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我们都知道Opencv上有许多算法,可以实现自适应阈值。
https://blog.csdn.net/qq_51491920/article/details/125727129
那么 Openmv 上的自适应阈值要如何实现呢。
废话不多说,直接上代码。如果想要知道原理,可以看上面链接中的文章。
获取前景
import sensor, image, time sensor.reset() # Reset and initialize the sensor. sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to RGB565 (or GRAYSCALE) sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240) sensor.skip_frames(time = 2000) # Wait for settings take effect. clock = time.clock() # Create a clock object to track the FPS. while(True): clock.tick() # Update the FPS clock. img = sensor.snapshot() # Take a picture and return the image. # to the IDE. The FPS should increase once disconnected. hist = img.get_histogram() Thresholds = hist.get_threshold() print(Thresholds) v = Thresholds.value() img.binary([(0,v)])
获取背景
import sensor, image, time sensor.reset() # Reset and initialize the sensor. sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to RGB565 (or GRAYSCALE) sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240) sensor.skip_frames(time = 2000) # Wait for settings take effect. clock = time.clock() # Create a clock object to track the FPS. while(True): clock.tick() # Update the FPS clock. img = sensor.snapshot() # Take a picture and return the image. # to the IDE. The FPS should increase once disconnected. hist = img.get_histogram() Thresholds = hist.get_threshold() print(Thresholds) v = Thresholds.value() img.binary([(v, 255)])
可以根据个人需求,选择所需要的代码。
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