mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 14:54:07 +00:00
579 lines
19 KiB
Python
579 lines
19 KiB
Python
# -*- coding: utf-8 -*-
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# @Time : 2024/4/20 18:24
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# @Author : TG
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# @File : utils.py
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# @Software: PyCharm
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import socket
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import time
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import logging
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import numpy as np
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import shutil
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import cv2
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import os
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from scipy.ndimage.measurements import label, find_objects
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import win32pipe
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import win32file
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import io
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from PIL import Image
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import select
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import msvcrt
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def receive_rgb_data(pipe):
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try:
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# 读取图片数据
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len_img = win32file.ReadFile(pipe, 4, None)
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data_size = int.from_bytes(len_img[1], byteorder='big')
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result, img_data = win32file.ReadFile(pipe, data_size, None)
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return img_data
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except Exception as e:
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print(f"数据接收失败，错误原因: {e}")
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return None
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def receive_spec_data(pipe):
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try:
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# 读取图片数据长度
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len_spec = win32file.ReadFile(pipe, 4, None)
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if len_spec is None:
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# 未能读取到数据长度,返回"0"
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return "0"
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data_size = int.from_bytes(len_spec[1], byteorder='big')
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if data_size == 0:
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# 接收到空数据,返回"0"
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return "0"
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# 读取图片数据
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result, spec_data = win32file.ReadFile(pipe, data_size, None)
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return spec_data
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except Exception as e:
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print(f"数据接收失败，错误原因: {e}")
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return '0'
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# def receive_spec_data(pipe):
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# try:
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# # 读取图片数据
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# len_spec = win32file.ReadFile(pipe, 4, None)
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# data_size = int.from_bytes(len_spec[1], byteorder='big')
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# result, spec_data = win32file.ReadFile(pipe, data_size, None)
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# return spec_data
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# except Exception as e:
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# print(f"数据接收失败，错误原因: {e}")
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# return None
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# def create_pipes(pipe_receive_name, pipe_send_name):
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# while True:
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# try:
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# # 打开或创建命名管道
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# pipe_receive = win32pipe.CreateNamedPipe(
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# pipe_receive_name,
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# win32pipe.PIPE_ACCESS_INBOUND,
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# win32pipe.PIPE_TYPE_BYTE | win32pipe.PIPE_WAIT,
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# 1, 80000000, 80000000, 0, None
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# )
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# pipe_send = win32pipe.CreateNamedPipe(
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# pipe_send_name,
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# win32pipe.PIPE_ACCESS_OUTBOUND, # 修改为输出模式
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# win32pipe.PIPE_TYPE_BYTE | win32pipe.PIPE_WAIT,
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# 1, 80000000, 80000000, 0, None
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# )
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#
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# # 等待发送端连接
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# win32pipe.ConnectNamedPipe(pipe_receive, None)
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# # 等待发送端连接
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# win32pipe.ConnectNamedPipe(pipe_send, None)
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# print("Sender connected.")
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# print("receive connected.")
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# return pipe_receive, pipe_send
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#
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# except Exception as e:
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# print(f"Error occurred while creating pipes: {e}")
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# print("Waiting for 5 seconds before retrying...")
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# time.sleep(5)
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def create_pipes(rgb_receive_name, rgb_send_name, spec_receive_name):
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while True:
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try:
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# 打开或创建命名管道
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rgb_receive = win32pipe.CreateNamedPipe(
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rgb_receive_name,
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win32pipe.PIPE_ACCESS_INBOUND,
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win32pipe.PIPE_TYPE_BYTE | win32pipe.PIPE_WAIT,
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1, 80000000, 80000000, 0, None
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)
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rgb_send = win32pipe.CreateNamedPipe(
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rgb_send_name,
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win32pipe.PIPE_ACCESS_OUTBOUND, # 修改为输出模式
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win32pipe.PIPE_TYPE_BYTE | win32pipe.PIPE_WAIT,
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1, 80000000, 80000000, 0, None
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)
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spec_receive = win32pipe.CreateNamedPipe(
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spec_receive_name,
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win32pipe.PIPE_ACCESS_INBOUND,
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win32pipe.PIPE_TYPE_BYTE | win32pipe.PIPE_WAIT,
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1, 200000000, 200000000, 0, None
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)
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print("pipe管道创建成功，等待连接...")
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# 等待发送端连接
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win32pipe.ConnectNamedPipe(rgb_receive, None)
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print("rgb_receive connected.")
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# 等待发送端连接
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win32pipe.ConnectNamedPipe(rgb_send, None)
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print("rgb_send connected.")
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win32pipe.ConnectNamedPipe(rgb_receive, None)
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print("spec_receive connected.")
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return rgb_receive, rgb_send, spec_receive
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except Exception as e:
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print(f"管道创建连接失败，失败原因: {e}")
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print("等待5秒后重试...")
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time.sleep(5)
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# def send_data(pipe_send, long_axis, short_axis, defect_num, total_defect_area, rp):
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#
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# # start_time = time.time()
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#
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# # width = rp.shape[0]
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# # height = rp.shape[1]
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# # print(width, height)
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# img_bytes = rp.tobytes()
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# # length = len(img_bytes) + 18
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# # print(length)
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# # length = length.to_bytes(4, byteorder='big')
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# # width = width.to_bytes(2, byteorder='big')
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# # height = height.to_bytes(2, byteorder='big')
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# length = (len(img_bytes) + 10).to_bytes(4, byteorder='big')
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# long_axis = long_axis.to_bytes(2, byteorder='big')
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# short_axis = short_axis.to_bytes(2, byteorder='big')
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# defect_num = defect_num.to_bytes(2, byteorder='big')
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# total_defect_area = int(total_defect_area).to_bytes(4, byteorder='big')
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# # cmd_type = 'RIM'
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# # result = result.encode('ascii')
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# # send_message = b'\xaa' + length + (' ' + cmd_type).upper().encode('ascii') + long_axis + short_axis + defect_num + total_defect_area + width + height + img_bytes + b'\xff\xff\xbb'
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# send_message = length + long_axis + short_axis + defect_num + total_defect_area + img_bytes
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# # print(long_axis)
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# # print(short_axis)
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# # print(defect_num)
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# # print(total_defect_area)
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# # print(width)
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# # print(height)
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#
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# try:
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# win32file.WriteFile(pipe_send, send_message)
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# print('发送成功')
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# # print(send_message)
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# except Exception as e:
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# logging.error(f'发送完成指令失败，错误类型：{e}')
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# return False
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#
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# # end_time = time.time()
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# # print(f'发送时间：{end_time - start_time}秒')
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#
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# return True
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def send_data(pipe_send, long_axis, short_axis, defect_num, total_defect_area, rp):
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# start_time = time.time()
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#
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rp1 = Image.fromarray(rp.astype(np.uint8))
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# cv2.imwrite('rp1.bmp', rp1)
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# 将 Image 对象保存到 BytesIO 流中
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img_bytes = io.BytesIO()
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rp1.save(img_bytes, format='BMP')
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img_bytes = img_bytes.getvalue()
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# width = rp.shape[0]
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# height = rp.shape[1]
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# print(width, height)
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# img_bytes = rp.tobytes()
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# length = len(img_bytes) + 18
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# print(length)
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# length = length.to_bytes(4, byteorder='big')
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# width = width.to_bytes(2, byteorder='big')
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# height = height.to_bytes(2, byteorder='big')
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print(f'原始长度:', len(rp.tobytes()))
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print(f'发送长度:', len(img_bytes))
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long_axis = long_axis.to_bytes(2, byteorder='big')
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short_axis = short_axis.to_bytes(2, byteorder='big')
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defect_num = defect_num.to_bytes(2, byteorder='big')
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total_defect_area = int(total_defect_area).to_bytes(4, byteorder='big')
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length = (len(img_bytes) + 4).to_bytes(4, byteorder='big')
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# cmd_type = 'RIM'
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# result = result.encode('ascii')
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# send_message = b'\xaa' + length + (' ' + cmd_type).upper().encode('ascii') + long_axis + short_axis + defect_num + total_defect_area + width + height + img_bytes + b'\xff\xff\xbb'
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# send_message = long_axis + short_axis + defect_num + total_defect_area + img_bytes
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send_message = long_axis + short_axis + defect_num + total_defect_area + length + img_bytes
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# print(long_axis)
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# print(short_axis)
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# print(defect_num)
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# print(total_defect_area)
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# print(width)
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# print(height)
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try:
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win32file.WriteFile(pipe_send, send_message)
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time.sleep(0.01)
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print('发送成功')
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# print(len(send_message))
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except Exception as e:
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logging.error(f'发送完成指令失败，错误类型：{e}')
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return False
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# end_time = time.time()
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# print(f'发送时间：{end_time - start_time}秒')
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return True
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def mkdir_if_not_exist(dir_name, is_delete=False):
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"""
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创建文件夹
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:param dir_name: 文件夹
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:param is_delete: 是否删除
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:return: 是否成功
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"""
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try:
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if is_delete:
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if os.path.exists(dir_name):
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shutil.rmtree(dir_name)
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print('[Info] 文件夹 "%s" 存在, 删除文件夹.' % dir_name)
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if not os.path.exists(dir_name):
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os.makedirs(dir_name)
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print('[Info] 文件夹 "%s" 不存在, 创建文件夹.' % dir_name)
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return True
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except Exception as e:
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print('[Exception] %s' % e)
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return False
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def create_file(file_name):
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"""
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创建文件
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:param file_name: 文件名
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:return: None
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"""
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if os.path.exists(file_name):
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print("文件存在：%s" % file_name)
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return False
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# os.remove(file_name) # 删除已有文件
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if not os.path.exists(file_name):
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print("文件不存在，创建文件：%s" % file_name)
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open(file_name, 'a').close()
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return True
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class Logger(object):
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def __init__(self, is_to_file=False, path=None):
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self.is_to_file = is_to_file
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if path is None:
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path = "tomato.log"
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self.path = path
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create_file(path)
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def log(self, content):
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if self.is_to_file:
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with open(self.path, "a") as f:
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print(time.strftime("[%Y-%m-%d_%H-%M-%S]:"), file=f)
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print(content, file=f)
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else:
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print(content)
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#提取西红柿，使用S+L的图像
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def extract_s_l(image):
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# image = cv2.imread(image_path)
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hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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lab = cv2.cvtColor(image, cv2.COLOR_BGR2Lab)
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s_channel = hsv[:,:,1]
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l_channel = lab[:,:,0]
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result = cv2.add(s_channel, l_channel)
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return result
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def find_reflection(image, threshold=190):
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# 读取图像
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# image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
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# 应用阈值分割
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_, reflection = cv2.threshold(image, threshold, 255, cv2.THRESH_BINARY)
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return reflection
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def otsu_threshold(image):
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# 将图像转换为灰度图像
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# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# 使用Otsu阈值分割
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_, binary = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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return binary
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# 提取花萼，使用G-R的图像
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def extract_g_r(image):
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# image = cv2.imread(image_path)
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g_channel = image[:,:,1]
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r_channel = image[:,:,2]
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result = cv2.subtract(cv2.multiply(g_channel, 1.5), r_channel)
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return result
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#提取西红柿，使用R-B的图像
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def extract_r_b(image):
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# image = cv2.imread(image_path)
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r_channel = image[:,:,2]
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b_channel = image[:,:,0]
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result = cv2.subtract(r_channel, b_channel)
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return result
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def extract_r_g(image):
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# image = cv2.imread(image_path)
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r_channel = image[:,:,2]
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g_channel = image[:,:,1]
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result = cv2.subtract(r_channel, g_channel)
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return result
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def threshold_segmentation(image, threshold, color=255):
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_, result = cv2.threshold(image, threshold, color, cv2.THRESH_BINARY)
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return result
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def bitwise_operation(image1, image2, operation='and'):
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if operation == 'and':
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result = cv2.bitwise_and(image1, image2)
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elif operation == 'or':
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result = cv2.bitwise_or(image1, image2)
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else:
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raise ValueError("operation must be 'and' or 'or'")
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return result
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def largest_connected_component(bin_img):
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# 使用connectedComponentsWithStats函数找到连通区域
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num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(bin_img, connectivity=8)
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# 如果只有背景标签,返回一个空的二值图像
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if num_labels <= 1:
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return np.zeros_like(bin_img)
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# 找到最大的连通区域（除了背景）
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largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA])
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# 创建一个新的二值图像,只显示最大的连通区域
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new_bin_img = np.zeros_like(bin_img)
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new_bin_img[labels == largest_label] = 255
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return new_bin_img
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def close_operation(bin_img, kernel_size=(5, 5)):
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size)
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closed_img = cv2.morphologyEx(bin_img, cv2.MORPH_CLOSE, kernel)
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return closed_img
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def open_operation(bin_img, kernel_size=(5, 5)):
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size)
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opened_img = cv2.morphologyEx(bin_img, cv2.MORPH_OPEN, kernel)
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return opened_img
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def draw_tomato_edge(original_img, bin_img):
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bin_img_processed = close_operation(bin_img, kernel_size=(15, 15))
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# cv2.imshow('Close Operation', bin_img_processed)
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# bin_img_processed = open_operation(bin_img_processed, kernel_size=(19, 19))
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# cv2.imshow('Open Operation', bin_img_processed)
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# 现在使用处理后的bin_img_processed查找轮廓
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contours, _ = cv2.findContours(bin_img_processed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 如果没有找到轮廓，直接返回原图
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if not contours:
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return original_img, np.zeros_like(bin_img) # 返回原图和全黑mask
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# 找到最大轮廓
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max_contour = max(contours, key=cv2.contourArea)
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# 多边形近似的精度调整
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epsilon = 0.0006 * cv2.arcLength(max_contour, True) # 可以调整这个值
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approx = cv2.approxPolyDP(max_contour, epsilon, True)
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# 绘制轮廓
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cv2.drawContours(original_img, [approx], -1, (0, 255, 0), 3)
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mask = np.zeros_like(bin_img)
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# 使用白色填充最大轮廓
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cv2.drawContours(mask, [max_contour], -1, (255), thickness=cv2.FILLED)
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return original_img, mask
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def draw_tomato_edge_convex_hull(original_img, bin_img):
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bin_img_blurred = cv2.GaussianBlur(bin_img, (5, 5), 0)
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contours, _ = cv2.findContours(bin_img_blurred, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if not contours:
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return original_img
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max_contour = max(contours, key=cv2.contourArea)
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hull = cv2.convexHull(max_contour)
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cv2.drawContours(original_img, [hull], -1, (0, 255, 0), 3)
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return original_img
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# 得到完整的西红柿二值图像，除了绿色花萼
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def fill_holes(bin_img):
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# 复制 bin_img 到 img_filled
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img_filled = bin_img.copy()
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# 获取图像的高度和宽度
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height, width = bin_img.shape
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# 创建一个掩码，比输入图像大两个像素点
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mask = np.zeros((height + 2, width + 2), np.uint8)
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# 使用 floodFill 函数填充黑色区域
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cv2.floodFill(img_filled, mask, (0, 0), 255)
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# 反转填充后的图像
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img_filled_d = cv2.bitwise_not(img_filled)
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# 使用 bitwise_or 操作合并原图像和填充后的图像
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img_filled = cv2.bitwise_or(bin_img, img_filled)
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# 裁剪 img_filled 和 img_filled_d 到与 bin_img 相同的大小
|
||
# img_filled = img_filled[:height, :width]
|
||
img_filled_d = img_filled_d[:height, :width]
|
||
|
||
return img_filled, img_filled_d
|
||
|
||
def bitwise_and_rgb_with_binary(rgb_img, bin_img):
|
||
# 将二值图像转换为三通道图像
|
||
bin_img_3channel = cv2.cvtColor(bin_img, cv2.COLOR_GRAY2BGR)
|
||
|
||
# 使用 bitwise_and 操作合并 RGB 图像和二值图像
|
||
result = cv2.bitwise_and(rgb_img, bin_img_3channel)
|
||
|
||
return result
|
||
|
||
|
||
def extract_max_connected_area(image, lower_hsv, upper_hsv):
|
||
# 读取图像
|
||
# image = cv2.imread(image_path)
|
||
|
||
# 将图像从BGR转换到HSV
|
||
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
|
||
|
||
# 使用阈值获取指定区域的二值图像
|
||
mask = cv2.inRange(hsv, lower_hsv, upper_hsv)
|
||
|
||
# 找到二值图像的连通区域
|
||
num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8)
|
||
|
||
# 找到最大的连通区域（除了背景）
|
||
largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA])
|
||
|
||
# 创建一个新的二值图像，只显示最大的连通区域
|
||
new_bin_img = np.zeros_like(mask)
|
||
new_bin_img[labels == largest_label] = 255
|
||
|
||
# 复制 new_bin_img 到 img_filled
|
||
img_filled = new_bin_img.copy()
|
||
|
||
# 获取图像的高度和宽度
|
||
height, width = new_bin_img.shape
|
||
|
||
# 创建一个掩码，比输入图像大两个像素点
|
||
mask = np.zeros((height + 2, width + 2), np.uint8)
|
||
|
||
# 使用 floodFill 函数填充黑色区域
|
||
cv2.floodFill(img_filled, mask, (0, 0), 255)
|
||
|
||
# 反转填充后的图像
|
||
img_filled_inv = cv2.bitwise_not(img_filled)
|
||
|
||
# 使用 bitwise_or 操作合并原图像和填充后的图像
|
||
img_filled = cv2.bitwise_or(new_bin_img, img_filled_inv)
|
||
|
||
return img_filled
|
||
def get_tomato_dimensions(edge_img):
|
||
"""
|
||
根据番茄边缘二值化轮廓图,计算番茄的长径、短径和长短径比值。
|
||
使用最小外接矩形和最小外接圆两种方法。
|
||
|
||
参数:
|
||
edge_img (numpy.ndarray): 番茄边缘二值化轮廓图,背景为黑色,番茄区域为白色。
|
||
|
||
返回:
|
||
tuple: (长径, 短径, 长短径比值)
|
||
"""
|
||
if edge_img is None or edge_img.any() == 0:
|
||
return (0, 0)
|
||
# 最小外接矩形
|
||
rect = cv2.minAreaRect(cv2.findContours(edge_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0][0])
|
||
major_axis, minor_axis = rect[1]
|
||
# aspect_ratio = max(major_axis, minor_axis) / min(major_axis, minor_axis)
|
||
|
||
# # 最小外接圆
|
||
# (x, y), radius = cv2.minEnclosingCircle(
|
||
# cv2.findContours(edge_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0][0])
|
||
# diameter = 2 * radius
|
||
# aspect_ratio_circle = 1.0
|
||
|
||
return (max(major_axis, minor_axis), min(major_axis, minor_axis))
|
||
|
||
def get_defect_info(defect_img):
|
||
"""
|
||
根据番茄区域缺陷二值化轮廓图,计算缺陷区域的个数和总面积。
|
||
|
||
参数:
|
||
defect_img (numpy.ndarray): 番茄区域缺陷二值化轮廓图,背景为黑色,番茄区域为白色,缺陷区域为黑色连通域。
|
||
|
||
返回:
|
||
tuple: (缺陷区域个数, 缺陷区域像素面积，缺陷像素总面积)
|
||
"""
|
||
# 检查输入是否为空
|
||
if defect_img is None or defect_img.any() == 0:
|
||
return (0, 0)
|
||
|
||
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(defect_img, connectivity=4)
|
||
max_area = max(stats[i, cv2.CC_STAT_AREA] for i in range(1, nb_components))
|
||
areas = []
|
||
for i in range(1, nb_components):
|
||
area = stats[i, cv2.CC_STAT_AREA]
|
||
if area != max_area:
|
||
areas.append(area)
|
||
number_defects = len(areas)
|
||
total_pixels = sum(areas)
|
||
return number_defects, total_pixels
|
||
|
||
|
||
|
||
|
||
|
||
|
||
def connected_components_analysis(binary_image):
|
||
"""
|
||
从二值化图像计算黑色连通域个数和各个黑色连通域像素面积及黑色像素总面积。
|
||
|
||
参数:
|
||
binary_image (numpy.ndarray): 二值化图像, 其中 0 表示白色, 1 表示黑色。
|
||
|
||
返回:
|
||
num_components (int): 黑色连通域的个数。
|
||
component_areas (list): 每个黑色连通域的像素面积。
|
||
total_black_area (int): 黑色像素的总面积。
|
||
"""
|
||
# 标记连通域
|
||
labeled_image, num_components = label(binary_image)
|
||
|
||
# 获取每个连通域的像素位置
|
||
slices = find_objects(labeled_image)
|
||
|
||
# 计算每个连通域的像素面积
|
||
component_areas = []
|
||
for slice_obj in slices:
|
||
component_area = np.sum(binary_image[slice_obj])
|
||
component_areas.append(component_area)
|
||
|
||
# 计算黑色像素的总面积
|
||
total_black_area = np.sum(binary_image)
|
||
|
||
return num_components, component_areas, total_black_area |