From 5f174084dd7f1497dba624eaff4d6ffde118d149 Mon Sep 17 00:00:00 2001
From: Niclas Dobbertin <niclas.dobbertin@stud.tu-darmstadt.de>
Date: Mon, 2 Oct 2023 19:13:37 +0200
Subject: add utils script from refactor

---
 bjoern/videoanalyse/utils.py | 125 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 125 insertions(+)
 create mode 100644 bjoern/videoanalyse/utils.py

(limited to 'bjoern/videoanalyse')

diff --git a/bjoern/videoanalyse/utils.py b/bjoern/videoanalyse/utils.py
new file mode 100644
index 0000000..e060a89
--- /dev/null
+++ b/bjoern/videoanalyse/utils.py
@@ -0,0 +1,125 @@
+#!/usr/bin/env python3
+
+import csv
+from datetime import datetime, timedelta
+
+import numpy as np
+import pandas as pd
+from Levenshtein import distance as levendist
+from sklearn.cluster import DBSCAN
+from sklearn.metrics import pairwise_distances
+
+
+def combine_ocr_logs(video_path, ocr_path, log_path):
+    date_format = "%Y-%m-%d %H-%M-%S"
+    video_date = datetime.strptime(video_path.stem, date_format)
+    print(video_date)
+    # video_delta = timedelta(hours=video_date.hour, minutes=video_date.minute, seconds=video_date.second)
+
+    def add_video_time_to_start(x, video_date):
+        start = timedelta(seconds=int(round(x)))
+        return (start + video_date).time().isoformat()
+
+    # analysis = pd.read_csv(vp_path / "analysis_results.csv")
+    analysis = pd.read_csv(ocr_path)
+    analysis["Starttime"] = analysis["start_time"].apply(
+        add_video_time_to_start, args=(video_date,)
+    )
+
+    # logs = pd.read_csv(vp_path / f"{vp_path.name}.csv")
+    logs = pd.read_csv(log_path)
+
+    def get_log_url(start_time):
+        start_time = datetime.strptime(start_time, "%H:%M:%S")
+
+        for _, row in logs.iterrows():
+            log_start = datetime.strptime(row[0], "%H:%M:%S")
+            log_end = datetime.strptime(row[1], "%H:%M:%S")
+            if start_time >= log_start and start_time <= log_end:
+                return row[3]
+        return 0
+
+    analysis["log_url"] = analysis.apply(lambda row: get_log_url(row.Starttime), axis=1)
+
+    # analysis.to_csv(vp_path / "merged.csv", quoting=csv.QUOTE_NONNUMERIC)
+    return analysis
+
+
+def calc_levenshtein_distance(df):
+    df["levenshtein-distance"] = df.apply(
+        lambda row: levendist(str(row.url), str(row.log_url)), axis=1
+    )
+    return df
+
+
+def group_urls(urls):
+    unique_urls = np.unique(urls)
+
+    # TODO: casting deprecation np
+    def levenshtein_from_idx(idx1, idx2):
+        return levendist(unique_urls[int(idx1)], unique_urls[int(idx2)])
+
+    X = np.searchsorted(unique_urls, list([[x] for x in urls]))
+
+    distance_matrix = pairwise_distances(
+        X=X, Y=None, metric=levenshtein_from_idx, n_jobs=-1
+    )
+    # TODO: eps and min_samples parameter
+    db = DBSCAN(eps=10, min_samples=5, metric="precomputed").fit(distance_matrix)
+    labels = db.labels_
+    zipped = zip(urls, labels)
+
+    # grouping solution from: https://www.geeksforgeeks.org/python-group-tuple-into-list-based-on-value/
+    # create an empty dictionary to store the grouped tuples
+    grouped_dict = {}
+
+    # loop through the tuples in the list
+    for tup in zipped:
+        # get the second element of the tuple
+        key = tup[1]
+        # if the key is not already in the dictionary, add it with an empty list as value
+        if key not in grouped_dict:
+            grouped_dict[key] = []
+        # append the current tuple to the list corresponding to the key in the dictionary
+        grouped_dict[key].append(tup[0])
+
+    # convert the dictionary values to lists and store in res
+    url_groups = [v for _, v in grouped_dict.items()]
+
+    return url_groups
+
+
+# TODO: use df instead of csv reader
+# TODO: return df instead of writing to file
+def write_grouped_metrics(df, url_groups, data_path):
+    # # for every row check which group its url belongs to and add a column with group indices
+    # # also add columns with longest/most frequent url in group
+    with open(data_path / "metrics.csv", "r") as input_file, open(
+        data_path / "metrics_grps.csv", "w", newline=""
+    ) as output_file:
+        csv_reader = csv.reader(input_file, quotechar='"')
+        csv_writer = csv.writer(
+            output_file, quotechar='"', quoting=csv.QUOTE_NONNUMERIC
+        )
+        header = next(csv_reader)
+        header.extend(
+            [
+                "group_index",
+                "longest",
+                "longest-distance",
+                "most_frequent",
+                "most_frequent-distance",
+            ]
+        )
+        csv_writer.writerow(header)
+        for row in csv_reader:
+            for idx, grp in enumerate(url_groups):
+                if row[2] in grp:
+                    row.append(str(idx))
+                    longest_in_grp = max(grp, key=len)
+                    row.append(longest_in_grp)
+                    row.append(levendist(row[5], longest_in_grp))
+                    most_frequent_in_grp = max(set(grp), key=grp.count)
+                    row.append(str(most_frequent_in_grp))
+                    row.append(levendist(row[5], most_frequent_in_grp))
+            csv_writer.writerow(row)
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