async def note(ctx, name):
name = name.lower().replace(" ", "-")
content = Notes(getPath(ctx)).get(name)
if content:
message = content
else:
message = f"Note “{name}” does not exist.\nUse `!notes` to get a list of available notes."
await ctx.send(message)
async def notes(ctx):
# TODO: Make this an embed so commands to read each note can be embedded in
# notes names (not sure that's possible)
notes = Notes(getPath(ctx)).getAll()
if notes:
message = "Here are the notes available to read:\n\n"
for name in notes.keys():
message += f"* {name}\n"
message += "\nUse `!note <name>` to read a note!"
else:
message = "There are no notes! You can add one with `!writenote <name> <content>`."
await ctx.send(message)
async def deletenote(ctx, name):
name = name.lower().replace(" ", "-")
notes = Notes(getPath(ctx))
if not name.replace("-", "").replace("_", "").isalpha():
await ctx.send("Note name can only contain letters, “-” and “_”.")
return
if notes.delete(name):
message = f"Note “{name}” successfully deleted!"
print(
f"Deleted note “{name}” on server “{ctx.guild.name}” ({ctx.guild.id})")
else:
message = f"Note {name} does not exist.\nUse `!notes to get a list of available notes."
await ctx.send(message)
async def writenote(ctx, *, args):
try:
(name, content) = args.split(maxsplit=1)
except ValueError:
await ctx.send("You must provide a content for the note.\nUsage: `!writenote <name> <content>`")
return
if not name.replace("-", "").replace("_", "").isalpha():
await ctx.send("Note name can only contain letters, “-” and “_”.")
return
name = name.lower().replace(" ", "-")
content = content.strip()
if len(name) > 30:
await ctx.send("Note name cannot exceed 30 characters.")
return
# Write notes to file
notes = Notes(getPath(ctx))
notes.write(name, content)
print(
f"Wrote note “{name}” on server “{ctx.guild.name}” ({ctx.guild.id}): {content}")
await ctx.send(f"Successfully wrote note “{name}”, use `!note {name}` to read it!")
from sklearn.model_selection import train_test_split
import helpers
import argparse
from sliding_window import localize_and_classify
from PIL import Image
import numpy as np
import os
import cv2
# Find paths and labels
dir_dataset = helpers.getPath()
filenames = helpers.getFilenames(dir_dataset)
labels = helpers.getLabels(dir_dataset)
# Flatten the data set
print("> ------ SVM classifier ------")
print("> Collecting Image data ...")
X, y = helpers.flattenImages(filenames, labels)
print("> Splitting train and test data ...")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
print("> Preprocess data ...")
X_train_HOG = helpers.SVM_preProcessing(X_train)#[0:5000])
X_test_HOG = helpers.SVM_preProcessing(X_test)#[0:2000])
print("> Creating a model ...")
# hog pca = 70, kernel='rbf', C=5, gamma=0.16 leads to good results.
svc_clf = helpers.SVM_getModel(X_train_HOG, y_train)#[0:6000])
# Train the classifier
import matplotlib.pyplot as plt
import helpers
import os
import numpy as np
# Check to analyze one graph or all graphs in directory
method = input("Do you want to analyze 1 graph or all? (0 for all)\n")
# For one file
if method == '1':
# Get path and check if is file
path = helpers.getPath("What's the .tData file path?\n", True)
title = os.path.basename(path).split('.')
fileData = helpers.grabData(path)
if(len(fileData) == 0):
exit(0)
# Store the data
groups = len(fileData)
tempLabels = []
tempSearches = []
tempTotals = []
for data in fileData:
tempSearches.append(data[1][0])
tempTotals.append(data[1][1])
tempLabels.append(data[0])
# Plot data
fig, ax = plt.subplots()
index = np.arange(groups)