在做人脸识别的时候,能够让电脑屏幕显示彩色的图像吗?
-
在做人脸识别的时候,能够让电脑屏幕显示彩色的图像吗???
-
没意义啊,因为人脸识别的算法是黑白的
-
@kidswong999 意思就是不能让电脑屏幕显示彩色的吗???因为老师想看看彩色的样子,并且想保存彩色的照片
-
那你直接在程序里把
GRAYSCALE
改成RGB565
就完事了。
-
@kidswong999 那还能进行人脸识别吗????
-
你试试不就知道了 ,我刚才运行了一下没问题。
-
@kidswong999 啊???我直接运行就卡死了???我们的不一样吗???能麻烦你把代码给我看一下吗???
-
# Face Detection Example # # This example shows off the built-in face detection feature of the OpenMV Cam. # # Face detection works by using the Haar Cascade feature detector on an image. A # Haar Cascade is a series of simple area contrasts checks. For the built-in # frontalface detector there are 25 stages of checks with each stage having # hundreds of checks a piece. Haar Cascades run fast because later stages are # only evaluated if previous stages pass. Additionally, your OpenMV Cam uses # a data structure called the integral image to quickly execute each area # contrast check in constant time (the reason for feature detection being # grayscale only is because of the space requirment for the integral image). import sensor, time, image # Reset sensor sensor.reset() # Sensor settings sensor.set_contrast(1) sensor.set_gainceiling(16) # HQVGA and GRAYSCALE are the best for face tracking. sensor.set_framesize(sensor.HQVGA) sensor.set_pixformat(sensor.RGB565) # Load Haar Cascade # By default this will use all stages, lower satges is faster but less accurate. face_cascade = image.HaarCascade("frontalface", stages=25) print(face_cascade) # FPS clock clock = time.clock() while (True): clock.tick() # Capture snapshot img = sensor.snapshot() # Find objects. # Note: Lower scale factor scales-down the image more and detects smaller objects. # Higher threshold results in a higher detection rate, with more false positives. objects = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25) # Draw objects for r in objects: img.draw_rectangle(r) # Print FPS. # Note: Actual FPS is higher, streaming the FB makes it slower. print(clock.fps())
-
@kidswong999 谢谢了