: {0} ({1:.2f}%)".format(num_incorrect, num_incorrect / float(num_correct + num_incorrect) * 100)
print "Skipped Files: {0}".format(len(skipped_files))
# Save correct / incorrect filenames in txt file
correct_out = open("correct_files.txt", "wb")
incorrect_out = open("incorrect_files.txt", "wb")
skipped_out = open("skipped_files.txt", "wb")
correct_out.write("\n".join(correct_files))
incorrect_out.write("\n".join(incorrect_files))
skipped_out.write("\n".join(skipped_files))
correct_out.close()
incorrect_out.close()
skipped_out.close()
