如何将处理后的Numpy帧作为video_reference传入Roboflow推理管道

方案一：手动处理帧并调用推理（推荐） #

由于video_reference仅支持视频路径、设备ID或RTSP流，无法直接传入处理后的帧，最直接的解决方式是手动控制帧的采集与处理流程，跳过InferencePipeline的自动流读取逻辑，直接调用推理接口并处理结果。

修改后的完整代码 #

import cv2
import os
import time
import numpy as np
from roboflow import Roboflow
from Cropping import ExtractAndStraightenFromImage
from LocateGrid import DetectGrid

# 初始化Roboflow模型
rf = Roboflow(api_key="<your-api-key>")
project = rf.workspace().project("crap-object-detection")
model = project.version(1).model

IMAGE_FILE_PATH = os.path.join("Capture", "BoardPictures")

# 创建截图保存目录
if not os.path.exists(IMAGE_FILE_PATH):  
    os.makedirs(IMAGE_FILE_PATH)

# 启动摄像头
vid = cv2.VideoCapture(0)
is_automatic = False

def apply_morphological_ops(img):
    kernel = np.ones((5,5),np.uint8)
    opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
    return opening

def my_custom_sink(predictions, frame):
    # 这里保留你原有的结果处理逻辑，比如绘制检测框、输出结果等
    print(predictions)

try:
    while True:
        ret, frame = vid.read()
        if not ret:
            print("Failed to grab frame")
            break

        key = cv2.waitKey(1)

        # 空格键保存截图
        if key == 32:
            unique_filename = time.strftime("%Y%m%d_%H%M%S") + ".png"
            screenshot_path = os.path.join(IMAGE_FILE_PATH, unique_filename)
            boardImg = ExtractAndStraightenFromImage(frame)
            cv2.imwrite(screenshot_path, boardImg)
            print(f"Screenshot saved as {screenshot_path}")

        # 裁剪校正帧
        boardImg = ExtractAndStraightenFromImage(frame)
        
        # 调用Roboflow推理接口
        predictions = model.predict(boardImg, confidence=40, overlap=30).json()
        
        # 传入自定义结果处理函数
        my_custom_sink(predictions, boardImg)

        # 显示画面
        cv2.imshow("Frame", frame)
        cv2.imshow("Board img", boardImg)
            
finally:
    vid.release()
    cv2.destroyAllWindows()

方案二：创建虚拟视频流供Pipeline读取 #

如果一定要使用InferencePipeline的video_reference参数，可以将处理后的帧推送到本地虚拟视频流（如RTSP），再让Pipeline读取该流地址。

实现步骤 #

1. 安装ffmpeg用于创建RTSP流服务；
2. 在帧处理循环中，将校正后的boardImg写入虚拟流；
3. 将虚拟流地址传入video_reference。

示例代码 #

import cv2
import os
import time
import numpy as np
from roboflow import Roboflow
from Cropping import ExtractAndStraightenFromImage
from LocateGrid import DetectGrid

# 初始化推理管道，读取本地RTSP流
pipeline = Roboflow.InferencePipeline.init(
    model_id="crap-object-detection/1",
    api_key="<your-api-key>",
    video_reference="rtsp://localhost:8554/stream",
    on_prediction=my_custom_sink,
)
pipeline.start()

IMAGE_FILE_PATH = os.path.join("Capture", "BoardPictures")
if not os.path.exists(IMAGE_FILE_PATH):  
    os.makedirs(IMAGE_FILE_PATH)

vid = cv2.VideoCapture(0)
# 替换为你的boardImg实际宽高
BOARD_IMG_WIDTH = 640
BOARD_IMG_HEIGHT = 480

# 配置VideoWriter，推送帧到RTSP流
fourcc = cv2.VideoWriter_fourcc(*'H264')
out = cv2.VideoWriter(
    'rtsp://localhost:8554/stream',
    fourcc,
    20.0,
    (BOARD_IMG_WIDTH, BOARD_IMG_HEIGHT)
)

def apply_morphological_ops(img):
    kernel = np.ones((5,5),np.uint8)
    opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
    return opening

def my_custom_sink(predictions, frame):
    # 自定义结果处理逻辑
    print(predictions)

try:
    while True:
        ret, frame = vid.read()
        if not ret:
            print("Failed to grab frame")
            break

        key = cv2.waitKey(1)
        if key == 32:
            unique_filename = time.strftime("%Y%m%d_%H%M%S") + ".png"
            screenshot_path = os.path.join(IMAGE_FILE_PATH, unique_filename)
            boardImg = ExtractAndStraightenFromImage(frame)
            cv2.imwrite(screenshot_path, boardImg)
            print(f"Screenshot saved as {screenshot_path}")

        boardImg = ExtractAndStraightenFromImage(frame)
        # 将处理后的帧写入虚拟流
        out.write(cv2.resize(boardImg, (BOARD_IMG_WIDTH, BOARD_IMG_HEIGHT)))

        cv2.imshow("Frame", frame)
        cv2.imshow("Board img", boardImg)
            
finally:
    vid.release()
    out.release()
    cv2.destroyAllWindows()
    pipeline.stop()

注意：此方案需提前启动ffmpeg的RTSP服务，例如执行命令：ffmpeg -listen 1 -i rtsp://localhost:8554/stream -c copy -f rtsp rtsp://localhost:8554/stream

内容的提问来源于stack exchange，提问作者doodle911