import cv2
import os
from os.path import abspath
import datetime
import matplotlib.pyplot as plt
from tqdm import tqdm
import numpy as np
import random 
from PIL import Image
import requests
import json

# Export's video frames
def video_to_frames(path):
    print("[INFO] Loading video...")
    basename = os.path.splitext(os.path.split(path)[1])[0]
    output_dir_name = basename + "_frames"
    output_dir = os.path.join(os.path.split(path)[0], output_dir_name)
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
    else:
        print("[INFO] Outut folder \"{}\" already exists".format(output_dir))
        return
    stream = cv2.VideoCapture(path)
    count = 0
    print("[INFO] Reading and saving frames...")
    while True:
        (retrieved, frame) = stream.read()
        if not retrieved:
            break
        count += 1
        filename = "frame-{}.jpg".format(count)
        path = os.path.abspath(os.path.join(output_dir, filename))
        cv2.imwrite(path, frame)
    print("[INFO] Frames saved to \"{}\"".format(output_dir))

# Plot images
def plot_images(paths):
    # plot the images
    number_of_images = len(paths)
    if number_of_images % 5 == 0:
        row = number_of_images // 5
    else:
        row = (number_of_images // 5) + 1
    column = 5
    width = 250
    size = width * row 
    outputDir = os.path.join(os.path.dirname(__name__), "output")
    fileName = datetime.datetime.now().strftime("%G-%m-%dT%H%M%S") + ".png" 
    filePath = os.path.join(outputDir, fileName)

    if not os.path.exists(outputDir):
        os.makedirs(outputDir)

    heightOfFig = 6 * row
    fig = plt.figure(figsize=(15, heightOfFig))

    # Plot the 5 highest on the first row
    plotIndex = 1
    for path in tqdm(paths):
        image = cv2.imread(path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        height = int(image.shape[0] * (width / image.shape[0]))
        image = cv2.resize(image, (width, height))
        fig.add_subplot(row, column, plotIndex)
        plt.imshow(image)
        plt.axis('off')
        plt.title(os.path.split(path)[1])
        plt.tight_layout()
        plotIndex += 1

    fig.tight_layout()
    plt.savefig(filePath) 

def list_dir_abs(directory):
    dirAbsPath = os.path.abspath(directory)
    listOfItems = os.listdir(directory)
    listOfPaths = []
    for item in listOfItems:
        path = os.path.join(dirAbsPath, item)
        listOfPaths.append(path)
    return listOfPaths

def pickRandomNum(paths, num):
    ranPaths = random.choices(paths, k=num)
    return ranPaths

def showImage(path):
    image = cv2.imread(path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    plt.imshow(image)
    plt.show()

def showDiscreteFFT(image):
    fftImage = np.fft.fft2(image)
    fftShift = np.fft.fftshift(fftImage)
    magnitude = 20 * np.log(np.abs(fftShift))
    
    fig = plt.figure(figsize=(15, 6))
    fig.add_subplot(1, 2, 1)
    plt.imshow(image, cmap="gray")
    fig.add_subplot(1, 2, 2)
    plt.imshow(magnitude, cmap="gray")
    fig.tight_layout()
    plt.show()

def loadImageFromURL(url):
    response = requests.get(url)
    image = Image.open(requests.get(url, stream=True).raw)
    return image

def PILToNumpy(image):
    image = np.array(image)
    return image

def URL2FFT(url):
    image = loadImageFromURL(url)
    if image.mode == "RGB":
        image = PILToNumpy(image)
        image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    else:
        image = PILToNumpy(image)
    showDiscreteFFT(image)

def fileToList(path):
    with open(path) as f:
        contents = f.read().rstrip()
        paths = contents.strip('][').replace("'", "").split(', ')
    return paths