252 lines
8.3 KiB
Python
252 lines
8.3 KiB
Python
#!/usr/bin/env python3
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#
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# This Source Code Form is subject to the terms of the Mozilla Public
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# License, v. 2.0. If a copy of the MPL was not distributed with this
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# file, You can obtain one at http://mozilla.org/MPL/2.0/.
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import cv2
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import json
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import numpy as np
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import os
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import pathlib
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import shutil
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import socket
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import tarfile
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import tempfile
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import urllib
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from functools import wraps
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from matplotlib import pyplot as plt
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from scipy.stats import spearmanr
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def open_data(file):
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return cv2.VideoCapture(str(file))
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def socket_timeout(value=120):
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"""Decorator for socket timeouts."""
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def _socket_timeout(func):
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@wraps(func)
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def __socket_timeout(*args, **kw):
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old = socket.getdefaulttimeout()
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socket.setdefaulttimeout(value)
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try:
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return func(*args, **kw)
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finally:
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socket.setdefaulttimeout(old)
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return __socket_timeout
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return _socket_timeout
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@socket_timeout(120)
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def query_activedata(query_json, log):
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"""Used to run queries on active data."""
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active_data_url = "http://activedata.allizom.org/query"
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req = urllib.request.Request(active_data_url)
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req.add_header("Content-Type", "application/json")
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jsondata = json.dumps(query_json)
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jsondataasbytes = jsondata.encode("utf-8")
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req.add_header("Content-Length", len(jsondataasbytes))
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log.info("Querying Active-data...")
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response = urllib.request.urlopen(req, jsondataasbytes)
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log.info("Status: %s" % {str(response.getcode())})
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data = json.loads(response.read().decode("utf8").replace("'", '"'))["data"]
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return data
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@socket_timeout(120)
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def download(url, loc, log):
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"""Downloads from a url (with a timeout)."""
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log.info("Downloading %s" % url)
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try:
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urllib.request.urlretrieve(url, loc)
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except Exception as e:
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log.info(str(e))
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return False
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return True
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def get_frames(video):
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"""Gets all frames from a video into a list."""
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allframes = []
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while video.isOpened():
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ret, frame = video.read()
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if ret:
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# Convert to gray to simplify the process
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allframes.append(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY))
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else:
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video.release()
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break
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return allframes
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def calculate_similarity(jobs_json, fetch_dir, output, log):
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"""Calculates the similarity score against the last live site test.
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The technique works as follows:
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1. Get the last live site test.
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2. For each 15x15 video pairings, build a cross-correlation matrix:
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1. Get each of the videos and calculate their histograms
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across the full videos.
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2. Calculate the correlation coefficient between these two.
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3. Average the cross-correlation matrix to obtain the score.
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The 2D similarity score is the same, except that it builds a histogram
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from the final frame instead of the full video.
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For finding the last live site, we use active-data. We search for
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PGO android builds since this metric is only available for live sites that
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run on android in mozilla-cental. Given that live sites currently
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run on cron 3 days a week, then it's also reasonable to look for tasks
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which have occurred before today and within the last two weeks at most.
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But this is a TODO for future work, since we need to determine a better
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way of selecting the last task (HG push logs?) - there's a lot that factors
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into these choices, so it might require a multi-faceted approach.
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Args:
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jobs_json: The jobs JSON that holds extra information.
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fetch_dir: The fetch directory that holds the new videos.
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log: The logger.
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Returns:
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Two similarity scores (3D, 2D) as a float, or None if there was an issue.
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"""
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app = jobs_json["application"]["name"]
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test = jobs_json["jobs"][0]["test_name"]
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splittest = test.split("-cold")
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cold = ""
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if len(splittest) > 0:
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cold = ".*cold"
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test = splittest[0]
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# PGO vs. OPT shouldn't matter much, but we restrict it to PGO builds here
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# for android, and desktop tests have the opt/pgo restriction removed
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plat = os.getenv("TC_PLATFORM", "")
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if "android" in plat:
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plat = plat.replace("/opt", "/pgo")
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else:
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plat = plat.replace("/opt", "").replace("/pgo", "")
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ad_query = {
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"from": "task",
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"limit": 1000,
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"where": {
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"and": [
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{
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"regexp": {
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"run.name": ".*%s.*browsertime.*-live.*%s%s.*%s.*"
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% (plat, app, cold, test)
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}
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},
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{"not": {"prefix": {"run.name": "test-vismet"}}},
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{"in": {"repo.branch.name": ["mozilla-central"]}},
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{"gte": {"action.start_time": {"date": "today-week-week"}}},
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{"lt": {"action.start_time": {"date": "today-1day"}}},
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{"in": {"task.run.state": ["completed"]}},
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]
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},
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"select": ["action.start_time", "run.name", "task.artifacts"],
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}
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# Run the AD query and find the browsertime videos to download
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failed = False
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try:
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data = query_activedata(ad_query, log)
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except Exception as e:
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log.info(str(e))
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failed = True
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if failed or not data:
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log.info("Couldn't get activedata data")
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return None
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log.info("Found %s datums" % str(len(data["action.start_time"])))
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maxind = np.argmax([float(t) for t in data["action.start_time"]])
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artifacts = data["task.artifacts"][maxind]
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btime_artifact = None
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for art in artifacts:
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if "browsertime-results" in art["name"]:
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btime_artifact = art["url"]
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break
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if not btime_artifact:
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log.info("Can't find an older live site")
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return None
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# Download the browsertime videos and untar them
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tmpdir = tempfile.mkdtemp()
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loc = os.path.join(tmpdir, "tmpfile.tgz")
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if not download(btime_artifact, loc, log):
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return None
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tmploc = tempfile.mkdtemp()
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try:
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with tarfile.open(str(loc)) as tar:
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tar.extractall(path=tmploc)
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except Exception:
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log.info(
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"Could not read/extract old browsertime results archive",
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path=loc,
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exc_info=True,
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)
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return None
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# Find all the videos
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oldmp4s = [str(f) for f in pathlib.Path(tmploc).rglob("*.mp4")]
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log.info("Found %s old videos" % str(len(oldmp4s)))
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newmp4s = [str(f) for f in pathlib.Path(fetch_dir).rglob("*.mp4")]
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log.info("Found %s new videos" % str(len(newmp4s)))
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# Finally, calculate the 2D/3D score
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nhists = []
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nhists2d = []
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total_vids = min(len(oldmp4s), len(newmp4s))
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xcorr = np.zeros((total_vids, total_vids))
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xcorr2d = np.zeros((total_vids, total_vids))
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for i in range(total_vids):
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datao = np.asarray(get_frames(open_data(oldmp4s[i])))
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histo, _, _ = plt.hist(datao.flatten(), bins=255)
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histo2d, _, _ = plt.hist(datao[-1, :, :].flatten(), bins=255)
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for j in range(total_vids):
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if i == 0:
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# Only calculate the histograms once; it takes time
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datan = np.asarray(get_frames(open_data(newmp4s[j])))
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histn, _, _ = plt.hist(datan.flatten(), bins=255)
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histn2d, _, _ = plt.hist(datan[-1, :, :].flatten(), bins=255)
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nhists.append(histn)
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nhists2d.append(histn2d)
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else:
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histn = nhists[j]
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histn2d = nhists2d[j]
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rho, _ = spearmanr(histn, histo)
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rho2d, _ = spearmanr(histn2d, histo2d)
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xcorr[i, j] = rho
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xcorr2d[i, j] = rho2d
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similarity = np.mean(xcorr)
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similarity2d = np.mean(xcorr2d)
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log.info("Average 3D similarity: %s" % str(np.round(similarity, 5)))
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log.info("Average 2D similarity: %s" % str(np.round(similarity2d, 5)))
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if similarity < 0.5:
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# For really low correlations, output the worst video pairing
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# so that we can visually see what the issue was
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minind = np.unravel_index(np.argmin(xcorr, axis=None), xcorr.shape)
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oldvid = oldmp4s[minind[0]]
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shutil.copyfile(oldvid, str(pathlib.Path(output, "old_video.mp4")))
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newvid = newmp4s[minind[1]]
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shutil.copyfile(newvid, str(pathlib.Path(output, "new_video.mp4")))
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return np.round(similarity, 5), np.round(similarity2d, 5)
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