例程讲解04-Image-Filters->advanced_frame_differencing高级帧间差分

# 高级帧间差分例子
#
# 注意: 为了运行这个程序,你需要插入SD卡。
#
# 这个例子示范了OpenMV的帧间差分算法。
# 之所以叫做高级的帧间查分,是因为背景图片会实时更新

import sensor, image, pyb, os, time

BG_UPDATE_FRAMES = 50 # How many frames before blending. 融合前有多少帧图像
BG_UPDATE_BLEND = 128 # How much to blend by... ([0-256]==[0.0-1.0]).

sensor.reset() # 初始化摄像头

sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.RGB565
#设置图像色彩格式,有RGB565色彩图和GRAYSCALE灰度图两种

sensor.set_framesize(sensor.QVGA) # or sensor.QQVGA (or others)  
#设置图像像素大小

sensor.skip_frames(time = 2000) # 让新的设置生效
sensor.set_auto_whitebal(False) # 关闭白平衡
clock = time.clock() # 跟踪FPS帧率

if not "temp" in os.listdir(): os.mkdir("temp") # 新建一个新的文件夹

print("About to save background image...")
sensor.skip_frames(time = 2000) # 给用户一个时间来准备
sensor.snapshot().save("temp/bg.bmp")
print("Saved background image - Now frame differencing!")

frame_count = 0
while(True):
    clock.tick() # 追踪两个snapshots()之间经过的毫秒数.
    img = sensor.snapshot() # 拍一张照片,返回图像

    frame_count += 1
    if frame_count > BG_UPDATE_FRAMES:
        frame_count = 0
        # Blend in new frame. We're doing 256-alpha here because we want to
        # blend the new frame into the backgound. Not the background into the
        # new frame which would be just alpha. Blend replaces each pixel by
        # ((NEW*(alpha))+(OLD*(256-alpha)))/256. So, a low alpha results in
        # low blending of the new image while a high alpha results in high
        # blending of the new image. We need to reverse that for this update.

        # 融入新的帧。
        # 我们在这里做256-alpha因为我们想把新帧混合到背景中而不是背景进入新帧,背景融入新帧只是alpha。
        # Blend将每个像素替换为((NEW*(alpha))+(OLD*(256-alpha)) /256.
        # 因此,低alpha导致新图像的低混合,而高alpha导致新图像的高混。
        # 我们需要在这次更新中逆转这一点
        img.blend("temp/bg.bmp", alpha=(256-BG_UPDATE_BLEND))
        img.save("temp/bg.bmp")

    # Replace the image with the "abs(NEW-OLD)" frame difference.
    # 将图像替换为 "abs(NEW-OLD)" 帧差
    img.difference("temp/bg.bmp")

    print(clock.fps()) # 注意: 当连接电脑后,OpenMV会变成一半的速度。当不连接电脑,帧率会增加。

星瞳科技OpenMV官方中文文档函数讲解:

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