Example #1
0
for s, sbj_id in enumerate(sbj_id_all):
    consistency_date = []
    dates = dates_all[s]
    video_loc = "D:\\NancyStudyData\\ecog\\raw\\" + sbj_id + "\\"
    label_loc = "C:\\Users\\wangnxr\\Documents\\rao_lab\\video_analysis\\manual_annotations\\tracks\\"
    #Save_locations
    extracted_label_loc = "C:\\Users\\wangnxr\Documents\\rao_lab\\video_analysis\\manual_annotations\\extracted_labels_reduced\\"
    label_accuracy_loc = "C:\\Users\\wangnxr\\Documents\\rao_lab\\video_analysis\\validation\\" + sbj_id + "\\"

    label_consistency_loc = "C:\\Users\\wangnxr\\Documents\\rao_lab\\video_analysis\\validation\\"

    for date in dates:
        automated_label_loc = "E:\\sound\\" + sbj_id + "\\" + str(date) + "\\"
        #automated_label_loc="E:\\mvmt\\" + sbj_id + "\\"
        consistency_file = []
        extract_labeller_reduced_labels(sbj_id, date, label_loc, video_loc,
                                        extracted_label_loc, 'sk')
        for file in glob.glob(extracted_label_loc + "\\" + sbj_id + "_" +
                              str(date) + "_*_" + 'sk' + ".p"):
            filename = file.split("\\")[-1]
            name, ext = file.split(".")
            filenum = file.split("_")[-2]
            if os.path.isfile(automated_label_loc + sbj_id + "_" + str(date) +
                              "_" + filenum + ".p"):
                auto_label_temp = pickle.load(
                    open(
                        automated_label_loc + sbj_id + "_" + str(date) + "_" +
                        filenum + ".p", "rb"))
                #plt.plot(auto_label_temp)
                #plt.show()
                #print filename
                labels1 = pickle.load(open(file, "rb"))
Example #2
0
label_num=0
consistency_all = []

for s, sbj_id in enumerate(sbj_id_all):
    consistency_date=[]
    dates=dates_all[s]
    video_loc="D:\\NancyStudyData\\ecog\\raw\\" + sbj_id + "\\"
    label_loc="C:\\Users\\wangnxr\\Documents\\rao_lab\\video_analysis\\manual_annotations\\tracks\\"
    #Save_locations
    extracted_label_loc="C:\\Users\\wangnxr\Documents\\rao_lab\\video_analysis\\manual_annotations\\extracted_labels_reduced\\"
    label_accuracy_loc="C:\\Users\\wangnxr\\Documents\\rao_lab\\video_analysis\\validation\\" + sbj_id + "\\"
    label_consistency_loc="C:\\Users\\wangnxr\\Documents\\rao_lab\\video_analysis\\validation\\"

    for date in dates:
        consistency_file=[]
        extract_labeller_reduced_labels(sbj_id, date, label_loc, video_loc, extracted_label_loc, labellers[0])
        extract_labeller_reduced_labels(sbj_id, date, label_loc, video_loc, extracted_label_loc, labellers[1])
        for file in glob.glob(extracted_label_loc + "\\" + sbj_id + "_" + str(date) + "_*_" + labellers[0] + ".p"):
            filename = file.split("\\")[-1]
            name, ext = file.split(".")
            filenum = file.split("_")[-2]
            file_alt = (extracted_label_loc + "\\" + sbj_id + "_"
                                      + str(date) + "_" + filenum + "_" + labellers[1] + ".p")
            if os.path.exists(file_alt):
                labels1 = pickle.load(open(file, "rb"))
                labels2 = pickle.load(open(file_alt, "rb"))
                accuracy_mat = np.zeros(len(labels1["labels_array"]))
                for l in xrange(len(labels1["labels_array"])):
                    correct = np.where(labels1["labels_array"][l]==labels2["labels_array"][l])[0].shape[0]
                    label1_yes += np.where(labels1["labels_array"][l]==1)[0].shape[0]
                    label2_yes += np.where(labels2["labels_array"][l]==1)[0].shape[0]
for s, sbj_id in enumerate(sbj_id_all):
    consistency_date=[]
    dates=dates_all[s]
    video_loc="D:\\NancyStudyData\\ecog\\raw\\" + sbj_id + "\\"
    label_loc="C:\\Users\\wangnxr\\Documents\\rao_lab\\video_analysis\\manual_annotations\\tracks\\"
    #Save_locations
    extracted_label_loc="C:\\Users\\wangnxr\Documents\\rao_lab\\video_analysis\\manual_annotations\\extracted_labels_reduced\\"
    label_accuracy_loc="C:\\Users\\wangnxr\\Documents\\rao_lab\\video_analysis\\validation\\" + sbj_id + "\\"

    label_consistency_loc="C:\\Users\\wangnxr\\Documents\\rao_lab\\video_analysis\\validation\\"

    for date in dates:
        automated_label_loc="E:\\sound\\" + sbj_id + "\\" + str(date) + "\\"
        #automated_label_loc="E:\\mvmt\\" + sbj_id + "\\"
        consistency_file=[]
        extract_labeller_reduced_labels(sbj_id, date, label_loc, video_loc, extracted_label_loc, 'sk')
        for file in glob.glob(extracted_label_loc + "\\" + sbj_id + "_" + str(date) + "_*_" + 'sk' + ".p"):
            filename = file.split("\\")[-1]
            name, ext = file.split(".")
            filenum = file.split("_")[-2]
            if os.path.isfile(automated_label_loc + sbj_id + "_" + str(date) + "_" + filenum + ".p"):
                auto_label_temp = pickle.load(open(automated_label_loc + sbj_id + "_" + str(date) + "_" + filenum + ".p", "rb"))
                #plt.plot(auto_label_temp)
                #plt.show()
                #print filename
                labels1 = pickle.load(open(file, "rb"))
                l = labels1["tracks"].index(type)
                o = labels1["tracks"].index("Other")
                auto_label = np.zeros(len(labels1["labels_array"][l]))
                for i in xrange(len(auto_label)):
                    if np.where(auto_label_temp[(i-1)*sr:(i+2)*sr] > threshold)[0].shape[0] > 30: