import matplotlib.pyplot as plt
from PIL import ImageFont
from PIL import ImageDraw 
import multiprocessing
from PIL import Image
import numpy as np
import itertools
# import logging
import math
import cv2
import os


# logging.basicConfig(filename=f'{os.getcwd()}/frame_processing.log', level=logging.INFO)
# logging.info('Starting frame processing')
fps = 0
def read_file(name):
    global fps
    cap = cv2.VideoCapture(name)
    fps = cap.get(cv2.CAP_PROP_FPS)
    if not cap.isOpened():
        # logging.error("Cannot open Video")
        exit()
    frames = []
    while True:
        ret,frame = cap.read()
        if not ret:
            # logging.info("Can't receive frame (stream end?). Exiting ...")
            break
        frames.append(frame)

    cap.release()
    cv2.destroyAllWindows()
    for i in range(len(frames)):
        # print(frames[i].shape)
        frames[i]=cv2.cvtColor(frames[i], cv2.COLOR_BGR2GRAY)

    frames_with_index = [(frame, i) for i, frame in enumerate(frames)]
    return frames_with_index

st = [0,1,2,3,4]
dt = {}
idx = 0;
l = (tuple(i) for i in itertools.product(st, repeat=4) if tuple(reversed(i)) >= tuple(i))
l=list(l)
cnt = 0
for i in range(0,len(l)):
    lt=l[i]
    mirror = tuple(reversed(lt))
    dt[mirror]=i;
    dt[lt]=i;


def calc_filtered_img(img):
    # residual_img= np.zeros(img.shape)
    # residual_img = np.array(img);
    fil = np.array([[-1,3,-3,1]])
    residual_img = cv2.filter2D(img, -1, fil)
    # for i in range(img.shape[0]):
    #     for j in range(img.shape[1]):
    #         residual_img[i, j] = - 3*img[i, j];
    #         if(j>0):
    #             residual_img[i, j] += img[i, j-1]
    #         if(j+1<img.shape[1]):
    #             residual_img[i, j] += 3*img[i, j+1]
    #         if(j+2<img.shape[1]):
    #             residual_img[i,j]-= img[i, j+2]
    # residual_img = np.convolve(img,[1,-3,3,-1],mode='same')
    return residual_img

def calc_q_t_img(img, q, t):
    # qt_img = np.zeros(img.shape)
    # for i in range(img.shape[0]):
    #     for j in range(img.shape[1]):
            # val = np.minimum(t, np.maximum(-t, np.round(img[i, j]/q)))
    #         qt_img[i, j] = val
    # print(dct)
    qt_img = np.minimum(t, np.maximum(-t, np.round(img/q)))
    return qt_img

def process_frame(frame_and_index):
    frame, index = frame_and_index
    # processing logic for a single frame
    # logging.info(f"Processing frame {index}")
    filtered_image = calc_filtered_img(frame)
    output_image = calc_q_t_img(filtered_image, q, t)
    output_image=output_image+2
    # plt.imshow(output_image)
    return output_image.astype(np.uint8)

# Center the filtered image at zero by adding 128
q = 3
t = 2
def process_video(frames_with_index):
    num_processes = multiprocessing.cpu_count()
    # logging.info(f"Using {num_processes} processes")
    pool = multiprocessing.Pool(num_processes)  
    # process the frames in parallel
    processed_frames = pool.map(process_frame, frames_with_index)
    pool.close() 
    pool.join()
    processed_frame_with_index = [(frame, i) for i, frame in enumerate(processed_frames)]
    return processed_frame_with_index

co_occurrence_matrix_size = 5
co_occurrence_matrix_distance = 4
def each_frame(frame_and_index,processed_frames):
    # go rowise and column wise
    frame,index = frame_and_index
    freq_dict = {}
    for i in range( frame.shape[0]):
        for j in range( frame.shape[1]-co_occurrence_matrix_distance):
            row = frame[i]
            v1 = row[j:j+4]
            k1 = tuple(v1)
            freq_dict[k1]=freq_dict.get(k1,0)+1
    freq_dict2={}
    for i in range( frame.shape[0]-co_occurrence_matrix_distance):
        for j in range( frame.shape[1]):
            column = frame[:, j]
            v2 = column[i:i+4]
            k2 = tuple(v2)
            freq_dict2[k2]=freq_dict2.get(k2,0)+1
    freq_dict3={}
    for i in range( frame.shape[0]):
        for j in range( frame.shape[1]):
            # get next possible 4 frames
            if index < len(processed_frames)-3:
                f1 = processed_frames[index+1][i,j]
                f2 = processed_frames[index+2][i,j]
                f3 = processed_frames[index+3][i,j]
                k = (frame[i,j], f1, f2, f3)
                freq_dict3[k]=freq_dict3.get(k,0)+1
    # logging.info(f"hist made for frame {index}")
    return (freq_dict,freq_dict2,freq_dict3)

def extract_video(processed_frame_with_index):
    processed_frames = [frame for frame, index in processed_frame_with_index]
    num_processes = multiprocessing.cpu_count()
    # logging.info(f"Using2 {num_processes} processes")
    pool = multiprocessing.Pool(num_processes)  
    # process the frames in parallel
    freq_dict_list = pool.starmap(each_frame, zip(processed_frame_with_index,itertools.repeat(processed_frames)))
    pool.close() 
    pool.join()
    return freq_dict_list
def final(freq_dict_list):
    descriptors = []
    for freq_dicts in freq_dict_list:
        di1=[]
        for freq_dict in freq_dicts:
            frame = np.zeros(325);
            for(k,v) in freq_dict.items():
                frame[dt[k]]+=v
            di1.append(frame);
        descriptors.append(di1)
    descriptors=np.array(descriptors);
    desc_1d = descriptors.reshape(descriptors.shape[0],-1)
    mean_1d = np.mean(desc_1d,axis=0)
    co_variance_1d = np.zeros((1,1))
    for frame in desc_1d:
        mean_1d+=frame
    mean_1d=frame/len(desc_1d)
        
    for frame in desc_1d:
        tmp = frame-mean_1d
        co_variance_1d+=np.matmul(tmp,tmp.T)
    co_variance_1d=co_variance_1d/len(desc_1d)

    mean = np.zeros(descriptors[0].shape)
    co_variance = np.zeros((3,3))
    for frame in descriptors:
        mean+=frame
    mean=frame/len(descriptors)

    # print(mean)
    for frame in descriptors:
        tmp=frame-mean
        tc=np.matmul(tmp,tmp.T)
        co_variance+=tc

    co_variance=co_variance/len(descriptors)
    return (mean,co_variance,descriptors,mean_1d,co_variance_1d,desc_1d)

def final_main(input1,input2):
    f1 = read_file(input1) 
    of1 = read_file(input2)
    pf1 = process_video(f1)
    print("video1 processed residual and quantization")
    pof1=process_video(of1)
    print("video2 processed residual and quantization")
    fd1 = extract_video(pf1)
    print("video1 Created co-variance matrix")
    ofd1 = extract_video(pof1)
    print("video2 Created co-variance matrix")
    mean1,co_variance1,disc1,mean_1d_1,co_variance_1d_1,desc_1d_1=final(fd1)
    mean2,co_variance2,disc2,mean_1d_2,co_variance_1d_2,desc_1d_2=final(ofd1)
    distances = []
    print("creating Descriptors");
    for index,disc in enumerate(disc1):
        gm = disc - mean2
        dm = np.matmul(np.matmul(gm.T,np.linalg.inv(co_variance2)),gm)
        dm_sq = np.sqrt(np.abs(dm))
        distances.append(dm_sq)

    distances = np.array(distances)

    dist2 = []
    for index, disc in enumerate(disc2):
        gm = disc - mean2
        dm = np.matmul(np.matmul(gm.T,np.linalg.inv(co_variance2)),gm)
        dm_sq = np.sqrt(np.abs(dm))
        dist2.append(dm_sq)

    dist2 = np.array(dist2)
    fourcc = cv2.VideoWriter_fourcc(*'mp4v') 
    height =f1[0][0].shape[0]+of1[0][0].shape[0]
    width = 325+f1[0][0].shape[1]
    video = cv2.VideoWriter('video.mp4', fourcc, 30, (width,height))
    inital_diff,final_diff = 10000,-1
    result = ''
    print("writing video")
    for index, dist in enumerate(distances):
        heatmap = dist;
        frame,index = f1[index]
        different = False
        if index<len(of1):
            frame2 = of1[index][0]
            diff = dist - dist2[index]
            if not np.allclose(diff, np.zeros(diff.shape)):
                different = True
                inital_diff = min(inital_diff, index)
                final_diff = max(final_diff, index)
                sum1= np.sum(dist)
                sum2 = np.sum(dist2[index])
                    
        new_im = Image.new('RGB', (width, height))
        new_im.paste(Image.fromarray(frame), (0, 0))
        new_im.paste(Image.fromarray(frame2), (0, frame.shape[0]))
        heatmapshow = None
        heatmapshow = cv2.normalize(heatmap, heatmapshow, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
        heatmapshow = cv2.applyColorMap(heatmapshow, cv2.COLORMAP_JET)
        new_im.paste(Image.fromarray(heatmapshow), (frame.shape[1], 0))

        draw = ImageDraw.Draw(new_im)
        text = "The images are same."
        if different:
            text = "The images are different."
        text_width, text_height = draw.textsize(text)

        x = (new_im.width - text_width) / 2
        y = (new_im.height - text_height) / 2

        draw.text((x, y), text, fill=(255, 255, 255))

        new_im = np.array(new_im)
        video.write(new_im)
    outputString = ""
    if inital_diff != 10000:
        outputString+=f"Initial difference at frame {inital_diff} at time {inital_diff/fps} seconds"
        outputString+=f"Final difference at frame {final_diff} at time {final_diff/fps} seconds"
    video.release()
    if(outputString==""):
        outputString= "Not tampering are detected"
    return ("video.mp4",outputString)