myutils.py

# Google sheet API
import csv
import os
import sys
from datetime import datetime

import cv2
import gspread
import matplotlib.pyplot as plt
import numpy as np
from oauth2client.service_account import ServiceAccountCredentials


def create_log_dir_by_date(parent_dir=".", log_dir="logs"):
    """
    Create a log directory based on date.

    Args:
        parent_dir (str, optional): parent directory. Defaults to '.'.
        log_dir (str, optional): log directory. Defaults to 'logs'.

    Returns:
        logs_date_dir (str): log directory based on date.
    """
    today = datetime.today().strftime("%Y-%m-%d")
    logs_date_dir = os.path.join(parent_dir, log_dir, today)
    if os.path.isdir(logs_date_dir) == False:
        os.makedirs(logs_date_dir, exist_ok=True)
    return logs_date_dir


def upload_df_to_GoogleSheet(
    df,
    csv_path,
    sheetName=None,
    json_keyfile_name="skit_GoogleCloud.json",
    spreadsheetName="CSV-to-Google-Sheet",
    verbose=False,
):
    """
    Upload the dataframe to Google sheet online .

    Args:
        df (pd.DataFrame): dataframe of the metrics of each model. If sheetName is not None, also upload the dataframe to Google sheet as csv file. (probably redundant since we read the csv file in the next step)
        csv_path (str): path to the input csv file.
        sheetName (str, optional): sheet name for Google sheet. Defaults to None.
        json_keyfile_name (str, optional): json key file name. Defaults to 'skit_GoogleCloud.json'. Replace it by your credential file name here.
        spreadsheetName (str, optional): name for the Goole SpreadSheet. Defaults to 'CSV-to-Google-Sheet'.
        verbose (bool, optional): option to pring logs. Defaults to False.
    """

    # Upload the dataframe to Google sheet
    scope = [
        "https://spreadsheets.google.com/feeds",
        "https://www.googleapis.com/auth/spreadsheets",
        "https://www.googleapis.com/auth/drive.file",
        "https://www.googleapis.com/auth/drive",
    ]

    # Authorize the client
    credentials = ServiceAccountCredentials.from_json_keyfile_name(json_keyfile_name, scope)
    client = gspread.authorize(credentials)

    # Open a spreadsheet
    spreadsheet = client.open(spreadsheetName)

    # List all worksheets
    worksheet_list = spreadsheet.worksheets()
    if verbose:
        print("all worksheets in the spreadsheet: ", worksheet_list)

    # Create a sheet if it doesn't exist
    if sheetName not in [worksheet.title for worksheet in worksheet_list]:
        print(f"Create a new worksheet {sheetName}")
        worksheet = spreadsheet.add_worksheet(title=sheetName, rows="100", cols="20")

    # Update the value
    spreadsheet.values_update(
        sheetName, params={"valueInputOption": "USER_ENTERED"}, body={"values": list(csv.reader(open(csv_path)))}
    )

    print(f"Successfully uploaded the dataframe to Google sheet {spreadsheetName} -> {sheetName}")


def print_dict(d):
    for k, v in d.items():
        print(k, v)


def print_dict_l2(d):
    for subdic_key in d:
        print("sud_dict key", subdic_key)
        for k, v in d[subdic_key].items():
            print(k, v)


def normalize_im(im):
    arr = np.asarray(im)
    arr = (arr - np.min(arr)) / (np.max(arr) - np.min(arr))
    return arr


def equalize_this(image_src, with_plot=False, gray_scale=False, convert2gray=True, clipLimit=2.0, tileGridLength=8):
    if len(image_src.shape) == 3:
        gray_scale = False

    if not gray_scale:
        if convert2gray:
            if np.max(image_src) <= 1:
                image_src = image_src * 255
            image_src = cv2.cvtColor(image_src.astype(np.uint8), cv2.COLOR_RGB2GRAY)
            # image_eq = cv2.equalizeHist(image_src)
            clahe = cv2.createCLAHE(clipLimit=clipLimit, tileGridSize=(tileGridLength, tileGridLength))
            image_eq = clahe.apply(image_src)
            cmap_val = "gray"
        else:
            r_image, g_image, b_image = cv2.split(image_src)
            print("r_image", type(r_image))
            print(r_image.shape, r_image.dtype)

            r_image_eq = cv2.equalizeHist(r_image.astype(np.uint8))
            g_image_eq = cv2.equalizeHist(g_image.astype(np.uint8))
            b_image_eq = cv2.equalizeHist(b_image.astype(np.uint8))

            image_eq = cv2.merge((r_image_eq, g_image_eq, b_image_eq))
            cmap_val = None
    else:
        image_eq = cv2.equalizeHist(image_src)
        cmap_val = "gray"

    if with_plot:
        fig = plt.figure(figsize=(10, 20))

        ax1 = fig.add_subplot(2, 2, 1)
        ax1.axis("off")
        ax1.title.set_text("Original")
        ax2 = fig.add_subplot(2, 2, 2)
        ax2.axis("off")
        ax2.title.set_text("Equalized")

        ax1.imshow(image_src, cmap=cmap_val)
        ax2.imshow(image_eq, cmap=cmap_val)
        return image_eq
    return image_eq