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feat:新增管道预热；修改通信协议，添加预热数据格式。
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TG
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20240529RGBtest3/config.py
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20240529RGBtest3/config.py
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# -*- coding: utf-8 -*-
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# @Time : 2024/6/17 下午3:36
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# @Author : TG
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# @File : config.py
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# @Software: PyCharm
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class Config:
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#文件相关参数
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#预热参数
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n_rows, n_cols, n_channels = 256, 256, 3
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@ -89,50 +89,56 @@ def main(is_debug=False):
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_ = detector.predict(np.ones((30, 30, 224), dtype=np.uint16))
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_, _, _, _, _ =dp.analyze_tomato(cv2.imread(r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\tomato_img\bad\71.bmp'))
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_, _, _, _, _ = dp.analyze_passion_fruit(cv2.imread(r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\passion_fruit_img\38.bmp'))
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print('初始化完成')
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# print('初始化完成')
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rgb_receive_name = r'\\.\pipe\rgb_receive'
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rgb_send_name = r'\\.\pipe\rgb_send'
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spec_receive_name = r'\\.\pipe\spec_receive'
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pipe = Pipe(rgb_receive_name, rgb_send_name, spec_receive_name)
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rgb_receive, rgb_send, spec_receive = pipe.create_pipes(rgb_receive_name, rgb_send_name, spec_receive_name)
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# 预热循环，只处理cmd为'YR'的数据
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while True:
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data = pipe.receive_rgb_data(rgb_receive)
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cmd, _ = pipe.parse_img(data)
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if cmd != 'YR':
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break # 当接收到的不是预热命令时，结束预热循环
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while True:
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start_time = time.time()
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images = []
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cmd = None
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for _ in range(5):
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start_time1 = time.time()
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# start_time1 = time.time()
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data = pipe.receive_rgb_data(rgb_receive)
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end_time10 = time.time()
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print(f'接收一份数据时间：{end_time10 - start_time1}秒')
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start_time11 = time.time()
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# end_time10 = time.time()
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# print(f'接收一份数据时间：{end_time10 - start_time1}秒')
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# start_time11 = time.time()
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cmd, img = pipe.parse_img(data)
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end_time1 = time.time()
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print(f'处理一份数据时间：{end_time1 - start_time11}秒')
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print(f'接收1张图时间：{end_time1 - start_time1}秒')
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# end_time1 = time.time()
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# print(f'处理一份数据时间：{end_time1 - start_time11}秒')
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# print(f'接收1张图时间：{end_time1 - start_time1}秒')
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# print(cmd, img.shape)
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# #打印img的数据类型
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# print(img.dtype)
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images.append(img)
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# print(len(images))
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if cmd not in ['TO', 'PF']:
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if cmd not in ['TO', 'PF', 'YR']:
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logging.error(f'错误指令，指令为{cmd}')
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continue
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spec = None
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if cmd == 'PF':
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start_time2 = time.time()
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# start_time2 = time.time()
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spec_data = pipe.receive_spec_data(spec_receive)
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_, spec = pipe.parse_spec(spec_data)
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end_time2 = time.time()
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print(f'接收光谱数据时间：{end_time2 - start_time2}秒')
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# end_time2 = time.time()
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# print(f'接收光谱数据时间：{end_time2 - start_time2}秒')
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# print(spec.shape)
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start_time3 = time.time()
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# start_time3 = time.time()
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response = process_data(cmd, images, spec, dp, pipe, detector)
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end_time3 = time.time()
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print(f'处理时间：{end_time3 - start_time3}秒')
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# end_time3 = time.time()
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# print(f'处理时间：{end_time3 - start_time3}秒')
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end_time = time.time()
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print(f'全流程时间：{end_time - start_time}秒')
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# print(f'全流程时间：{end_time - start_time}秒')
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if response:
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logging.info(f'处理成功，响应为: {response}')
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else:
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@ -109,13 +109,15 @@ class Pipe:
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:return: 指令类型和内容
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"""
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try:
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assert len(data) > 2
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assert len(data) > 1
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except AssertionError:
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logging.error('指令转换失败，长度不足3')
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logging.error('指令转换失败，长度不足2')
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return '', None
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cmd, data = data[:2], data[2:]
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cmd = cmd.decode('ascii').strip().upper()
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# 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
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if cmd == 'YR':
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return cmd, None
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n_rows, n_cols, img = data[:2], data[2:4], data[4:]
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try:
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n_rows, n_cols = [int.from_bytes(x, byteorder='big') for x in [n_rows, n_cols]]
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@ -126,7 +128,7 @@ class Pipe:
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assert n_rows * n_cols * 3 == len(img)
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# 因为是float32类型 所以长度要乘12 ，如果是uint8则乘3
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except AssertionError:
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logging.error('图像指令IM转换失败，数据长度错误')
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logging.error('图像指令转换失败，数据长度错误')
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return '', None
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img = np.frombuffer(img, dtype=np.uint8).reshape(n_rows, n_cols, -1)
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return cmd, img
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@ -211,7 +211,7 @@ def extract_max_connected_area(image_path, lower_hsv, upper_hsv):
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def main():
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parser = argparse.ArgumentParser(description='Process some integers.')
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parser.add_argument('--dir_path', type=str, default=r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\broken',
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parser.add_argument('--dir_path', type=str, default=r'D:\project\supermachine--tomato-passion_fruit\20240419RGBtest2\data',
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help='the directory path of images')
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parser.add_argument('--threshold_s_l', type=int, default=180,
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help='the threshold for s_l')
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@ -227,8 +227,9 @@ def main():
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otsu_thresholded = otsu_threshold(s_l)
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img_fore = bitwise_and_rgb_with_binary(cv2.imread(img_path), otsu_thresholded)
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img_fore_defect = extract_g_r(img_fore)
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cv2.imshow('img_fore_defect1', img_fore_defect)
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img_fore_defect = threshold_segmentation(img_fore_defect, args.threshold_r_b)
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# cv2.imshow('img_fore_defect', img_fore_defect)
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cv2.imshow('img_fore_defect2', img_fore_defect)
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thresholded_s_l = threshold_segmentation(s_l, args.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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@ -239,11 +240,23 @@ def main():
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cv2.imshow('defect', defect)
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edge, mask = draw_tomato_edge(cv2.imread(img_path), new_bin_img)
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org_defect = bitwise_and_rgb_with_binary(edge, new_bin_img)
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fore = bitwise_and_rgb_with_binary(cv2.imread(img_path), mask)
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fore_g_r_t = threshold_segmentation(extract_g_r(fore), 20)
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#统计白色像素点个数
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print(np.sum(fore_g_r_t == 255))
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print(np.sum(mask == 255))
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print(np.sum(fore_g_r_t == 255)/np.sum(mask == 255))
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fore_g_r_t_ture = bitwise_and_rgb_with_binary(cv2.imread(img_path), fore_g_r_t)
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cv2.imwrite('defect_big.bmp', fore_g_r_t_ture)
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# cv2.imwrite('defect_big.bmp', fore_g_r_t_ture)
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org_defect_new = bitwise_and_rgb_with_binary(edge, new_bin_img)
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res = cv2.bitwise_or(new_bin_img, fore_g_r_t)
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nogreen = bitwise_and_rgb_with_binary(edge, res)
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cv2.imshow('nogreen', nogreen)
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white = find_reflection(img_path)
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# SVM预测
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@ -260,13 +273,12 @@ def main():
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cv2.imshow('fore', fore)
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cv2.imshow('fore_g_r_t', fore_g_r_t)
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cv2.imshow('mask', mask)
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print('mask', mask.shape)
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print('filled', filled_img.shape)
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print('largest', new_bin_img.shape)
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print('rp', org_defect.shape)
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cv2.imshow('res', res)
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# lower_hsv = np.array([19, 108, 15])
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# upper_hsv = np.array([118, 198, 134])
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# max_connected_area = extract_max_connected_area(img_path, lower_hsv, upper_hsv)
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# cv2.imshow('Max Connected Area', max_connected_area)
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# 显示原始图像
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original_img = cv2.imread(img_path)
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@ -18,6 +18,7 @@
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| 指令1 | 指令2 | 指令含义 |
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| :---: | :---: | :----------: |
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| Y | R | 预热数据 |
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| T | O | 番茄数据 |
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| P | F | 百香果数据 |
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| R | E | 返回结果数据 |
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@ -25,6 +26,10 @@
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## **数据**
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**预热数据包：‘Y’‘R’**，不包含数据字段，仅有**长度字段+指令字段**
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**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**
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$$
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i-4=rows \times cols \times 3
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@ -49,8 +54,6 @@ $$
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**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**
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$$
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i-16=rows \times cols \times 3
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@ -61,4 +64,6 @@ $$
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| :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | --- | --- | --- | --- |
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| 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] | | ... | |
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**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**
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