165 lines
6.1 KiB
Python
165 lines
6.1 KiB
Python
import cv2
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import time
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import os
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import numpy as np
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from PIL import Image
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from skimage.metrics import structural_similarity as ssim
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import threading
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import transport_client
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from transport_client import send_images # 导入上传函数
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# ================== 配置数 ==================
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url = "rtsp://admin:XJ123456@192.168.1.51:554/streaming/channels/101"
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save_interval = 15 # 每隔 N 帧处理一次（可调）
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SSIM_THRESHOLD = 0.9 # SSIM 相似度阈值，>0.9 认为太像
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output_dir = os.path.join("userdata", "image") # 固定路径：userdata/image
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# 灰色判断参数
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GRAY_LOWER = 70
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GRAY_UPPER = 230
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GRAY_RATIO_THRESHOLD = 0.7
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# 上传配置（新增）
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UPLOAD_SERVER_URL = "http://www.xj-robot.com:6000/upload"
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UPLOAD_SITE_NAME = "FactoryA"
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UPLOAD_LINE_ID = "Line1"
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UPLOAD_PURPOSE = "DET"
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# 创建输出目录
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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print(f"已创建目录: {output_dir}")
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def is_large_gray(image, gray_lower=GRAY_LOWER, gray_upper=GRAY_UPPER, ratio_thresh=GRAY_RATIO_THRESHOLD):
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"""
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判断图片是否大面积为灰色（R/G/B 都在 [gray_lower, gray_upper] 区间）
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"""
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img_array = np.array(image)
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if len(img_array.shape) != 3 or img_array.shape[2] != 3:
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return True # 非三通道图视为无效/灰色
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h, w, _ = img_array.shape
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total = h * w
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gray_mask = (
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(img_array[:, :, 0] >= gray_lower) & (img_array[:, :, 0] <= gray_upper) &
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(img_array[:, :, 1] >= gray_lower) & (img_array[:, :, 1] <= gray_upper) &
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(img_array[:, :, 2] >= gray_lower) & (img_array[:, :, 2] <= gray_upper)
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)
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gray_pixels = np.sum(gray_mask)
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gray_ratio = gray_pixels / total
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return gray_ratio > ratio_thresh
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max_retry_seconds = 10 # 最大重试时间为10秒
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retry_interval_seconds = 1 # 每隔1秒尝试重新连接一次
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while True: # 外层循环用于处理重新连接逻辑
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cap = cv2.VideoCapture(url)
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start_time = time.time() # 记录开始尝试连接的时间
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while not cap.isOpened(): # 如果无法打开摄像头，则进入重试逻辑
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if time.time() - start_time >= max_retry_seconds:
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print(f"已尝试重新连接 {max_retry_seconds} 秒，但仍无法获取视频流。")
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exit()
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print("无法打开摄像头，正在尝试重新连接...")
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time.sleep(retry_interval_seconds) # 等待一段时间后再次尝试
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cap = cv2.VideoCapture(url)
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print("✅ 开始读取视频流...")
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frame_count = 0
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last_gray = None # 用于 SSIM 去重
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try:
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while True:
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ret, frame = cap.read()
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if not ret:
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print("读取帧失败，可能是流中断或摄像头断开")
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cap.release() # 释放资源以便重新连接
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break # 跳出内层循环尝试重新连接
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frame_count += 1
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# 仅在指定间隔处理保存逻辑
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if frame_count % save_interval != 0:
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cv2.imshow('Camera Stream (Live)', frame)
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if cv2.waitKey(1) == ord('q'):
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raise KeyboardInterrupt
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continue
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print(f"处理帧 {frame_count}")
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# 转为 PIL 图像（用于后续判断和旋转）
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rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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pil_image = Image.fromarray(rgb_frame)
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# STEP 1: 判断是否为大面积灰色（优先级最高）
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if is_large_gray(pil_image):
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print(f"跳过：大面积灰色图像 (frame_{frame_count})")
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cv2.imshow('Camera Stream (Live)', frame)
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if cv2.waitKey(1) == ord('q'):
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raise KeyboardInterrupt
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continue
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# STEP 2: 判断是否为重复帧（基于 SSIM）
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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if last_gray is not None:
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try:
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similarity = ssim(gray, last_gray)
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if similarity > SSIM_THRESHOLD:
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print(f"跳过：与上一帧太相似 (SSIM={similarity:.3f})")
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cv2.imshow('Camera Stream (Live)', frame)
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if cv2.waitKey(1) == ord('q'):
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raise KeyboardInterrupt
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continue
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except Exception as e:
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print(f"SSIM 计算异常: {e}")
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# 更新 last_gray 用于下一帧比较
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last_gray = gray.copy()
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# STEP 3: 旋转 180 度
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rotated_pil = pil_image.rotate(180, expand=False)
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# 生成文件名（时间戳 + 毫秒防重），使用 .png 扩展名
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timestamp = time.strftime("%Y%m%d_%H%M%S")
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ms = int((time.time() % 1) * 1000)
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filename = f"frame_{timestamp}_{ms:03d}.png" # ✅ 确保是 .png
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filepath = os.path.join(output_dir, filename)
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# 保存图像为 PNG 格式（无损）
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try:
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rotated_pil.save(filepath, format='PNG') # ✅ PNG 不需要 quality 参数
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print(f"已保存: {filepath}")
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# ✅ 新增：异步上传
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def upload_task():
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send_images(
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folder_path=output_dir,
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server_url=UPLOAD_SERVER_URL,
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site_name=UPLOAD_SITE_NAME,
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line_id=UPLOAD_LINE_ID,
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purpose=UPLOAD_PURPOSE
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)
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threading.Thread(target=upload_task, daemon=True).start()
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print(f"📤 已提交上传任务: {filename}")
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except Exception as e:
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print(f"保存失败 {filename}: {e}")
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# 显示画面
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cv2.imshow('Camera Stream (Live)', frame)
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if cv2.waitKey(1) == ord('q'):
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raise KeyboardInterrupt
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except KeyboardInterrupt:
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print("\n用户中断")
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break # 跳出外层循环并退出程序
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finally:
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cap.release()
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cv2.destroyAllWindows()
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print(f"视频流已关闭，共处理 {frame_count} 帧。")
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print("程序结束") |