mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 14:54:07 +00:00
217 lines
8.1 KiB
Python
217 lines
8.1 KiB
Python
# -*- coding: utf-8 -*-
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# @Time : 2024/4/12 15:04
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# @Author : TG
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# @File : main.py
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# @Software: PyCharm
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import socket
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import sys
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import numpy as np
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import cv2
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import root_dir
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import time
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import os
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from root_dir import ROOT_DIR
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from utils import PreSocket, receive_sock, parse_protocol, ack_sock, done_sock, DualSock, simple_sock, test_sock
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import logging
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from utils import threshold_segmentation, largest_connected_component, draw_tomato_edge, bitwise_and_rgb_with_binary, extract_s_l, get_tomato_dimensions, get_defect_info
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from collections import deque
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import time
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def process_cmd(cmd: str, img: any, connected_sock: socket.socket) -> tuple:
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"""
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处理指令
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:param cmd: 指令类型
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:param data: 指令内容
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:param connected_sock: socket
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:param detector: 模型
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:return: 是否处理成功
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"""
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start_time = time.time()
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if cmd == 'IM':
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# image = cv2.imdecode(np.frombuffer(img, dtype=np.uint8), cv2.IMREAD_COLOR)
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# image = img
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threshold_s_l = 180
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# threshold_r_b = 15
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s_l = extract_s_l(img)
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# otsu_thresholded = ImageProcessor.otsu_threshold(s_l)
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# img_fore = ImageProcessor.bitwise_and_rgb_with_binary(img, otsu_thresholded)
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# img_fore_defect = ImageProcessor.extract_g_r(img_fore)
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# img_fore_defect = ImageProcessor.threshold_segmentation(img_fore_defect, threshold_r_b)
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# cv2.imshow('img_fore_defect', img_fore_defect)
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thresholded_s_l = threshold_segmentation(s_l, threshold_s_l)
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new_bin_img = largest_connected_component(thresholded_s_l)
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# zhongggggg = cv2.bitwise_or(new_bin_img, cv2.imread('defect_mask.bmp', cv2.IMREAD_GRAYSCALE))
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# cv2.imshow('zhongggggg', zhongggggg)
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# new_otsu_bin_img = ImageProcessor.largest_connected_component(otsu_thresholded)
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# filled_img, defect = ImageProcessor.fill_holes(new_bin_img)
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# defect = ImageProcessor.bitwise_and_rgb_with_binary(cv2.imread(img), defect)
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# cv2.imshow('defect', defect)
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edge, mask = draw_tomato_edge(img, new_bin_img)
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org_defect = bitwise_and_rgb_with_binary(edge, new_bin_img)
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# fore = ImageProcessor.bitwise_and_rgb_with_binary(cv2.imread(img), mask)
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# fore_g_r_t = ImageProcessor.threshold_segmentation(ImageProcessor.extract_g_r(fore), 20)
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# fore_g_r_t_ture = ImageProcessor.bitwise_and_rgb_with_binary(cv2.imread(img), fore_g_r_t)
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# cv2.imwrite('defect_big.bmp', fore_g_r_t_ture)
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# res = cv2.bitwise_or(new_bin_img, fore_g_r_t)
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# white = ImageProcessor.find_reflection(img)
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# cv2.imwrite('new_bin_img.bmp', new_bin_img)
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long_axis, short_axis = get_tomato_dimensions(mask)
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number_defects, total_pixels = get_defect_info(new_bin_img)
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rp = org_defect
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rp = cv2.cvtColor(rp, cv2.COLOR_BGR2RGB)
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# cv2.imwrite('rp1.bmp', rp)
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# elif cmd == 'TR':
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# detector = WoodClass(w=4096, h=1200, n=8000, p1=0.8, debug_mode=False)
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# model_name = None
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# if "$" in data:
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# data, model_name = data.split("$", 1)
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# model_name = model_name + ".p"
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# settings.data_path = data
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# settings.model_path = ROOT_DIR / 'models' / detector.fit_pictures(data_path=settings.data_path, file_name=model_name)
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# response = simple_sock(connected_sock, cmd_type=cmd, result=result)
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# elif cmd == 'MD':
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# settings.model_path = data
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# detector.load(path=settings.model_path)
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# response = simple_sock(connected_sock, cmd_type=cmd, result=result)
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# elif cmd == 'KM':
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# x_data, y_data, labels, img_names = detector.get_luminance_data(data, plot_2d=True)
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# result = detector.data_adjustments(x_data, y_data, labels, img_names)
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# result = ','.join([str(x) for x in result])
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# response = simple_sock(connected_sock, cmd_type=cmd, result=result)
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else:
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logging.error(f'错误指令，指令为{cmd}')
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response = False
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end_time = time.time()
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elapsed_time = end_time - start_time
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print(f'处理时间：{elapsed_time}秒')
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return long_axis, short_axis, number_defects, total_pixels, rp
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def main(is_debug=False):
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file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'report.log'))
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file_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING)
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console_handler = logging.StreamHandler(sys.stdout)
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console_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING)
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logging.basicConfig(format='%(asctime)s %(filename)s[line:%(lineno)d] - %(levelname)s - %(message)s',
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handlers=[file_handler, console_handler],
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level=logging.DEBUG)
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dual_sock = DualSock(connect_ip='127.0.0.1')
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while not dual_sock.status:
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dual_sock.reconnect()
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while True:
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long_axis_list = []
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short_axis_list = []
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defect_num_sum = 0
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total_defect_area_sum = 0
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rp = None
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for i in range(5):
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start_time = time.time()
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pack, next_pack = receive_sock(dual_sock)
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if pack == b"":
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time.sleep(2)
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dual_sock.reconnect()
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continue
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cmd, img = parse_protocol(pack)
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print(cmd)
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print(img.shape)
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end_time = time.time()
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elapsed_time = end_time - start_time
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print(f'接收时间：{elapsed_time}秒')
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long_axis, short_axis, number_defects, total_pixels, rp = process_cmd(cmd=cmd, img=img, connected_sock=dual_sock)
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# print(long_axis, short_axis, number_defects, type(total_pixels), rp.shape)
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if i <= 2:
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long_axis_list.append(long_axis)
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short_axis_list.append(short_axis)
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if i == 1:
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rp_result = rp
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defect_num_sum += number_defects
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total_defect_area_sum += total_pixels
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long_axis = round(sum(long_axis_list) / 3)
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short_axis = round(sum(short_axis_list) / 3)
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# print(type(long_axis), type(short_axis), type(defect_num_sum), type(total_defect_area_sum), type(rp_result))
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response = test_sock(dual_sock, cmd_type=cmd, long_axis=long_axis, short_axis=short_axis,
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defect_num=defect_num_sum, total_defect_area=total_defect_area_sum, rp=rp_result)
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print(long_axis, short_axis, defect_num_sum, total_defect_area_sum, rp_result.shape)
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# while True:
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# result_buffer = []
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# for _ in range(5):
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# pack, next_pack = receive_sock(dual_sock) # 接收数据，如果没有数据则阻塞，如果返回的是空字符串则表示出现错误
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# if pack == b"": # 无数据表示出现错误
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# time.sleep(5)
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# dual_sock.reconnect()
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# break
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#
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# cmd, data = parse_protocol(pack)
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# print(cmd)
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# print(data)
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#
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# result = process_cmd(cmd=cmd, data=data, connected_sock=dual_sock, detector=detector, settings=settings)
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# result_buffer.append(result) # 将处理结果添加到缓冲区
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#
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# # 在这里进行对5次结果的处理，可以进行合并、比较等操作
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# final_result = combine_results(result_buffer)
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#
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# # 发送最终结果
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# response = simple_sock(dual_sock, cmd_type=cmd, result=final_result)
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# print(final_result)
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# result_buffer = []
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if __name__ == '__main__':
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# 2个端口
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# 接受端口21122
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# 发送端口21123
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# 接收到图片 n_rows * n_bands * n_cols, float32
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# 发送图片 n_rows * n_cols, uint8
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main(is_debug=False)
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# test(r"D:\build-tobacco-Desktop_Qt_5_9_0_MSVC2015_64bit-Release\calibrated15.raw")
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# main()
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# debug_main()
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# test_run(all_data_dir=r'D:\数据')
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# with open(r'D:\数据\虫子\valid2.raw', 'rb') as f:
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# data = np.frombuffer(f.read(), dtype=np.float32).reshape(600, 29, 1024).transpose(0, 2, 1)
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# plt.matshow(data[:, :, 10])
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# plt.show()
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# detector = SpecDetector('model_spec/model_29.p')
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# result = detector.predict(data)
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#
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# plt.matshow(result)
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# plt.show()
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# result = result.reshape((600, 1024)) |