#
from urllib.request import urlopen as uRequest # collect.py
from bs4 import BeautifulSoup as bSoup # collect.py
import pymysql # database.py
from modules.collect import grab100 as grabTopPages
from modules.database import Database
# Grab data
books = grabTopPages()
# Connect to database
db = Database('localhost', 'root', 'pyDB')
# Send data to database
try:
for book in books:
# db.query("ALTER TABLE `top100LC` CHANGE addDate addDate CHAR(34);")
db.query(
"""INSERT INTO `top100lc` (Title, Author, Rank, Readers, Opinions, Rate, addDate)
VALUES ('{0}', '{1}', '{2}', '{3}', '{4}', '{5}', NOW())""".format(
book[0], book[1], book[2], book[3], book[4], book[5]))
print("\nSuccessful send data to database")
except:
print("\nCan't send data to database")
# Close database connect
db.close()
def process():
output_folder = os.getcwd() + "/../video/output/"
# Take the new video and move it to the output folder
for video in get_list_of_files(config("General.SourcePath")):
# Get the camera number from the video path
camera_number = video.split('/')[-3]
# Check if the camera is enabled, if not delete the video and return
if not camera_config(camera_number, "Enabled", "bool"):
log("Camera is not enabled", "info")
os.unlink(video)
return False, None
# Create video Model
database = Database()
event_id = database.createEvent({
'filename': path.basename(video),
'timestamp': datetime.now()
})
# Prepare the frames for further analysis
video_folder, total_frames, frames = prepare_video(
video, output_folder, event_id)
frames_interval = camera_config(camera_number, "FramesInterval")
attributes = {
'video_folder': path.basename(video_folder),
'total_frames': total_frames,
'skipped_frames': frames_interval,
'camera': camera_number
}
# If we want to notify in any case, let's skip the object detection
if camera_config(camera_number, "NotifyAlways", "bool"):
database.updateEvent(event_id, attributes)
return frames[1], camera_number
# Now that we have extracted the frames from the video, let's start the analysis
outcome = False
frame_counter = 0
for frame in frames:
frame_counter = frame_counter + 1
log("analysing: " + frame, "debug")
outcome, attributes = analyse_image(
camera_number, frame, video_folder, attributes)
if outcome:
log("object found", "info")
attributes['frame'] = frame_counter * int(frames_interval)
break
# If the outcome is not True, delete the folder with the video and frames
if not outcome:
log("No object found", "info")
if camera_config(camera_number, "DeleteVideosIfNothingFound", "bool"):
log("Deleting {}".format(video_folder), "info")
shutil.rmtree(video_folder, ignore_errors=True)
# Serialize the labels
try:
attributes['labels_found'] = json.dumps(
attributes['labels_found'], cls=JsonHelper)
except:
log('could not dump the attributes to array. Probably the attributes obj did not have the labels_found key', 'error')
database.updateEvent(event_id, attributes)
database.close()
return outcome, camera_number
return None, None
