def filename(path):
def create_directory(directory):
def load_json(src):
def delete_files_in_folder(path): def env(name, default=None):
def window(seq, n=2):
def block_shaped(image, n_rows, n_cols):
def flatten(l):
def camel_to_underscore(name):
def underscore_to_camel(name):
def change_dict_naming_convention(d, convert_function):
	def as_dict(self, camel=True):
	def from_dict(cls, data, from_camel=True):
	def from_dicts(cls, data, from_camel=True):
	def to_mongo_dict(self):
def to_mongo_dict(obj):
def to_dict(obj):
def props(obj):
def image_filename(index, file_type="jpg"):
def extract_filename(path):
def extract_name(file):
def basepath(path):
def extract_index(path):
def objects_as_dict(objects):
def slice_paths(paths, limit):
def derivative(distances):
def crop_image(image, cx=20, cy=20):
DEBUG = bool(int(getenv('DEBUG', False)))
	T = type(objects[0])
class Model(object): def plot(title, datum, scatter=None, project=None, store=False, xlim=None, ylim=None):
	plt.figure("Results {}".format(title))
	if xlim:
		plt.xlim(xlim)

	if ylim:
		plt.ylim(ylim)

	for data in datum:
		plt.plot(data)

	if scatter:
		for x, y in scatter:
			plt.scatter(x, y, color="green")

	if not store:
		plt.show()
	else:
		base_path = project.folder_path("plots")
		path = join(base_path, "{}.png".format(title))
		plt.savefig(path)

	plt.clf()
	plt.cla()
	plt.close() class ExtractionType(Enum):
	histogram = "histogram"
	simpleHistogram = "simpleHistogram"
	edge = "edge"
	edgeim = "edgeim" class Project(Model):
	keyframe_montage_size = None
	keyframe_size = None

	def __init__(self, title):
		self.name = title

		identifier = re.sub('[^a-zA-Z0-9-_*.]', ' ', title.lower())
		self.identifier = identifier.replace(" ", "_")

		local_base_path = StoragePath.local.base_path(self.identifier)
		create_directory(local_base_path)

	class Folder(Enum):
		frames = 1
		keyframes = 2
		keyframe_thumbnails = 3
		spatio = 4
		plots = 5

		def __str__(self):
			return {
				Project.Folder.frames: "frames",
				Project.Folder.keyframes: "keyframes",
				Project.Folder.keyframe_thumbnails: "keyframe_thumbnails",
				Project.Folder.spatio: "spatio_temporal_slices",
				Project.Folder.plots: "plots",
			}[self]

	class File(Enum):
		shots = 1
		chapters = 2
		subtitles = 3
		merged_subtitles = 4
		original_subtitles = 5
		script = 6
		keyframe_montage = 7
		shot_change_ratio = 8

		def __str__(self):
			return {
				Project.File.shots: "shots.json",
				Project.File.chapters: "chapters.json",
				Project.File.subtitles: "subtitles.json",
				Project.File.original_subtitles: "subtitles.srt",
				Project.File.merged_subtitles: "merged_subtitles.json",
				Project.File.script: "script.json",
				Project.File.keyframe_montage: "keyframe_montage.jpg",
				Project.File.shot_change_ratio: "shot_change_ratio.json",
			}[self]

	def setup(self):
		for folder_type in Project.Folder:
			self.folder_path(folder_type)

	@staticmethod
	def file_exists(path):
		return os.path.exists(path)

	def folder_path(self, folder_type, destination=None, storage_env=StoragePath.local):
		assert folder_type is not None

		folder = str(folder_type) if type(folder_type) == Project.Folder else folder_type

		if destination:
			return self.__folder_path(destination, storage_env)
		else:
			base_path = storage_env.base_path(self.identifier)
			default_path = os.path.join(base_path, folder)
			return self.__folder_path(default_path, storage_env)

	def file_path(self, file_type, destination=None, storage_env=StoragePath.local):
		return self.__file_path(str(file_type), destination, storage_env)

	def write(self, data, file_type, destination=None):
		destination = self.__file_path(str(file_type), destination)
		write_json(destination, data)

	def read(self, file_type):
		path = self.__file_path(str(file_type))
		with open(path) as data:
			return json.load(data)

	def __folder_path(self, folder, storage_env=StoragePath.local):
		if storage_env == StoragePath.local:
			create_directory(folder)

		return folder

	def __file_path(self, filename, full_path=None, storage_env=StoragePath.local):
		if full_path is None:
			base_path = storage_env.base_path(self.identifier)
			return os.path.join(base_path, filename)

		if os.path.isdir(full_path):
			full_path = os.path.join(full_path, filename)

		dir = os.path.dirname(full_path)
		create_directory(dir)
		return full_path class Model(object):
	def as_dict(self, camel=True):
		dictionary = to_dict(self)
		if camel:
			return change_dict_naming_convention(dictionary, underscore_to_camel)
		else:
			return dictionary

	@classmethod
	def from_dict(cls, data, from_camel=True):
		if from_camel:
			return change_dict_naming_convention(data, camel_to_underscore)
		else:
			return data

	@classmethod
	def from_dicts(cls, data, from_camel=True):
		return [cls.from_dict(d) for d in data]

	def to_mongo_dict(self):
		dictionary = self.as_dict()
		return to_mongo_dict(dictionary) def image_filename(index, file_type="jpg"):
	return '{:010d}.{}'.format(index + 1, file_type) def derivative(distances):
	windows = window(distances)
	derivative_distances = []

	for i, (d1, d2) in enumerate(windows):
		if i == 0:
			derivative_distances.append(0.0)

		if d2 >= d1:
			derivative_distances.append(d2 - d1)
		else:
			derivative_distances.append(0.0)

	distances.insert(0, 0)

	return derivative_distances def slice_paths(paths, limit):
	lower = int(limit[0])
	upper = int(limit[1])

	new_paths = []
	for i, path in enumerate(paths):
		_, file_extension = splitext(path)
		file_index = extract_index(path)

		if lower <= file_index <= upper:
			new_paths.append(path)

	return new_paths def extract_index(path):
	file = extract_filename(path)
	filename = extract_name(file)
	file_index = int(filename)

	return file_index def window(seq, n=2):
	"""Returns a sliding window (of width n) over data from the iterable"""
	"   s -> (s0,s1,...s[n-1]), (s1,s2,...,sn), ...                   "
	it = iter(seq)
	result = tuple(islice(it, n))
	if len(result) == n:
		yield result
	for elem in it:
		result = result[1:] + (elem,)
		yield result
import glob
import math
import os
import shlex
import subprocess
import cv2
import matplotlib.pyplot as plt
import numpy as np
from os.path import join
from pprint import pprint
from shutil import copy2
from statistics import mean
from scipy.spatial import distance as dist
from tqdm import tqdm
from analyzer import path_utils
from analyzer import utils
from analyzer import plot as uplot
from analyzer.constants import ExtractionType
from analyzer.project import Project
from analyzer.utils import Model, image_filename, derivative, slice_paths, extract_index, window
	progress_bar = tqdm(total=len(distances), desc="shots extraction")
	for i, ((t1_d, p1), (t2_d, p2), (t3_d, p3)) in enumerate(windows):
		actual_index = relative + i + 2  # center of the window

		prev_diff = abs(t1_d - t2_d)

		if t1_d < t2_d and prev_diff > threshold:
			shot = Shot(start_index=last_shot_index, end_index=actual_index, id=len(shots), relative_diff=t2_d)
			shots.append(shot)
			last_shot_index = actual_index

		if last_index == i:
			shot = Shot(start_index=last_shot_index, end_index=actual_index + 1, id=len(shots), relative_diff=t2_d)
			shots.append(shot)

		progress_bar.update()

	progress_bar.close()

	return shots


def adaptive_threshold(distances):
	ave = np.array(distances).mean()

	filtered = [val for val in distances if val > 0.0]
	ave = np.array(filtered).mean()

	sliding_window = utils.window(distances, 3)
	local_maxima = [d2 for d1, d2, d3 in sliding_window if d2 > ave and d1 < d2 and d3 < d2]

	t = np.array(local_maxima).mean()

	return t


def log_statistics(shots, fps=25):
	shot_lengths = [shot.length for shot in shots]
	average_shot_length = mean(shot_lengths)

	print("average shot length: {:.1f} frames ~ {:.2f} sec".format(average_shot_length, average_shot_length / fps))


class Keyframe(object):
	def __init__(self, index):
		self.index = index
		self.colors = None
		self.labels = None


class Shot(Model):
	def __init__(self, start_index=None, end_index=None, id=None, relative_diff=None):
		if end_index:
			self.keyframe = Keyframe(start_index)
			self.end_index = end_index
			self.length = end_index - start_index
			self.duration = int(self.length / 25 * 1000)
		else:
			self.keyframe = None
			self.end_index = None
			self.duration = None

		self.start_index = start_index
		self.id = id
		self.start_diff = relative_diff

	def as_dict(self, camel=True):
		d = Model.as_dict(self)
		if self.keyframe.labels:
			d["keyframe"]["labels"] = [label.as_dict() for label in self.keyframe.labels]

		return d

	def to_mongo_dict(self):
		d = self.as_dict()
		return utils.to_mongo_dict(d)

	@classmethod
	def from_dict(cls, data, from_camel=True):
		d = Model.from_dict(data, from_camel)
		shot = Shot(d.get("start_index"), d.get("end_index"), d.get("id"), d.get("relative_diff"))

		if "labels" in d["keyframe"] and d["keyframe"]["labels"] is not None:
			shot.keyframe.labels = [Label.from_dict(l) for l in (d["keyframe"]["labels"])]

		if "colors" in d["keyframe"] and d["keyframe"]["colors"] is not None:
			shot.keyframe.colors = [l for l in (d["keyframe"]["colors"])]

		return shot

	def __str__(self):
		return "id: {}, start_index: {} length: {}, end_index: {}" \
			.format(self.id, self.start_index, self.length, self.end_index)

	def __repr__(self):
		return self.__str__()


class Label(Model):
	def __init__(self, description, score):
		self.description = description
		self.score = score

	@classmethod
	def from_dict(cls, data, from_camel=True):
		d = Model.from_dict(data, from_camel)
		return cls(d.get("description"), d.get("score"))

	def __str__(self):
		return "{:<12} => {}".format(self.description, self.score)

	def __repr__(self):
		return self.__str__()


####################################################################
# keyframes


def copy_keyframes(project, _shots):

	key_frames_file_names = [image_filename(shot.keyframe.index) for shot in _shots]