OpenMV4跑不了神经网络，模型和label文件都拷贝到TF卡，提示帧缓冲内存不足“，修改图像分辨率也不行！
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# TensorFlow Lite Mobilenet V1 Example # # Google's Mobilenet V1 detects 1000 classes of objects # # WARNING: Mobilenet is trained on ImageNet and isn't meant to classify anything # in the real world. It's just designed to score well on the ImageNet dataset. # This example just shows off running mobilenet on the OpenMV Cam. However, the # default model is not really usable for anything. You have to use transfer # learning to apply the model to a target problem by re-training the model. # # NOTE: This example only works on the OpenMV Cam H7 Pro (that has SDRAM) and better! # To get the models please see the CNN Network library in OpenMV IDE under # Tools -> Machine Vision. The labels are there too. # You should insert a microSD card into your camera and copy-paste the mobilenet_labels.txt # file and your chosen model into the root folder for ths script to work. # # In this example we slide the detector window over the image and get a list # of activations. Note that use a CNN with a sliding window is extremely compute # expensive so for an exhaustive search do not expect the CNN to be real-time. import sensor, image, time, os, tf sensor.reset() # Reset and initialize the sensor. sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE) sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240) sensor.set_windowing((240, 240)) # Set 240x240 window. sensor.skip_frames(time=2000) # Let the camera adjust. mobilenet_version = "1" # 1 mobilenet_width = "0.5" # 1.0, 0.75, 0.50, 0.25 mobilenet_resolution = "128" # 224, 192, 160, 128 mobilenet = "mobilenet_v%s_%s_%s_quant.tflite" % (mobilenet_version, mobilenet_width, mobilenet_resolution) labels = [line.rstrip('\n') for line in open("mobilenet_labels.txt")] print(labels) clock = time.clock() while(True): clock.tick() img = sensor.snapshot() # net.classify() will run the network on an roi in the image (or on the whole image if the roi is not # specified). A classification score output vector will be generated for each location. At each scale the # detection window is moved around in the ROI using x_overlap (0-1) and y_overlap (0-1) as a guide. # If you set the overlap to 0.5 then each detection window will overlap the previous one by 50%. Note # the computational work load goes WAY up the more overlap. Finally, for multi-scale matching after # sliding the network around in the x/y dimensions the detection window will shrink by scale_mul (0-1) # down to min_scale (0-1). For example, if scale_mul is 0.5 the detection window will shrink by 50%. # Note that at a lower scale there's even more area to search if x_overlap and y_overlap are small... # Setting x_overlap=-1 forces the window to stay centered in the ROI in the x direction always. If # y_overlap is not -1 the method will search in all vertical positions. # Setting y_overlap=-1 forces the window to stay centered in the ROI in the y direction always. If # x_overlap is not -1 the method will serach in all horizontal positions. # default settings just do one detection... change them to search the image... for obj in tf.classify(mobilenet, img, min_scale=1.0, scale_mul=0.5, x_overlap=-1, y_overlap=-1): print("**********\nTop 5 Detections at [x=%d,y=%d,w=%d,h=%d]" % obj.rect()) img.draw_rectangle(obj.rect()) # This combines the labels and confidence values into a list of tuples # and then sorts that list by the confidence values. sorted_list = sorted(zip(labels, obj.output()), key = lambda x: x[1], reverse = True) for i in range(5): print("%s = %f" % (sorted_list[i][0], sorted_list[i][1])) print(clock.fps(), "fps")
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edge impulse只能在OpenMV4 Plus上用，OpenMV4上不行。