升级必须要连着bootloader都升级才行,IDE升级有提示的。
kidswong999 发布的帖子
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RE: openmv4H7报错ValueError: Failed to allocate tensors,怎么回事
对于H7, net = ml.Model("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
改为 net = ml.Model("trained.tflite", load_to_fb=True),因为这个代码不是给H7用的,是给H7 Plus用的。
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RE: openmv4H7报错(Failed to allocate tensors),这是怎么回事???
用这个代码
# Edge Impulse - OpenMV FOMO Object Detection Example # # This work is licensed under the MIT license. # Copyright (c) 2013-2024 OpenMV LLC. All rights reserved. # https://github.com/openmv/openmv/blob/master/LICENSE import sensor, image, time, ml, math, uos, gc 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. net = None labels = None min_confidence = 0.5 # load the model, alloc the model file on the heap if we have at least 64K free after loading net = ml.Model("trained.tflite", load_to_fb=True) labels = [line.rstrip('\n') for line in open("labels.txt")] colors = [ # Add more colors if you are detecting more than 7 types of classes at once. (255, 0, 0), ( 0, 255, 0), (255, 255, 0), ( 0, 0, 255), (255, 0, 255), ( 0, 255, 255), (255, 255, 255), ] threshold_list = [(math.ceil(min_confidence * 255), 255)] def fomo_post_process(model, inputs, outputs): ob, oh, ow, oc = model.output_shape[0] x_scale = inputs[0].roi[2] / ow y_scale = inputs[0].roi[3] / oh scale = min(x_scale, y_scale) x_offset = ((inputs[0].roi[2] - (ow * scale)) / 2) + inputs[0].roi[0] y_offset = ((inputs[0].roi[3] - (ow * scale)) / 2) + inputs[0].roi[1] l = [[] for i in range(oc)] for i in range(oc): img = image.Image(outputs[0][0, :, :, i] * 255) blobs = img.find_blobs( threshold_list, x_stride=1, y_stride=1, area_threshold=1, pixels_threshold=1 ) for b in blobs: rect = b.rect() x, y, w, h = rect score = ( img.get_statistics(thresholds=threshold_list, roi=rect).l_mean() / 255.0 ) x = int((x * scale) + x_offset) y = int((y * scale) + y_offset) w = int(w * scale) h = int(h * scale) l[i].append((x, y, w, h, score)) return l clock = time.clock() while(True): clock.tick() img = sensor.snapshot() for i, detection_list in enumerate(net.predict([img], callback=fomo_post_process)): if i == 0: continue # background class if len(detection_list) == 0: continue # no detections for this class? print("********** %s **********" % labels[i]) for x, y, w, h, score in detection_list: center_x = math.floor(x + (w / 2)) center_y = math.floor(y + (h / 2)) print(f"x {center_x}\ty {center_y}\tscore {score}") img.draw_circle((center_x, center_y, 12), color=colors[i]) print(clock.fps(), "fps", end="\n\n")
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RE: 第一段代码没报错,第二段出现NoneType object has no attribute cx,叹号处报错
你没有考虑到没有找到色块的情况。redmax_blob就是None,然后就没有中心点了。
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RE: openmv连接不到电脑
应该是固件烧录错了,
按照这个连接烧录固件,https://singtown.com/cn/learn/50903
如果还不行,那就直接联系售后寄回维修。 -
RE: 颜色识别和模板识别需要相互切换,我想用中断计数但是一按中断就会和openmv断联
- 不要用try catch把主程序包起来,出错你都看不到。
- 没有import image, sensor
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RE: OverflowError: buffer too small 遇到图片情况就会报错
THRESHOLD = (1, 28, -21, 5, -20, 35) import sensor,image,time,gc from pyb import LED from pid import PID from pyb import UART uart = UART(3,115200,bits=8, parity=None, stop=1, timeout_char=2000, read_buf_len=1024) def sending_data(data1): global uart data = bytearray([0x23, data1, 0x40]) uart.write(data); rho_pid = PID(p=0.6, i=0, d=0.15) theta_pid = PID(p=0.003, i=0, d=0.002) LED(1).on() LED(2).on() LED(3).on() sensor.reset() sensor.set_vflip(True) sensor.set_hmirror(True) sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQQVGA) sensor.skip_frames(time = 2000) clock = time.clock() while(True): clock.tick() img = sensor.snapshot().binary([THRESHOLD]) line = img.get_regression([(100,100)], robust = True) if (line): rho_err = abs(line.rho())-img.width()/2 if line.theta()>90: theta_err = line.theta()-180 else: theta_err = line.theta() img.draw_line(line.line(), color = 127) if line.magnitude()>8: rho_output = rho_pid.get_pid(rho_err,1) theta_output = theta_pid.get_pid(theta_err,1) output = rho_output+theta_output sending_data(int(output)) print(int(output)) pass gc.collect()