import numpy as np
import PIL
from PIL import Image, ImageDraw
import gradio as gr
import torch
import easyocr
import os
from pathlib import Path
import cv2
import pandas as pd


#torch.hub.download_url_to_file('https://github.com/AaronCWacker/Yggdrasil/blob/main/images/BeautyIsTruthTruthisBeauty.JPG', 'BeautyIsTruthTruthisBeauty.JPG')
#torch.hub.download_url_to_file('https://github.com/AaronCWacker/Yggdrasil/blob/main/images/PleaseRepeatLouder.jpg', 'PleaseRepeatLouder.jpg')
#torch.hub.download_url_to_file('https://github.com/AaronCWacker/Yggdrasil/blob/main/images/ProhibitedInWhiteHouse.JPG', 'ProhibitedInWhiteHouse.JPG')

torch.hub.download_url_to_file('https://raw.githubusercontent.com/AaronCWacker/Yggdrasil/master/images/20-Books.jpg','20-Books.jpg')
torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/english.png', 'COVID.png')
torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/chinese.jpg', 'chinese.jpg')
torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/japanese.jpg', 'japanese.jpg')
torch.hub.download_url_to_file('https://i.imgur.com/mwQFd7G.jpeg', 'Hindi.jpeg')

def draw_boxes(image, bounds, color='yellow', width=2):
    draw = ImageDraw.Draw(image)
    for bound in bounds:
        p0, p1, p2, p3 = bound[0]
        draw.line([*p0, *p1, *p2, *p3, *p0], fill=color, width=width)
    return image

def box_size(box):
    points = box[0]
    if len(points) == 4:
        x1, y1 = points[0]
        x2, y2 = points[2]
        return abs(x1 - x2) * abs(y1 - y2)
    else:
        return 0

def box_position(box):
    return (box[0][0][0] + box[0][2][0]) / 2, (box[0][0][1] + box[0][2][1]) / 2


def inference(video, lang, time_step):
    output = 'results.mp4'
    reader = easyocr.Reader(lang)
    bounds = []   
    vidcap = cv2.VideoCapture(video)
    success, frame = vidcap.read()
    count = 0
    frame_rate = vidcap.get(cv2.CAP_PROP_FPS)
    output_frames = []
    temporal_profiles = []
    max_boxes = 10
    
    # Get the positions of the largest boxes in the first frame
    while success and not bounds:
        if count == 0:
            bounds = reader.readtext(frame)
            im = PIL.Image.fromarray(frame)
            im_with_boxes = draw_boxes(im, bounds)
            largest_boxes = sorted(bounds, key=lambda x: box_size(x), reverse=True)[:max_boxes]
            positions = [box_position(b) for b in largest_boxes]
            temporal_profiles = [[] for _ in range(len(largest_boxes))]
        success, frame = vidcap.read()
        count += 1
    
    # Match bboxes to position and store the text read by OCR
    while success:
        if count % (int(frame_rate * time_step)) == 0:
            bounds = reader.readtext(frame)
            for box in bounds:
                bbox_pos = box_position(box)
                for i, position in enumerate(positions):
                    distance = np.linalg.norm(np.array(bbox_pos) - np.array(position))
                    if distance < 50:
                        temporal_profiles[i].append((count / frame_rate, box[1]))
                        break
            im = PIL.Image.fromarray(frame)
            im_with_boxes = draw_boxes(im, bounds)
            output_frames.append(np.array(im_with_boxes))
        success, frame = vidcap.read()
        count += 1
    
    # Default resolutions of the frame are obtained. The default resolutions are system dependent.
    # We convert the resolutions from float to integer.
    width = int(vidcap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = vidcap.get(cv2.CAP_PROP_FPS)
    frames_total = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))

    # Define the codec and create VideoWriter object.
    temp = f"{Path(output).stem}_temp{Path(output).suffix}"
    output_video = cv2.VideoWriter(
        temp, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height)
    )
    # output_video = cv2.VideoWriter(output, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
    for frame in output_frames:
        output_video.write(frame)
    output_video.release()
    vidcap.release()

    # Compressing the video for smaller size and web compatibility.
    os.system(
        f"ffmpeg -y -i {temp} -c:v libx264 -b:v 5000k -minrate 1000k -maxrate 8000k -pass 1 -c:a aac -f mp4 /dev/null && ffmpeg -y -i {temp} -c:v libx264 -b:v 5000k -minrate 1000k -maxrate 8000k -pass 2 -c:a aac -movflags faststart {output}"
    )
    os.system(f"rm -rf {temp} ffmpeg2pass-0.log ffmpeg2pass-0.log.mbtree")
    
    # Format temporal profiles as a DataFrame
    df = pd.DataFrame(columns=["Box", "Time (s)", "Text"])
    for i, profile in enumerate(temporal_profiles):
        for t, text in profile:
            df = df.append({"Box": f"Box {i+1}", "Time (s)": t, "Text": text}, ignore_index=True)

    return output, df



title = '🖼️Video to Multilingual OCR👁️Gradio'
description = 'Multilingual OCR which works conveniently on all devices in multiple languages.'
article = "<p style='text-align: center'></p>"

examples = [
#['PleaseRepeatLouder.jpg',['ja']],['ProhibitedInWhiteHouse.JPG',['en']],['BeautyIsTruthTruthisBeauty.JPG',['en']],
['20-Books.jpg',['en']],['COVID.png',['en']],['chinese.jpg',['ch_sim', 'en']],['japanese.jpg',['ja', 'en']],['Hindi.jpeg',['hi', 'en']]
]

css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
choices = [
    "ch_sim",
    "ch_tra",
    "de",
    "en",
    "es",
    "ja",
    "hi",
    "ru"
]


gr.Interface(
    inference,
    [
        # gr.inputs.Image(type='file', label='Input Image'),
        gr.inputs.Video(label='Input Video'),
        gr.inputs.CheckboxGroup(choices, type="value", default=['en'], label='Language'),
        gr.inputs.Number(label='Time Step (in seconds)', default=1.0)
    ],
    [
        gr.outputs.Video(label='Output Video'),
        gr.outputs.Dataframe(headers=['Box', 'Time (s)', 'Text'])
    ],
    title=title,
    description=description,
    article=article,
    # examples=examples,
    css=css,
    enable_queue=True
).launch(debug=True)