134 lines
5.4 KiB
Python
134 lines
5.4 KiB
Python
from maix import image, app, time
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# 橘黄色乒乓球的颜色阈值配置（HSV色域）
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thresholds = [
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[73, 93, 7, 27, 13, 33],
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[40, 65, 30, 60, 40, 70],
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[39, 59, 41, 61, 48, 68],
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]
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# 新增：丢失目标的阈值配置（可根据实际帧率调整，建议5-10帧）
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LOST_FRAME_THRESHOLD = 10 # 连续x帧没识别到才判定为真正丢失
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SEND_INTERVAL = 5 # 控制指令发送间隔（避免高频发送，单位：ms）
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def select_largest_blob(blobs):
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"""
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筛选并选取最大的有效blob（替换原融合逻辑）
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1. 筛除宽/高小于10px的小blob（过滤噪声）
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2. 从剩余blob中选取面积最大的单个blob
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:param blobs: 识别到的所有blob列表
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:return: 最大blob的矩形参数 (x, y, width, height) 和中心点 (cx, cy)；未识别到则返回None, None
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"""
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if not blobs:
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return None, (-1,-1)
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# 步骤1：过滤掉宽/高小于10px的无效blob
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valid_blobs = []
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for b in blobs:
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x, y, w, h = b[0], b[1], b[2], b[3]
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if w >= 10 and h >= 10: # 筛除边距（宽/高）小于10px的blob
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valid_blobs.append(b)
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# 过滤后无有效blob，返回None
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if not valid_blobs:
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return None, (-1,-1)
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# 步骤2：选取面积（w*h）最大的blob
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largest_blob = max(valid_blobs, key=lambda b: b[2] * b[3])
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# 提取最大blob的矩形参数和中心点
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x, y, w, h = largest_blob[0], largest_blob[1], largest_blob[2], largest_blob[3]
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cx = x + w // 2
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cy = y + h // 2
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return (x, y, w, h), (cx, cy)
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def calculate_deviation_ball(center, image_center):
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"""
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优化：仅计算偏移量，不直接发送指令（将指令发送逻辑移到主循环统一处理）
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:return: (deviation_x, deviation_y) 或 None（无有效目标）
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"""
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center_x, center_y = center
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if center_x == -1 and center_y == -1 :
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return None # 无有效目标
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# 计算x/y方向的偏移偏差（目标中心 - 图像中心）
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deviation_x = center_x - image_center[0]
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deviation_y = center_y - image_center[1]
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# 仅当偏移超过阈值时返回有效偏移，否则返回None（避免微小抖动）
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if abs(deviation_x) > 10 or abs(deviation_y) > 10 :
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return (deviation_x, deviation_y)
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else:
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return None
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def trace_ball_main(resources):
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detector = resources["detector"]
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cam = resources["cam"]
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disp = resources["disp"]
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serial_dev = resources["serial_dev"]
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img_width = resources["img_width"]
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img_height = resources["img_height"]
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image_center = resources["image_center"]
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# 新增：状态变量初始化
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last_valid_deviation = None # 记录最后一次有效偏移量
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lost_frames = 0 # 丢失帧数计数
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last_send_time = 0 # 最后一次发送指令的时间（防高频）
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while not app.need_exit():
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current_time = time.ticks_ms()
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img = cam.read().flip(image.FlipDir.Y)
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# 识别色块（像素阈值设为10，过滤小噪声）
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blobs = img.find_blobs(thresholds, pixels_threshold=10)
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# 筛选并选取最大的有效blob
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largest_rect, target_center = select_largest_blob(blobs)
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# 计算当前帧的偏移量
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current_deviation = calculate_deviation_ball(target_center, image_center)
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# -------------------------- 核心优化逻辑 --------------------------
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if current_deviation is not None:
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# 1. 识别到有效目标：重置丢失计数，更新最后一次有效偏移
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lost_frames = 0
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last_valid_deviation = current_deviation
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# 控制发送频率，避免串口堵塞
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if current_time - last_send_time >= SEND_INTERVAL:
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dx, dy = current_deviation
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cmd = f"DX:{dx},DY:{dy};"
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serial_dev.write_str(cmd)
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print(f"识别到目标: {cmd}")
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last_send_time = current_time
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else:
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# 2. 未识别到目标：累计丢失帧数
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lost_frames += 1
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if lost_frames < LOST_FRAME_THRESHOLD and last_valid_deviation is not None:
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# 2.1 丢失帧数未达阈值：继续发送最后一次有效偏移
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if current_time - last_send_time >= SEND_INTERVAL:
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dx, dy = last_valid_deviation
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cmd = f"DX:{dx},DY:{dy};"
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serial_dev.write_str(cmd)
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print(f"短暂丢失，沿用最后偏移: {cmd} (丢失帧数: {lost_frames})")
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last_send_time = current_time
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else:
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# 2.2 丢失帧数达阈值：发送9999，重置最后有效偏移
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if current_time - last_send_time >= SEND_INTERVAL:
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cmd = f"DX:9999,DY:9999;"
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serial_dev.write_str(cmd)
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print(f"目标丢失超过阈值: {cmd} (丢失帧数: {lost_frames})")
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last_valid_deviation = None
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last_send_time = current_time
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# -----------------------------------------------------------------
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# 绘制识别结果
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if largest_rect:
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x, y, w, h = largest_rect
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img.draw_rect(x, y, w, h, image.COLOR_GREEN, 2)
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cx, cy = target_center
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img.draw_circle(cx, cy, 3, image.COLOR_RED, -1)
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disp.show(img)
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time.sleep_ms(1) |