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__)