def main(sourcedatafolder, targetdatafile):
allimages = []
allfaces = []
allgenres = []
allhashes = []
allfiles = []
for file in glob.glob(join(sourcedatafolder, "*")):
filehash, genre = get_metadata(file)
filename = get_filename(file)
allhashes.append(filehash)
allgenres.append(genre)
allfiles.append(filename)
image = load_image(file)
image_gray = make_grayscale(image)
faces = find_faces(image_gray, image)
#imageblackwhite = make_binary(image_gray)
#faces = find_faces(imageblackwhite, image)
allfaces.append(faces)
### for showing images comment out show_image
#print("faces:", faces)
#show_image(image)
#show_image(imageblackwhite)
save_data(allhashes, allgenres, allfaces, allfiles, targetdatafile)
docfile.write(sourcedatafolder, targetdatafile, documentationfile, __doc__,
tail, __file__)
def main(sourcedatafolder, targetdatafile, tail):
allhashes = []
allgenres = []
all_median = []
all_stdev = []
all_max = []
count = 0
for file in glob.glob(join(sourcedatafolder, "*")):
count += 1
filehash, genre = get_metadata(file)
allhashes.append(filehash)
allgenres.append(genre)
image = load_image(file)
gray_channel = get_channel(image)
gray_hist = make_histogram(image)
gray_median, gray_stdev = get_channel_data(gray_channel)
gray_max = get_histogramdata(gray_hist)
all_median.append(gray_median)
all_stdev.append(gray_stdev)
all_max.append(gray_max)
if count % 100 == 0:
print(str(count))
save_data(allhashes, allgenres, all_median, all_stdev, all_max,
targetdatafile)
docfile.write(sourcedatafolder, targetdatafile, documentationfile,
docstring, tail, __file__)
def main(sourcedatafile, targetdatafile, documentationfile, tail):
"""
Visualize the feature distribution.
"""
data = load_data(sourcedatafile)
genres = get_metadata(data)
featurematrix = get_featurematrix(data)
make_scatterplot(genres, featurematrix, targetdatafile)
docfile.write(sourcedatafile, targetdatafile, documentationfile, docstring, tail, __file__)
def main(sourcedatafile, targetdatafile, documentationfile, classifiertype, tail):
"""
Classify music albums into subgenres based on their cover art.
"""
data = load_data(sourcedatafile)
genres = get_metadata(data)
featurematrix = get_featurematrix( data)
classifier = define_classifier(classifiertype)
perform_classification(featurematrix, genres, classifier)
docfile.write(sourcedatafile, targetdatafile, documentationfile, docstring, tail, __file__)
def main(sourcedatafolder, targetdatafolder, documentationfile, docstring, tail):
if not os.path.exists(targetdatafolder):
os.makedirs(targetdatafolder)
for file in glob.glob(sourcedatafolder + "/*"):
basename, ext = os.path.basename(file).split(".")
image = bif.load(file)
image = bif.resize(image, 500, 500)
image = bif.mode(image, "bw")
bif.save(image, basename, targetdatafolder)
docfile.write(sourcedatafolder, targetdatafolder, documentationfile, docstring, tail, __file__)
def main(first_csv, second_csv):
merged = merge(first_csv, second_csv)
save_data(merged)
first_tmp = os.path.basename(os.path.normpath(
first_csv)) # get filenames of the CSV files for use in the docfile
second_tmp = os.path.basename(os.path.normpath(second_csv))
docfile.write(
first_csv, merged_csv, documentationfile, __doc__ + " (" + first_tmp +
" with " + second_tmp + " => " + os.path.basename(merged_csv) + ")\n",
tail, __file__)
def main(sourcedatafile, targetdatafile, documentationfile, classifiertype, tail):
"""
Classify music albums into subgenres based on their cover art.
"""
data = load_data(sourcedatafile)
# print(type(data))
genres = get_metadata(data)
featurematrix = get_featurematrix(data)
classifier = define_classifier(classifiertype, n_neighbors, weights)
features_test, labels_test, labels_predicted = perform_classification(featurematrix, genres, classifier)
save_data( labels_test, labels_predicted, targetdatafile)
docfile.write(sourcedatafile, targetdatafile, documentationfile, docstring, tail, __file__)
def main(sourcedatafile, targetdatafile, documentationfile, classifiertype, tail):
"""
Classify music albums into subgenres based on their cover art.
"""
data = load_data(sourcedatafile)
labels_test, labels_predicted, classes = unpack_data(data)
# genres = get_metadata(data)
# featurematrix = get_featurematrix( data)
# classifier = define_classifier(classifiertype)
# labels_test, labels_predicted, classes = perform_classification(featurematrix, genres, classifier)
make_confmatrix(labels_test, labels_predicted, classes, targetdatafile)
docfile.write(sourcedatafile, targetdatafile, documentationfile, docstring, tail, __file__)
def main(sourcedatafolder, targetdatafile, tail):
allhashes = []
allgenres = []
all_median_blue = []
all_median_green = []
all_median_red = []
all_stdev_blue = []
all_stdev_green = []
all_stdev_red = []
allhist_max_blue = []
allhist_max_green = []
allhist_max_red = []
for file in glob.glob(join(sourcedatafolder, "*")):
filehash, genre = get_metadata(file)
allhashes.append(filehash)
allgenres.append(genre)
image = load_image(file)
h_blue, h_green, h_red = make_histograms(image)
hist_max_blue = get_histogramdata(h_blue)
hist_max_green = get_histogramdata(h_green)
hist_max_red = get_histogramdata(h_red)
blue, green, red = get_channels(image)
median_blue, stdev_blue = get_channel_data(blue)
median_green, stdev_green = get_channel_data(green)
median_red, stdev_red = get_channel_data(red)
all_median_blue.append(median_blue)
all_median_green.append(median_green)
all_median_red.append(median_red)
all_stdev_blue.append(stdev_blue)
all_stdev_green.append(stdev_green)
all_stdev_red.append(stdev_red)
allhist_max_blue.append(hist_max_blue)
allhist_max_green.append(hist_max_green)
allhist_max_red.append(hist_max_red)
save_data(allhashes, allgenres, all_median_blue, all_median_green,
all_median_red, all_stdev_blue, all_stdev_green, all_stdev_red,
allhist_max_blue, allhist_max_green, allhist_max_red,
targetdatafile)
docfile.write(sourcedatafolder, targetdatafile, documentationfile,
docstring, tail, __file__)
print(all_median_blue[0])
print(all_stdev_blue[0])
print(allhist_max_blue[0])
def main(sourcedatafolder, targetdatafile, tail):
allhashes = []
allgenres = []
allhist_median = []
allhist_stdev = []
allhist_max = []
for file in glob.glob(join(sourcedatafolder, "*")):
filehash, genre = get_metadata(file)
allhashes.append(filehash)
allgenres.append(genre)
image = load_image(file)
image_gray = make_grayscale(image)
histogram = make_histogram(image_gray)
hist_median, hist_stdev, hist_max = get_histogramdata(histogram)
allhist_median.append(hist_median)
allhist_stdev.append(hist_stdev)
allhist_max.append(hist_max)
save_data(allhashes, allgenres, allhist_median, allhist_stdev, allhist_max, targetdatafile)
docfile.write(sourcedatafolder, targetdatafile, documentationfile, docstring, tail, __file__)
def main(sourcedatafolder, targetdatafile):
allimages = []
allpeople = []
allgenres = []
allhashes = []
allfiles = []
for file in glob.glob(join(sourcedatafolder, "*")):
filehash, genre = get_metadata(file)
filename = get_filename(file)
allhashes.append(filehash)
allgenres.append(genre)
allfiles.append(filename)
image = load_image(file)
image_gray = image
people = find_people(image_gray)
allpeople.append(people)
### for showing images comment out show_image
#print("people:", people)
#show_image(image)
save_data(allhashes, allgenres, allpeople, allfiles, targetdatafile)
docfile.write(sourcedatafolder, targetdatafile, documentationfile, __doc__,
tail, __file__)
def main(sourcedatafile, targetdatafile, documentationfile, tail):
data = load_data(sourcedatafile)
data = normalize_data(data)
save_data(data, targetdatafile)
docfile.write(sourcedatafile, targetdatafile, documentationfile, docstring,
tail, __file__)
def main(sourcedatafolder, targetdatafile, tail):
allfilenames = []
# allhmed = []
# allhstd = []
allhmax1 = []
allhmax2 = []
allhmax3 = []
allvmed = []
allvstd = []
allvmax1 = []
allvmax2 = []
allvmax3 = []
allsmed = []
allsstd = []
allsmax1 = []
allsmax2 = []
allsmax3 = []
for file in glob.glob(join(sourcedatafolder, "*")):
filename = get_metadata(file)
allfilenames.append(filename)
print("\n====", filename)
image = load_image(file)
#print("histogram for HUE (which color)\n0=red, 60=yellow, 240=blue")
histogram = make_histogram(image, 0, 12, [0, 180])
#plt.plot(histogram)
#plt.show()
#print(histogram)
hue, sat, val = get_channels(image)
hmax1, hmax2, hmax3 = get_hmax(histogram)
# allhmed.append(hmed)
# allhstd.append(hstd)
allhmax1.append(hmax1 * 10)
allhmax2.append(hmax2 * 10)
allhmax3.append(hmax3 * 10)
print("histogram for SATURATION (how colorful)\n0=pale, 100=intense")
histogram = make_histogram(image, 1, 10, [0, 256])
#print(histogram)
#plt.plot(histogram)
#plt.show()
smax1, smax2, smax3 = get_smax(histogram)
print(smax1, smax2, smax3)
smed, sstd = get_channel_data(sat)
allsmax1.append(smax1 * 10)
allsmax2.append(smax2 * 10)
allsmax3.append(smax3 * 10)
allsmed.append(smed)
allsstd.append(sstd)
print("histogram for VALUE (how bright)\n0=dark, 100=bright")
histogram = make_histogram(image, 2, 10, [0, 256])
#plt.plot(histogram)
#plt.show()
vmax1, vmax2, vmax3 = get_vmax(histogram)
vmed, vstd = get_channel_data(val)
print(vmax1, vmax2, vmax3)
allvmax1.append(vmax1 * 10)
allvmax2.append(vmax2 * 10)
allvmax3.append(vmax3 * 10)
allvmed.append(vmed)
allvstd.append(vstd)
save_data(allfilenames, allhmax1, allhmax2, allhmax3, allsmax1, allsmax2,
allsmax3, allsmed, allsstd, allvmax1, allvmax2, allvmax3,
allvmed, allvstd, targetdatafile)
docfile.write(sourcedatafolder, targetdatafile, documentationfile,
docstring, tail, __file__)
