import cv2
import time
import os
import numpy as np
from PIL import Image
from skimage.metrics import structural_similarity as ssim

# ================== 配置参数 ==================
url = "rtsp://admin:XJ123456@192.168.1.51:554/streaming/channels/101"
save_interval = 15  # 每隔 N 帧处理一次（可调）
SSIM_THRESHOLD = 0.9 # SSIM 相似度阈值，>0.9 认为太像（重复，测试肉眼看不出区别的差不多在0.92以上，肉眼看的出区别的在0.7-0.85）
output_dir = os.path.join("userdata", "image")  # 固定路径：userdata/image     可修改

# 灰色判断参数/测试过异常的完全可删除，且不会影响正常图片
GRAY_LOWER = 70
GRAY_UPPER = 230
GRAY_RATIO_THRESHOLD = 0.7

# 创建输出目录
if not os.path.exists(output_dir):
    os.makedirs(output_dir)
    print(f"已创建目录: {output_dir}")

def is_large_gray(image, gray_lower=GRAY_LOWER, gray_upper=GRAY_UPPER, ratio_thresh=GRAY_RATIO_THRESHOLD):
    """
    判断图片是否大面积为灰色（R/G/B 都在 [gray_lower, gray_upper] 区间）
    """
    img_array = np.array(image)
    if len(img_array.shape) != 3 or img_array.shape[2] != 3:
        return True  # 非三通道图视为无效/灰色

    h, w, _ = img_array.shape
    total = h * w

    gray_mask = (
        (img_array[:, :, 0] >= gray_lower) & (img_array[:, :, 0] <= gray_upper) &
        (img_array[:, :, 1] >= gray_lower) & (img_array[:, :, 1] <= gray_upper) &
        (img_array[:, :, 2] >= gray_lower) & (img_array[:, :, 2] <= gray_upper)
    )
    gray_pixels = np.sum(gray_mask)
    gray_ratio = gray_pixels / total

    return gray_ratio > ratio_thresh

# ================ 主程序开始 ================
cap = cv2.VideoCapture(url)
if not cap.isOpened():
    print("❌ 无法打开摄像头")
    exit()

print("✅ 开始读取视频流...")
frame_count = 0
last_gray = None  # 用于 SSIM 去重

try:
    while True:
        ret, frame = cap.read()
        if not ret:
            print("读取帧失败，可能是流中断或摄像头断开")
            break

        frame_count += 1

        # 仅在指定间隔处理保存逻辑
        if frame_count % save_interval != 0:
            cv2.imshow('Camera Stream (Live)', frame)
            if cv2.waitKey(1) == ord('q'):
                break
            continue

        print(f"处理帧 {frame_count}")

        # 转为 PIL 图像（用于后续判断和旋转）
        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        pil_image = Image.fromarray(rgb_frame)

        # STEP 1: 判断是否为大面积灰色（优先级最高）
        if is_large_gray(pil_image):
            print(f"跳过：大面积灰色图像 (frame_{frame_count})")
            cv2.imshow('Camera Stream (Live)', frame)
            if cv2.waitKey(1) == ord('q'):
                break
            continue

        # STEP 2: 判断是否为重复帧（基于 SSIM）
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        if last_gray is not None:
            try:
                similarity = ssim(gray, last_gray)
                if similarity > SSIM_THRESHOLD:
                    print(f"跳过：与上一帧太相似 (SSIM={similarity:.3f})")
                    cv2.imshow('Camera Stream (Live)', frame)
                    if cv2.waitKey(1) == ord('q'):
                        break
                    continue
            except Exception as e:
                print(f"SSIM 计算异常: {e}")

        # 更新 last_gray 用于下一帧比较
        last_gray = gray.copy()

        # STEP 3: 旋转 180 度
        rotated_pil = pil_image.rotate(180, expand=False)

        # 生成文件名（时间戳 + 毫秒防重）
        timestamp = time.strftime("%Y%m%d_%H%M%S")
        ms = int((time.time() % 1) * 1000)
        filename = f"frame_{timestamp}_{ms:03d}.jpg"
        filepath = os.path.join(output_dir, filename)

        # 保存图像
        try:
            rotated_pil.save(filepath, format='JPEG', quality=95)
            print(f"已保存: {filepath}")
        except Exception as e:
            print(f"保存失败 {filename}: {e}")

        # 显示画面
        cv2.imshow('Camera Stream (Live)', frame)
        if cv2.waitKey(1) == ord('q'):
            break

except KeyboardInterrupt:
    print("\n用户中断")

finally:
    cap.release()
    cv2.destroyAllWindows()
    print(f"视频流已关闭，共处理 {frame_count} 帧。")