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Copy pathlambda_function.py
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350 lines (320 loc) · 12.8 KB
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import os
import json
import tempfile
import shutil
import logging
import time
import subprocess
import cv2
import numpy as np
import random
import boto3
import botocore
from glob import glob
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import List, Tuple, Optional, Dict, Any
from supabase import create_client
from botocore.exceptions import ClientError
from logging import LoggerAdapter, Filter, StreamHandler, Formatter
from dotenv import load_dotenv
# ─── Load & Validate Environment ─────────────────────────────────────────────
load_dotenv()
AWS_REGION = os.getenv('AWS_REGION', 'us-east-1')
S3_BUCKET_VIDEOS = os.getenv('S3_BUCKET_VIDEOS')
S3_BUCKET_FRAMES = os.getenv('S3_BUCKET_FRAMES')
SUPABASE_URL = os.getenv('SUPABASE_URL')
SUPABASE_KEY = os.getenv('SUPABASE_KEY')
if not all([S3_BUCKET_VIDEOS, S3_BUCKET_FRAMES, SUPABASE_URL, SUPABASE_KEY]):
raise RuntimeError("Missing one of required env vars")
# Optional tuning
S3_RETRIES = int(os.getenv('S3_RETRIES', '3'))
S3_RETRY_DELAY_MS = int(os.getenv('S3_RETRY_DELAY', '1000'))
S3_CONNECT_TIMEOUT = int(os.getenv('S3_CONNECT_TIMEOUT', '10'))
S3_READ_TIMEOUT = int(os.getenv('S3_READ_TIMEOUT', '60'))
S3_UPLOAD_THREADS = int(os.getenv('S3_UPLOAD_THREADS', '4'))
MIN_FRAMES = int(os.getenv('MIN_FRAMES', '4'))
SCENE_THRESHOLD = float(os.getenv('SCENE_THRESHOLD', '0.12'))
IMAGE_CROP_TOL = int(os.getenv('IMAGE_CROP_TOL', '10'))
FFMPEG_PATH = os.getenv('FFMPEG_PATH', 'ffmpeg')
CONCURRENCY_LIMIT = int(os.getenv('CONCURRENCY_LIMIT', '2'))
MIN_REMAINING_MS = int(os.getenv('MIN_REMAINING_MS', '60000'))
DEBUG = os.getenv('DEBUG', 'true').lower() == 'true'
# ─── Clients & Logging ────────────────────────────────────────────────────────
botocore_cfg = botocore.config.Config(
retries={'max_attempts': S3_RETRIES, 'mode': 'standard'},
connect_timeout=S3_CONNECT_TIMEOUT,
read_timeout=S3_READ_TIMEOUT,
)
s3 = boto3.client('s3', region_name=AWS_REGION, config=botocore_cfg)
supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
root = logging.getLogger()
root.setLevel(logging.DEBUG if DEBUG else logging.INFO)
handler = StreamHandler()
handler.setFormatter(Formatter("%(asctime)s %(levelname)s [%(shortcode)s] %(message)s"))
root.handlers = [handler]
class ShortcodeFilter(Filter):
def filter(self, record):
record.shortcode = getattr(record, 'shortcode', '-')
return True
handler.addFilter(ShortcodeFilter())
def get_logger(code: Optional[str]=None) -> LoggerAdapter:
return LoggerAdapter(root, {'shortcode': code or '-'})
# ─── S3 + DB Helpers ──────────────────────────────────────────────────────────
def s3_key_exists(bucket: str, key: str) -> bool:
try:
s3.head_object(Bucket=bucket, Key=key)
return True
except ClientError as e:
if e.response['Error']['Code'] in ('404', 'NoSuchKey'):
return False
raise
def download_video(platform: str, code: str) -> Optional[str]:
log = get_logger(code)
key = f"{platform}/{code}/video.mp4"
for attempt in range(1, S3_RETRIES+1):
fd, tmp_path = tempfile.mkstemp(suffix='.mp4')
os.close(fd)
try:
s3.download_file(S3_BUCKET_VIDEOS, key, tmp_path)
log.info(f"Downloaded {key}")
return tmp_path
except Exception as e:
log.error(f"Download attempt {attempt} failed: {e}")
if os.path.exists(tmp_path):
os.remove(tmp_path)
if attempt < S3_RETRIES:
time.sleep(S3_RETRY_DELAY_MS/1000.0)
log.error(f"All downloads failed for {key}")
return None
def upload_frame(path: str, platform: str, code: str, errors: List[str]) -> bool:
key = f"{platform}/{code}/frames/{os.path.basename(path)}"
for attempt in range(1, S3_RETRIES+1):
try:
s3.upload_file(path, S3_BUCKET_FRAMES, key)
os.remove(path)
return True
except Exception as e:
msg = f"Upload error attempt {attempt} for {key}: {e}"
errors.append(msg)
if attempt < S3_RETRIES:
time.sleep((2 ** attempt) * (1 + random.random()*0.1))
errors.append(f"Failed to upload {key}")
return False
def update_db_extracted(code: str, count: int):
supabase.table('insta_content') \
.update({'is_extracted': True, 'frames': count}) \
.eq('code', code) \
.execute()
def record_errors(code: str, errors: List[str]):
if not errors:
return
rows = [{'code': code, 'error': e} for e in errors]
supabase.table('extraction_errors').insert(rows).execute()
# ─── Frame-Extraction Helpers ─────────────────────────────────────────────────
def detect_image_crop(img: np.ndarray, tol: int = IMAGE_CROP_TOL):
h, w = img.shape[:2]
def is_blank_line(line: np.ndarray) -> bool:
"""All pixels within tol of this line's median color?"""
median_col = np.median(line, axis=0)
diffs = np.abs(line.astype(int) - median_col.astype(int)).sum(axis=1)
return np.all(diffs <= tol)
# left
x0 = 0
for x in range(w):
if is_blank_line(img[:, x, :]):
x0 += 1
else:
break
# right
x1 = w
for x in range(w - 1, -1, -1):
if is_blank_line(img[:, x, :]):
x1 -= 1
else:
break
# top
y0 = 0
for y in range(h):
if is_blank_line(img[y, :, :]):
y0 += 1
else:
break
# bottom
y1 = h
for y in range(h - 1, -1, -1):
if is_blank_line(img[y, :, :]):
y1 -= 1
else:
break
# if nothing left, no crop
if x0 >= x1 or y0 >= y1:
return None
return (x0, y0, x1 - x0, y1 - y0)
def ffmpeg_extract_with_pts(video_path: str, out_dir: str, threshold: float):
os.makedirs(out_dir, exist_ok=True)
pattern = os.path.join(out_dir, '%d.jpg')
cmd = [
FFMPEG_PATH, '-hide_banner', '-loglevel', 'error',
'-i', video_path,
'-vf', f"select='gt(scene,{threshold})'",
'-vsync', 'vfr', '-frame_pts', '1', '-q:v', '2',
pattern
]
subprocess.run(cmd, check=True)
imgs = []
for file in glob(os.path.join(out_dir, '*.jpg')):
frame_no = int(os.path.splitext(os.path.basename(file))[0])
imgs.append((file, frame_no))
imgs.sort(key=lambda x: x[1])
return imgs
def extract_key_frames(video_path: str, out_dir: str, code: str, errors: List[str]):
log = get_logger(code)
# FFmpeg pass
raw = ffmpeg_extract_with_pts(video_path, out_dir, SCENE_THRESHOLD)
log.info(f"FFmpeg detected {len(raw)} scenes")
# Probe FPS
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
cap.release()
# Crop, drop fully-blank, rename
saved = 0
for old_path, frame_no in raw:
img = cv2.imread(old_path)
if img is None:
os.remove(old_path)
continue
rect = detect_image_crop(img)
if rect:
x,y,w,h = rect
img = img[y:y+h, x:x+w]
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if np.all(gray == gray[0,0]):
os.remove(old_path)
continue
seconds = frame_no/fps
new = os.path.join(out_dir, f"{saved+1}_{seconds:.2f}.jpg")
cv2.imwrite(new, img)
os.remove(old_path)
saved += 1
# Fallback sampling if too few frames
if saved < MIN_FRAMES:
log.info("Fallback to OpenCV sampling")
cap = cv2.VideoCapture(video_path)
total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
step = max(1, total//(MIN_FRAMES+1))
for i in range(1, MIN_FRAMES+1):
if saved >= MIN_FRAMES:
break
fn = i*step
cap.set(cv2.CAP_PROP_POS_FRAMES, fn)
ret, frame = cap.read()
if not ret:
continue
# optional crop
rect = detect_image_crop(frame)
if rect:
x,y,w,h = rect
frame = frame[y:y+h, x:x+w]
# drop fully-blank
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if np.all(gray == gray[0,0]):
continue
# save
sec = fn/fps
out = os.path.join(out_dir, f"{saved+1}_{sec:.2f}.jpg")
cv2.imwrite(out, frame)
saved += 1
cap.release()
log.info(f"Total after fallback: {saved}")
else:
log.info(f"Kept {saved} frames (>= {MIN_FRAMES})")
# if we STILL got nothing, error out
if saved == 0:
raise RuntimeError("No frames extracted (FFmpeg & fallback both failed)")
return [os.path.join(out_dir, f) for f in os.listdir(out_dir) if f.endswith('.jpg')]
# ─── Core Processing ──────────────────────────────────────────────────────────
def process_single(item: Dict[str,str], context) -> Dict[str,Any]:
platform, code = item['platform'], item['code']
log = get_logger(code)
errors: List[str] = []
status = {}
# Timeout guard
if context and context.get_remaining_time_in_millis() < MIN_REMAINING_MS:
errors.append("Timeout: low remaining time")
status = {'shortcode': code, 'status': 'skipped'}
else:
# Check S3 source
if not s3_key_exists(S3_BUCKET_VIDEOS, f"{platform}/{code}/video.mp4"):
errors.append("Source video missing")
status = {'shortcode': code, 'status': 'skipped'}
else:
vid = download_video(platform, code)
if not vid:
errors.append("Download failed")
status = {'shortcode': code, 'status': 'skipped'}
else:
tmpdir = tempfile.mkdtemp()
try:
frames = extract_key_frames(vid, tmpdir, code, errors)
ok = True
# upload with ThreadPool
with ThreadPoolExecutor(max_workers=S3_UPLOAD_THREADS) as pool:
futures = [ pool.submit(upload_frame, p, platform, code, errors) for p in frames ]
for f in as_completed(futures):
if not f.result():
ok = False
if not ok:
raise RuntimeError("One or more uploads failed")
update_db_extracted(code, len(frames))
status = {'shortcode': code, 'status': 'extracted', 'frames': len(frames)}
except Exception as e:
log.error(f"Processing error: {e}")
errors.append(str(e))
status = {'shortcode': code, 'status': 'error', 'message': str(e)}
finally:
shutil.rmtree(tmpdir, ignore_errors=True)
os.remove(vid)
# persist errors
record_errors(code, errors)
return status
def lambda_handler(event, context):
# Support two shapes:
# 1) API-style: { "items": [ { platform, code }, … ] }
# 2) SQS-style: { "Records": [ { body: '{"platform":"…","code":"…"}' }, … ] }
if "items" in event:
items = event["items"]
elif "Records" in event:
items = []
for rec in event["Records"]:
try:
body = rec.get("body", "")
data = json.loads(body)
# if body itself has an "items" array, unpack it; else treat as single item
if isinstance(data, dict) and "items" in data:
items.extend(data["items"])
else:
items.append(data)
except Exception:
# ignore bad record
continue
else:
items = []
results = []
# Sequential processing (no threads)
for item in items:
try:
res = process_single(item, context)
except Exception as e:
# Catch any unexpected error so one bad video
# doesn't abort the whole batch
get_logger(item.get('code')).error(f"Unhandled error: {e}")
res = {
'shortcode': item.get('code'),
'status': 'error',
'message': str(e)
}
results.append(res)
return {
'status': 'completed',
'results': results
}
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