feat:新增管道预热；修改通信协议，添加预热数据格式。

20240529RGBtest3/config.py

@@ -0,0 +1,12 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2024/6/17 下午3:36
+# @Author  : TG
+# @File    : config.py
+# @Software: PyCharm
+
+
+
+class Config:
+    #文件相关参数
+    #预热参数
+    n_rows, n_cols, n_channels = 256, 256, 3

20240529RGBtest3/main.py

@@ -89,50 +89,56 @@ def main(is_debug=False):
     _ = detector.predict(np.ones((30, 30, 224), dtype=np.uint16))
     _, _, _, _, _ =dp.analyze_tomato(cv2.imread(r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\tomato_img\bad\71.bmp'))
     _, _, _, _, _ = dp.analyze_passion_fruit(cv2.imread(r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\passion_fruit_img\38.bmp'))
-    print('初始化完成')
+    # print('初始化完成')
 
     rgb_receive_name = r'\\.\pipe\rgb_receive'
     rgb_send_name = r'\\.\pipe\rgb_send'
     spec_receive_name = r'\\.\pipe\spec_receive'
     pipe = Pipe(rgb_receive_name, rgb_send_name, spec_receive_name)
     rgb_receive, rgb_send, spec_receive = pipe.create_pipes(rgb_receive_name, rgb_send_name, spec_receive_name)
+    # 预热循环，只处理cmd为'YR'的数据
+    while True:
+        data = pipe.receive_rgb_data(rgb_receive)
+        cmd, _ = pipe.parse_img(data)
+        if cmd != 'YR':
+            break  # 当接收到的不是预热命令时，结束预热循环
     while True:
 
         start_time = time.time()
         images = []
         cmd = None
         for _ in range(5):
-            start_time1 = time.time()
+            # start_time1 = time.time()
             data = pipe.receive_rgb_data(rgb_receive)
-            end_time10 = time.time()
+            # end_time10 = time.time()
-            print(f'接收一份数据时间：{end_time10 - start_time1}秒')
+            # print(f'接收一份数据时间：{end_time10 - start_time1}秒')
-            start_time11 = time.time()
+            # start_time11 = time.time()
             cmd, img = pipe.parse_img(data)
-            end_time1 = time.time()
+            # end_time1 = time.time()
-            print(f'处理一份数据时间：{end_time1 - start_time11}秒')
+            # print(f'处理一份数据时间：{end_time1 - start_time11}秒')
-            print(f'接收1张图时间：{end_time1 - start_time1}秒')
+            # print(f'接收1张图时间：{end_time1 - start_time1}秒')
             # print(cmd, img.shape)
             # #打印img的数据类型
             # print(img.dtype)
             images.append(img)
             # print(len(images))
-        if cmd not in ['TO', 'PF']:
+        if cmd not in ['TO', 'PF', 'YR']:
             logging.error(f'错误指令，指令为{cmd}')
             continue
         spec = None
         if cmd == 'PF':
-            start_time2 = time.time()
+            # start_time2 = time.time()
             spec_data = pipe.receive_spec_data(spec_receive)
             _, spec = pipe.parse_spec(spec_data)
-            end_time2 = time.time()
+            # end_time2 = time.time()
-            print(f'接收光谱数据时间：{end_time2 - start_time2}秒')
+            # print(f'接收光谱数据时间：{end_time2 - start_time2}秒')
             # print(spec.shape)
-        start_time3 = time.time()
+        # start_time3 = time.time()
         response = process_data(cmd, images, spec, dp, pipe, detector)
-        end_time3 = time.time()
+        # end_time3 = time.time()
-        print(f'处理时间：{end_time3 - start_time3}秒')
+        # print(f'处理时间：{end_time3 - start_time3}秒')
         end_time = time.time()
-        print(f'全流程时间：{end_time - start_time}秒')
+        # print(f'全流程时间：{end_time - start_time}秒')
         if response:
             logging.info(f'处理成功，响应为: {response}')
         else:

20240529RGBtest3/utils.py

@@ -109,13 +109,15 @@ class Pipe:
         :return: 指令类型和内容
         """
         try:
-            assert len(data) > 2
+            assert len(data) > 1
         except AssertionError:
-            logging.error('指令转换失败，长度不足3')
+            logging.error('指令转换失败，长度不足2')
             return '', None
         cmd, data = data[:2], data[2:]
         cmd = cmd.decode('ascii').strip().upper()
-
+        # 如果收到的是预热指令'YR'，直接返回命令和None，不处理图像数据
+        if cmd == 'YR':
+            return cmd, None
         n_rows, n_cols, img = data[:2], data[2:4], data[4:]
         try:
             n_rows, n_cols = [int.from_bytes(x, byteorder='big') for x in [n_rows, n_cols]]
@@ -126,7 +128,7 @@ class Pipe:
             assert n_rows * n_cols * 3 == len(img)
             # 因为是float32类型 所以长度要乘12 ，如果是uint8则乘3
         except AssertionError:
-            logging.error('图像指令IM转换失败，数据长度错误')
+            logging.error('图像指令转换失败，数据长度错误')
             return '', None
         img = np.frombuffer(img, dtype=np.uint8).reshape(n_rows, n_cols, -1)
         return cmd, img

20240529RGBtest3/xs/image_preprocessing20240615.py

@@ -211,7 +211,7 @@ def extract_max_connected_area(image_path, lower_hsv, upper_hsv):
 
 def main():
     parser = argparse.ArgumentParser(description='Process some integers.')
-    parser.add_argument('--dir_path', type=str, default=r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\broken',
+    parser.add_argument('--dir_path', type=str, default=r'D:\project\supermachine--tomato-passion_fruit\20240419RGBtest2\data',
                         help='the directory path of images')
     parser.add_argument('--threshold_s_l', type=int, default=180,
                         help='the threshold for s_l')
@@ -227,8 +227,9 @@ def main():
             otsu_thresholded = otsu_threshold(s_l)
             img_fore = bitwise_and_rgb_with_binary(cv2.imread(img_path), otsu_thresholded)
             img_fore_defect = extract_g_r(img_fore)
+            cv2.imshow('img_fore_defect1', img_fore_defect)
             img_fore_defect = threshold_segmentation(img_fore_defect, args.threshold_r_b)
-            # cv2.imshow('img_fore_defect', img_fore_defect)
+            cv2.imshow('img_fore_defect2', img_fore_defect)
             thresholded_s_l = threshold_segmentation(s_l, args.threshold_s_l)
             new_bin_img = largest_connected_component(thresholded_s_l)
             # zhongggggg = cv2.bitwise_or(new_bin_img, cv2.imread('defect_mask.bmp', cv2.IMREAD_GRAYSCALE))
@@ -239,11 +240,23 @@ def main():
             cv2.imshow('defect', defect)
             edge, mask = draw_tomato_edge(cv2.imread(img_path), new_bin_img)
             org_defect = bitwise_and_rgb_with_binary(edge, new_bin_img)
+
+
             fore = bitwise_and_rgb_with_binary(cv2.imread(img_path), mask)
+
+
             fore_g_r_t = threshold_segmentation(extract_g_r(fore), 20)
+            #统计白色像素点个数
+            print(np.sum(fore_g_r_t == 255))
+            print(np.sum(mask == 255))
+            print(np.sum(fore_g_r_t == 255)/np.sum(mask == 255))
+
             fore_g_r_t_ture = bitwise_and_rgb_with_binary(cv2.imread(img_path), fore_g_r_t)
-            cv2.imwrite('defect_big.bmp', fore_g_r_t_ture)
+            # cv2.imwrite('defect_big.bmp', fore_g_r_t_ture)
+            org_defect_new = bitwise_and_rgb_with_binary(edge, new_bin_img)
             res = cv2.bitwise_or(new_bin_img, fore_g_r_t)
+            nogreen = bitwise_and_rgb_with_binary(edge, res)
+            cv2.imshow('nogreen', nogreen)
             white = find_reflection(img_path)
 
             # SVM预测
@@ -260,13 +273,12 @@ def main():
             cv2.imshow('fore', fore)
             cv2.imshow('fore_g_r_t', fore_g_r_t)
             cv2.imshow('mask', mask)
-            print('mask', mask.shape)
-            print('filled', filled_img.shape)
-            print('largest', new_bin_img.shape)
-            print('rp', org_defect.shape)
             cv2.imshow('res', res)
 
-
+            # lower_hsv = np.array([19, 108, 15])
+            # upper_hsv = np.array([118, 198, 134])
+            # max_connected_area = extract_max_connected_area(img_path, lower_hsv, upper_hsv)
+            # cv2.imshow('Max Connected Area', max_connected_area)
 
             # 显示原始图像
             original_img = cv2.imread(img_path)

20240529RGBtest3/通信协议(20240612).md

@@ -18,6 +18,7 @@
 
 | 指令1 | 指令2 |   指令含义   |
 | :---: | :---: | :----------: |
+|   Y   |   R   |   预热数据   |
 |   T   |   O   |   番茄数据   |
 |   P   |   F   |  百香果数据  |
 |   R   |   E   | 返回结果数据 |
@@ -25,6 +26,10 @@
 
 ## **数据**
 
+**预热数据包：‘Y’‘R’**，不包含数据字段，仅有**长度字段+指令字段**
+
+
+
 **RGB图像数据包：'指令1''指令2 '**，`数据1`~`数据i`包含了图像的行数rows(高度)、列数cols(宽度)、以及图像的RGB数据，组合方式为**高度+宽度+RGB数据**
 $$
 i-4=rows \times cols \times 3
@@ -49,8 +54,6 @@ $$
 
 
 
-
-
 **返回结果数据包：'R''E'**，`数据1`~`数据i`包含了糖度值Brix、颜色占比color、直径long、预估重量weight、缺陷个数num、缺陷面积area、结果图像的行数rows(高度)、列数cols(宽度)、以及结果图像的RGB数据，组合方式为**糖度值+颜色占比+直径+预估重量+缺陷个数+缺陷面积+高度+宽度+RGB数据**
 $$
 i-16=rows \times cols \times 3
@@ -61,4 +64,6 @@ $$
 | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | --- | --- | --- | --- |
 | Brix[15:8] | Brix[7:0] | color[7:0] | long[15:8] | long[7:0] | weight[7:0] | num[15:8] | num[7:0] | area[31:24] | area[23:16] | area[15:8] | area[7:0] | rows[15:8] | rows[7:0] | cols[15:8] | cols[7:0] | | ... | |
 
+
+
 **返回空果数据包：‘K’‘O’**，不包含数据字段，仅有**长度字段+指令字段**