|
|
|
@ -10,6 +10,7 @@ import os
|
|
|
|
|
import pathlib
|
|
|
|
|
import shutil
|
|
|
|
|
import socket
|
|
|
|
|
import structlog
|
|
|
|
|
import tarfile
|
|
|
|
|
import tempfile
|
|
|
|
|
import urllib
|
|
|
|
@ -19,8 +20,24 @@ from matplotlib import pyplot as plt
|
|
|
|
|
from scipy.stats import spearmanr
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def open_data(file):
|
|
|
|
|
return cv2.VideoCapture(str(file))
|
|
|
|
|
log = None
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# We add the `and` conditions to it later
|
|
|
|
|
base_ad_query = {
|
|
|
|
|
"from": "task",
|
|
|
|
|
"limit": 1000,
|
|
|
|
|
"where": {
|
|
|
|
|
"and": []
|
|
|
|
|
},
|
|
|
|
|
"select": [
|
|
|
|
|
"action.start_time",
|
|
|
|
|
"run.name",
|
|
|
|
|
"task.artifacts",
|
|
|
|
|
"task.group.id",
|
|
|
|
|
"task.id"
|
|
|
|
|
],
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def socket_timeout(value=120):
|
|
|
|
@ -38,8 +55,12 @@ def socket_timeout(value=120):
|
|
|
|
|
return _socket_timeout
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def _open_data(file):
|
|
|
|
|
return cv2.VideoCapture(str(file))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@socket_timeout(120)
|
|
|
|
|
def query_activedata(query_json, log):
|
|
|
|
|
def _query_activedata(query_json):
|
|
|
|
|
"""Used to run queries on active data."""
|
|
|
|
|
active_data_url = "http://activedata.allizom.org/query"
|
|
|
|
|
|
|
|
|
@ -59,7 +80,7 @@ def query_activedata(query_json, log):
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@socket_timeout(120)
|
|
|
|
|
def download(url, loc, log):
|
|
|
|
|
def _download(url, loc):
|
|
|
|
|
"""Downloads from a url (with a timeout)."""
|
|
|
|
|
log.info("Downloading %s" % url)
|
|
|
|
|
try:
|
|
|
|
@ -70,7 +91,7 @@ def download(url, loc, log):
|
|
|
|
|
return True
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def get_frames(video):
|
|
|
|
|
def _get_frames(video):
|
|
|
|
|
"""Gets all frames from a video into a list."""
|
|
|
|
|
allframes = []
|
|
|
|
|
while video.isOpened():
|
|
|
|
@ -84,77 +105,11 @@ def get_frames(video):
|
|
|
|
|
return allframes
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def calculate_similarity(jobs_json, fetch_dir, output, log):
|
|
|
|
|
"""Calculates the similarity score against the last live site test.
|
|
|
|
|
|
|
|
|
|
The technique works as follows:
|
|
|
|
|
1. Get the last live site test.
|
|
|
|
|
2. For each 15x15 video pairings, build a cross-correlation matrix:
|
|
|
|
|
1. Get each of the videos and calculate their histograms
|
|
|
|
|
across the full videos.
|
|
|
|
|
2. Calculate the correlation coefficient between these two.
|
|
|
|
|
3. Average the cross-correlation matrix to obtain the score.
|
|
|
|
|
|
|
|
|
|
The 2D similarity score is the same, except that it builds a histogram
|
|
|
|
|
from the final frame instead of the full video.
|
|
|
|
|
|
|
|
|
|
For finding the last live site, we use active-data. We search for
|
|
|
|
|
PGO android builds since this metric is only available for live sites that
|
|
|
|
|
run on android in mozilla-cental. Given that live sites currently
|
|
|
|
|
run on cron 3 days a week, then it's also reasonable to look for tasks
|
|
|
|
|
which have occurred before today and within the last two weeks at most.
|
|
|
|
|
But this is a TODO for future work, since we need to determine a better
|
|
|
|
|
way of selecting the last task (HG push logs?) - there's a lot that factors
|
|
|
|
|
into these choices, so it might require a multi-faceted approach.
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
jobs_json: The jobs JSON that holds extra information.
|
|
|
|
|
fetch_dir: The fetch directory that holds the new videos.
|
|
|
|
|
log: The logger.
|
|
|
|
|
Returns:
|
|
|
|
|
Two similarity scores (3D, 2D) as a float, or None if there was an issue.
|
|
|
|
|
"""
|
|
|
|
|
app = jobs_json["application"]["name"]
|
|
|
|
|
test = jobs_json["jobs"][0]["test_name"]
|
|
|
|
|
splittest = test.split("-cold")
|
|
|
|
|
|
|
|
|
|
cold = ""
|
|
|
|
|
if len(splittest) > 0:
|
|
|
|
|
cold = ".*cold"
|
|
|
|
|
test = splittest[0]
|
|
|
|
|
|
|
|
|
|
# PGO vs. OPT shouldn't matter much, but we restrict it to PGO builds here
|
|
|
|
|
# for android, and desktop tests have the opt/pgo restriction removed
|
|
|
|
|
plat = os.getenv("TC_PLATFORM", "")
|
|
|
|
|
if "android" in plat:
|
|
|
|
|
plat = plat.replace("/opt", "/pgo")
|
|
|
|
|
else:
|
|
|
|
|
plat = plat.replace("/opt", "").replace("/pgo", "")
|
|
|
|
|
ad_query = {
|
|
|
|
|
"from": "task",
|
|
|
|
|
"limit": 1000,
|
|
|
|
|
"where": {
|
|
|
|
|
"and": [
|
|
|
|
|
{
|
|
|
|
|
"regexp": {
|
|
|
|
|
"run.name": ".*%s.*browsertime.*-live.*%s%s.*%s.*"
|
|
|
|
|
% (plat, app, cold, test)
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
{"not": {"prefix": {"run.name": "test-vismet"}}},
|
|
|
|
|
{"in": {"repo.branch.name": ["mozilla-central"]}},
|
|
|
|
|
{"gte": {"action.start_time": {"date": "today-week-week"}}},
|
|
|
|
|
{"lt": {"action.start_time": {"date": "today-1day"}}},
|
|
|
|
|
{"in": {"task.run.state": ["completed"]}},
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"select": ["action.start_time", "run.name", "task.artifacts"],
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
# Run the AD query and find the browsertime videos to download
|
|
|
|
|
def _get_browsertime_results(query):
|
|
|
|
|
"""Used to run an AD query and extract the browsertime results if they exist."""
|
|
|
|
|
failed = False
|
|
|
|
|
try:
|
|
|
|
|
data = query_activedata(ad_query, log)
|
|
|
|
|
data = _query_activedata(query)
|
|
|
|
|
except Exception as e:
|
|
|
|
|
log.info(str(e))
|
|
|
|
|
failed = True
|
|
|
|
@ -162,6 +117,7 @@ def calculate_similarity(jobs_json, fetch_dir, output, log):
|
|
|
|
|
log.info("Couldn't get activedata data")
|
|
|
|
|
return None
|
|
|
|
|
|
|
|
|
|
# Find the newest browsertime task
|
|
|
|
|
log.info("Found %s datums" % str(len(data["action.start_time"])))
|
|
|
|
|
maxind = np.argmax([float(t) for t in data["action.start_time"]])
|
|
|
|
|
artifacts = data["task.artifacts"][maxind]
|
|
|
|
@ -171,13 +127,20 @@ def calculate_similarity(jobs_json, fetch_dir, output, log):
|
|
|
|
|
btime_artifact = art["url"]
|
|
|
|
|
break
|
|
|
|
|
if not btime_artifact:
|
|
|
|
|
log.info("Can't find an older live site")
|
|
|
|
|
log.info("Can't find an older site test")
|
|
|
|
|
return None
|
|
|
|
|
|
|
|
|
|
log.info("Comparing videos to TASK_GROUP=%s, TASK_ID=%s" % (
|
|
|
|
|
data["task.group.id"][maxind], data["task.id"][maxind]
|
|
|
|
|
))
|
|
|
|
|
|
|
|
|
|
# Download the browsertime videos and untar them
|
|
|
|
|
tmpdir = tempfile.mkdtemp()
|
|
|
|
|
loc = os.path.join(tmpdir, "tmpfile.tgz")
|
|
|
|
|
if not download(btime_artifact, loc, log):
|
|
|
|
|
if not _download(btime_artifact, loc):
|
|
|
|
|
log.info(
|
|
|
|
|
"Failed to download browsertime-results artifact from %s" % btime_artifact
|
|
|
|
|
)
|
|
|
|
|
return None
|
|
|
|
|
tmploc = tempfile.mkdtemp()
|
|
|
|
|
try:
|
|
|
|
@ -191,22 +154,90 @@ def calculate_similarity(jobs_json, fetch_dir, output, log):
|
|
|
|
|
)
|
|
|
|
|
return None
|
|
|
|
|
|
|
|
|
|
# Find all the videos
|
|
|
|
|
oldmp4s = [str(f) for f in pathlib.Path(tmploc).rglob("*.mp4")]
|
|
|
|
|
log.info("Found %s old videos" % str(len(oldmp4s)))
|
|
|
|
|
newmp4s = [str(f) for f in pathlib.Path(fetch_dir).rglob("*.mp4")]
|
|
|
|
|
log.info("Found %s new videos" % str(len(newmp4s)))
|
|
|
|
|
return tmploc
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def _data_from_last_task(label):
|
|
|
|
|
"""Gets the data from the last PGO/OPT task with the same label.
|
|
|
|
|
|
|
|
|
|
We look for both OPT and PGO tasks. The difference
|
|
|
|
|
between them should be minimal. This method also provides
|
|
|
|
|
a way to compare recordings from this task to another
|
|
|
|
|
known task based on the TC_GROUP_ID environment varible.
|
|
|
|
|
"""
|
|
|
|
|
label_opt = label.replace("/pgo", "/opt")
|
|
|
|
|
label_pgo = label.replace("/opt", "/pgo")
|
|
|
|
|
|
|
|
|
|
base_ad_query["where"]["and"] = [
|
|
|
|
|
{"in": {"task.run.state": ["completed"]}},
|
|
|
|
|
{"or": [
|
|
|
|
|
{"eq": {"run.name": label_pgo}},
|
|
|
|
|
{"eq": {"run.name": label_opt}}
|
|
|
|
|
]}
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
task_group_id = os.getenv("TC_GROUP_ID", "")
|
|
|
|
|
if task_group_id:
|
|
|
|
|
base_ad_query["where"]["and"].append(
|
|
|
|
|
{"eq": {"task.group.id": task_group_id}}
|
|
|
|
|
)
|
|
|
|
|
else:
|
|
|
|
|
base_ad_query["where"]["and"].extend([
|
|
|
|
|
{"in": {"repo.branch.name": ["mozilla-central"]}},
|
|
|
|
|
{"gte": {"action.start_time": {"date": "today-week-week"}}},
|
|
|
|
|
])
|
|
|
|
|
|
|
|
|
|
return _get_browsertime_results(base_ad_query)
|
|
|
|
|
|
|
|
|
|
# Finally, calculate the 2D/3D score
|
|
|
|
|
|
|
|
|
|
def _data_from_last_live_task(label):
|
|
|
|
|
"""Gets the data from the last live site PGO task."""
|
|
|
|
|
label_live = label.replace("/opt", "/pgo").replace("tp6m", "tp6m-live")
|
|
|
|
|
|
|
|
|
|
base_ad_query["where"]["and"] = [
|
|
|
|
|
{"in": {"repo.branch.name": ["mozilla-central"]}},
|
|
|
|
|
{"gte": {"action.start_time": {"date": "today-week-week"}}},
|
|
|
|
|
{"in": {"task.run.state": ["completed"]}},
|
|
|
|
|
{"eq": {"run.name": label_live}},
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
return _get_browsertime_results(base_ad_query)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def _get_similarity(old_videos_info, new_videos_info, output, prefix=""):
|
|
|
|
|
"""Calculates a similarity score for two groupings of videos.
|
|
|
|
|
|
|
|
|
|
The technique works as follows:
|
|
|
|
|
1. Get the last live site test.
|
|
|
|
|
2. For each 15x15 video pairings, build a cross-correlation matrix:
|
|
|
|
|
1. Get each of the videos and calculate their histograms
|
|
|
|
|
across the full videos.
|
|
|
|
|
2. Calculate the correlation coefficient between these two.
|
|
|
|
|
3. Average the cross-correlation matrix to obtain the score.
|
|
|
|
|
|
|
|
|
|
The 2D similarity score is the same, except that it builds a histogram
|
|
|
|
|
from the final frame instead of the full video.
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
old_videos: List of old videos.
|
|
|
|
|
new_videos: List of new videos (from this task).
|
|
|
|
|
output: Location to output videos with low similarity scores.
|
|
|
|
|
prefix: Prefix a string to the output.
|
|
|
|
|
Returns:
|
|
|
|
|
Two similarity scores (3D, 2D) as a float.
|
|
|
|
|
"""
|
|
|
|
|
nhists = []
|
|
|
|
|
nhists2d = []
|
|
|
|
|
|
|
|
|
|
total_vids = min(len(oldmp4s), len(newmp4s))
|
|
|
|
|
old_videos = [entry["data"] for entry in old_videos_info]
|
|
|
|
|
new_videos = [entry["data"] for entry in new_videos_info]
|
|
|
|
|
|
|
|
|
|
total_vids = min(len(old_videos), len(new_videos))
|
|
|
|
|
xcorr = np.zeros((total_vids, total_vids))
|
|
|
|
|
xcorr2d = np.zeros((total_vids, total_vids))
|
|
|
|
|
|
|
|
|
|
for i in range(total_vids):
|
|
|
|
|
datao = np.asarray(get_frames(open_data(oldmp4s[i])))
|
|
|
|
|
datao = np.asarray(_get_frames(old_videos[i]))
|
|
|
|
|
|
|
|
|
|
histo, _, _ = plt.hist(datao.flatten(), bins=255)
|
|
|
|
|
histo2d, _, _ = plt.hist(datao[-1, :, :].flatten(), bins=255)
|
|
|
|
@ -214,7 +245,7 @@ def calculate_similarity(jobs_json, fetch_dir, output, log):
|
|
|
|
|
for j in range(total_vids):
|
|
|
|
|
if i == 0:
|
|
|
|
|
# Only calculate the histograms once; it takes time
|
|
|
|
|
datan = np.asarray(get_frames(open_data(newmp4s[j])))
|
|
|
|
|
datan = np.asarray(_get_frames(new_videos[j]))
|
|
|
|
|
|
|
|
|
|
histn, _, _ = plt.hist(datan.flatten(), bins=255)
|
|
|
|
|
histn2d, _, _ = plt.hist(datan[-1, :, :].flatten(), bins=255)
|
|
|
|
@ -237,15 +268,93 @@ def calculate_similarity(jobs_json, fetch_dir, output, log):
|
|
|
|
|
log.info("Average 3D similarity: %s" % str(np.round(similarity, 5)))
|
|
|
|
|
log.info("Average 2D similarity: %s" % str(np.round(similarity2d, 5)))
|
|
|
|
|
|
|
|
|
|
if similarity < 0.5:
|
|
|
|
|
# For really low correlations, output the worst video pairing
|
|
|
|
|
if np.round(similarity, 1) <= 0.7 or np.round(similarity2d, 1) <= 0.7:
|
|
|
|
|
# For low correlations, output the worst video pairing
|
|
|
|
|
# so that we can visually see what the issue was
|
|
|
|
|
minind = np.unravel_index(np.argmin(xcorr, axis=None), xcorr.shape)
|
|
|
|
|
|
|
|
|
|
oldvid = oldmp4s[minind[0]]
|
|
|
|
|
shutil.copyfile(oldvid, str(pathlib.Path(output, "old_video.mp4")))
|
|
|
|
|
oldvid = old_videos_info[minind[0]]["path"]
|
|
|
|
|
shutil.copyfile(oldvid, str(pathlib.Path(output, "%sold_video.mp4" % prefix)))
|
|
|
|
|
|
|
|
|
|
newvid = newmp4s[minind[1]]
|
|
|
|
|
shutil.copyfile(newvid, str(pathlib.Path(output, "new_video.mp4")))
|
|
|
|
|
newvid = new_videos_info[minind[1]]["path"]
|
|
|
|
|
shutil.copyfile(newvid, str(pathlib.Path(output, "%snew_video.mp4" % prefix)))
|
|
|
|
|
|
|
|
|
|
return np.round(similarity, 5), np.round(similarity2d, 5)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def calculate_similarity(jobs_json, fetch_dir, output):
|
|
|
|
|
"""Calculates the similarity score for this task.
|
|
|
|
|
|
|
|
|
|
Here we use activedata to find the last live site that ran and
|
|
|
|
|
to find the last task (with the same label) that ran. Those two
|
|
|
|
|
tasks are then compared to the current one and 4 metrics are produced.
|
|
|
|
|
|
|
|
|
|
For live sites, we only calculate 2 of these metrics, since the
|
|
|
|
|
playback similarity is not applicable to it.
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
jobs_json: The jobs JSON that holds extra information.
|
|
|
|
|
fetch_dir: The fetch directory that holds the new videos.
|
|
|
|
|
output: The output directory.
|
|
|
|
|
Returns:
|
|
|
|
|
A dictionary containing up to 4 different metrics (their values default
|
|
|
|
|
to None if a metric couldn't be calculated):
|
|
|
|
|
PlaybackSimilarity: Similarity of the full playback to a live site test.
|
|
|
|
|
PlaybackSimilarity2D: - // - (but for the final frame only)
|
|
|
|
|
Similarity: Similarity of the tests video recording to its last run.
|
|
|
|
|
Similarity2D: - // - (but for the final frame only)
|
|
|
|
|
"""
|
|
|
|
|
global log
|
|
|
|
|
log = structlog.get_logger()
|
|
|
|
|
|
|
|
|
|
label = os.getenv("TC_LABEL", "")
|
|
|
|
|
if not label:
|
|
|
|
|
log.info("TC_LABEL is undefined, cannot calculate similarity metrics")
|
|
|
|
|
return {}
|
|
|
|
|
|
|
|
|
|
# Get all the newest videos from this task
|
|
|
|
|
new_btime_videos = [
|
|
|
|
|
{"data": _open_data(str(f)), "path": str(f)}
|
|
|
|
|
for f in pathlib.Path(fetch_dir).rglob("*.mp4")
|
|
|
|
|
]
|
|
|
|
|
log.info("Found %s new videos" % str(len(new_btime_videos)))
|
|
|
|
|
|
|
|
|
|
# Get the similarity against the last task
|
|
|
|
|
old_btime_res = _data_from_last_task(label)
|
|
|
|
|
old_sim = old_sim2d = None
|
|
|
|
|
if old_btime_res:
|
|
|
|
|
old_btime_videos = [
|
|
|
|
|
{"data": _open_data(str(f)), "path": str(f)}
|
|
|
|
|
for f in pathlib.Path(old_btime_res).rglob("*.mp4")
|
|
|
|
|
]
|
|
|
|
|
log.info("Found %s old videos" % str(len(old_btime_videos)))
|
|
|
|
|
|
|
|
|
|
old_sim, old_sim2d = _get_similarity(
|
|
|
|
|
old_btime_videos, new_btime_videos, output
|
|
|
|
|
)
|
|
|
|
|
else:
|
|
|
|
|
log.info("Failed to find an older test task")
|
|
|
|
|
|
|
|
|
|
# Compare recordings to their live site variant if it exists
|
|
|
|
|
live_sim = live_sim2d = None
|
|
|
|
|
if "live" not in jobs_json["extra_options"]:
|
|
|
|
|
live_btime_res = _data_from_last_live_task(label)
|
|
|
|
|
if live_btime_res:
|
|
|
|
|
live_btime_videos = [
|
|
|
|
|
{"data": _open_data(str(f)), "path": str(f)}
|
|
|
|
|
for f in pathlib.Path(live_btime_res).rglob("*.mp4")
|
|
|
|
|
]
|
|
|
|
|
log.info("Found %s live videos" % str(len(live_btime_videos)))
|
|
|
|
|
|
|
|
|
|
live_sim, live_sim2d = _get_similarity(
|
|
|
|
|
live_btime_videos, new_btime_videos, output, prefix="live_"
|
|
|
|
|
)
|
|
|
|
|
else:
|
|
|
|
|
log.info("Failed to find a live site variant")
|
|
|
|
|
|
|
|
|
|
return {
|
|
|
|
|
"PlaybackSimilarity": live_sim,
|
|
|
|
|
"PlaybackSimilarity2D": live_sim2d,
|
|
|
|
|
"Similarity": old_sim,
|
|
|
|
|
"Similarity2D": old_sim2d,
|
|
|
|
|
}
|
|
|
|
|