import gradio as gr
import cv2
import numpy as np
from PIL import Image,ImageDraw, ImageFont
from transformers import pipeline
import torch
from random import choice
import os
from datetime import datetime

# 初始化对象检测器并移动到GPU（如果可用）
detector = pipeline(model="facebook/detr-resnet-101", use_fast=True)
if torch.cuda.is_available():
    detector.model.to('cuda')

COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
          "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
          "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]



threshold = 90  # 置信度阈值
label_list = ["person", "car", "truck"]

label_color_dict = {}

def query_data(in_pil_img: Image.Image):
    results = detector(in_pil_img)
    print(f"检测结果：{results}")
    return results


def get_font_size(box_width, min_size=10, max_size=48):
    """根据边界框宽度计算合适的字体大小"""
    # 字体大小取决于边界框宽度，取值最小为24
    font_size = max(24,int(box_width / 10))
    return max(min(font_size, max_size), min_size)

def get_text_position(box, text_bbox):
    """根据边界框和文本边界框返回适当的位置"""
    xmin, ymin, xmax, ymax = box['xmin'], box['ymin'], box['xmax'], box['ymax']
    text_width, text_height = text_bbox[2] - text_bbox[0], text_bbox[3] - text_bbox[1]
    
    # 尝试将文本放置在边界框上方，但如果空间不足，则放置在边界框内
    if ymin - text_height >= 0:
        return (xmin, ymin - text_height)  # 上方
    else:
        return (xmin, ymin)  # 内部

def get_annotated_image(in_pil_img):
    draw = ImageDraw.Draw(in_pil_img)
    in_results = query_data(in_pil_img)

    for prediction in in_results:
        box = prediction['box']
        label = prediction['label']
        score = round(prediction['score'] * 100, 1)
        if score < threshold:
            continue  # 过滤掉低置信度的预测结果
        if label not in label_list:
            continue  # 过滤掉不在允许显示的label列表中的预测结果

        if label not in label_color_dict:  # 为每个类别随机分配颜色, 后续维持一致
            color = choice(COLORS)
            label_color_dict[label] = color
        else:
            color = label_color_dict[label]

        # 计算字体大小
        box_width = box['xmax'] - box['xmin']
        font_size = get_font_size(box_width)
        font = ImageFont.truetype(font="arial.ttf", size=font_size)  # 确保你有可用的字体文件

        # 获取文本边界框
        text = f"{label}: {score}%" 
        text_bbox = draw.textbbox((0, 0), text, font=font)

        # 绘制矩形
        draw.rectangle([box['xmin'], box['ymin'], box['xmax'], box['ymax']], outline=color, width=3)

        # 添加文本
        text_pos = get_text_position(box, text_bbox)
        draw.text(text_pos, text, fill=color, font=font)

    # 返回的是原始图像对象，它已经被修改了
    return np.array(in_pil_img.convert('RGB'))


def process_video(input_video_path):
    cap = cv2.VideoCapture(input_video_path)
    if not cap.isOpened():
        raise ValueError("无法打开输入视频文件")

    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = cap.get(cv2.CAP_PROP_FPS)
    
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # 使用 'mp4v' 编码器
    output_dir = './output_videos'  # 指定输出目录
    os.makedirs(output_dir, exist_ok=True)  # 确保输出目录存在

    # 生成唯一文件名
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    output_video_filename = f"output_{timestamp}.mp4"
    output_video_path = os.path.join(output_dir, output_video_filename)
    print(f"输出视频信息：{output_video_path}, {width}x{height}, {fps}fps")
    out = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height))

    while True:
        ret, frame = cap.read()
        if not ret:
            break
        
        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        pil_image = Image.fromarray(rgb_frame)
        print(f"Input frame of shape {rgb_frame.shape} and type {rgb_frame.dtype}")  # 调试信息
        annotated_frame = get_annotated_image(pil_image)
        bgr_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)
        print(f"Annotated frame of shape {bgr_frame.shape} and type {bgr_frame.dtype}")  # 调试信息
        # 确保帧的尺寸与视频输出一致
        if bgr_frame.shape[:2] != (height, width):
            bgr_frame = cv2.resize(bgr_frame, (width, height))

        print(f"Writing frame of shape {bgr_frame.shape} and type {bgr_frame.dtype}")  # 调试信息
        out.write(bgr_frame)

    cap.release()
    out.release()

    # 返回输出视频路径给 Gradio
    return output_video_path

def change_threshold(value):
    global threshold
    threshold = value
    return f"当前置信度阈值为{threshold}%"

def update_labels(selected_labels):
    # 更新 label_list 以匹配用户的选择
    global label_list
    label_list = selected_labels
    return selected_labels

with gr.Blocks(css=".gradio-container {background:lightyellow;}", title="基于AI的安全风险识别及防控应用") as demo:
    gr.HTML("<div style='font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;'>基于AI的安全风险识别及防控应用</div>")
    
    # 设置置信度阈值
    threshold_slider = gr.Slider(minimum=0, maximum=100, value=threshold, step=1, label="置信度阈值")
    textbox = gr.Textbox(value=f"当前置信度阈值为{threshold}%", label="置信度显示")
    # 绑定滑块变化事件到change_threshold函数，同时设置输出为textbox
    threshold_slider.change(fn=change_threshold, inputs=[threshold_slider], outputs=[textbox])

    # 设置允许显示的label列表
    label_checkboxes = gr.CheckboxGroup(choices=label_list, value=label_list, label="检测目标")
    # 允许修改label_list
    label_checkboxes.change(fn=update_labels, inputs=[label_checkboxes], outputs=[label_checkboxes])

    with gr.Row():
        input_video = gr.Video(label="输入视频")
        detect_button = gr.Button("开始检测", variant="primary")
        output_video = gr.Video(label="输出视频")

    # 将process_video函数绑定到按钮点击事件，并将处理后的视频路径传递给output_video
    detect_button.click(process_video, inputs=input_video, outputs=output_video)

demo.launch()