Merge branch 'master' of https://github.com/FEIJINTI/tobacco_color

config.py

@@ -20,7 +20,7 @@ class Config:
     # 光谱模型参数
     blk_size = 4
     pixel_model_path = r"./models/dt.p"
-    blk_model_path = r"./models/rf_4x4_c22_20_sen8_8.model"
+    blk_model_path = r"./models/rf_4x4_c22_20_sen8_9.model"
     spec_size_threshold = 3
 
     # rgb模型参数

efficient_ui.py

@@ -0,0 +1,51 @@
+import cv2
+import numpy as np
+
+import transmit
+from config import Config
+
+from utils import ImgQueue as Queue
+
+
+class EfficientUI(object):
+    def __init__(self):
+        # 相关参数
+        img_fifo_path = "/tmp/dkimg.fifo"
+        mask_fifo_path = "/tmp/dkmask.fifo"
+        rgb_fifo_path = "/tmp/dkrgb.fifo"
+        # 创建队列用于链接各个线程
+        rgb_img_queue, spec_img_queue = Queue(), Queue()
+        detector_queue, save_queue, self.visual_queue = Queue(), Queue(), Queue()
+        mask_queue = Queue()
+        # 两个接收者,接收光谱和rgb图像
+        spec_len = Config.nRows * Config.nCols * Config.nBands * 4  # float型变量, 4个字节
+        rgb_len = Config.nRgbRows * Config.nRgbCols * Config.nRgbBands * 1  # int型变量
+        spec_receiver = transmit.FifoReceiver(fifo_path=img_fifo_path, output=spec_img_queue, read_max_num=spec_len)
+        rgb_receiver = transmit.FifoReceiver(fifo_path=rgb_fifo_path, output=rgb_img_queue, read_max_num=rgb_len)
+        # 指令执行与图像流向控制
+        subscribers = {'detector': detector_queue, 'visualize': self.visual_queue, 'save': save_queue}
+        cmd_img_controller = transmit.CmdImgSplitMidware(rgb_queue=rgb_img_queue, spec_queue=spec_img_queue,
+                                                         subscribers=subscribers)
+        # 探测器
+        detector = transmit.ThreadDetector(input_queue=detector_queue, output_queue=mask_queue)
+        # 发送
+        sender = transmit.FifoSender(output_fifo_path=mask_fifo_path, source=mask_queue)
+        # 启动所有线程
+        spec_receiver.start(post_process_func=transmit.BeforeAfterMethods.spec_data_post_process, name='spce_thread')
+        rgb_receiver.start(post_process_func=transmit.BeforeAfterMethods.rgb_data_post_process, name='rgb_thread')
+        cmd_img_controller.start(name='control_thread')
+        detector.start(name='detector_thread')
+        sender.start(pre_process=transmit.BeforeAfterMethods.mask_preprocess, name='sender_thread')
+
+    def start(self):
+        # 启动图形化
+        while True:
+            cv2.imshow('image_show', mat=np.ones((256, 1024)))
+            key_code = cv2.waitKey(30)
+            if key_code == ord("s"):
+                pass
+
+
+if __name__ == '__main__':
+    app = EfficientUI()
+    app.start()

main.py

@@ -1,16 +1,18 @@
 import os
 import time
+from queue import Queue
+
 import numpy as np
-import scipy.io
+from matplotlib import pyplot as plt
+
+import models
+import transmit
 
 from config import Config
-from models import RgbDetector, SpecDetector, ManualTree, AnonymousColorDetector
+from models import RgbDetector, SpecDetector
 import cv2
 
 
-SAVE_IMG, SAVE_NUM = False, 30
-
-
 def main():
     spec_detector = SpecDetector(blk_model_path=Config.blk_model_path, pixel_model_path=Config.pixel_model_path)
     rgb_detector = RgbDetector(tobacco_model_path=Config.rgb_tobacco_model_path,
@@ -23,8 +25,6 @@ def main():
         os.mkfifo(mask_fifo_path, 0o777)
     if not os.access(rgb_fifo_path, os.F_OK):
         os.mkfifo(rgb_fifo_path, 0o777)
-    if SAVE_IMG:
-        img_list = []
     while True:
         fd_img = os.open(img_fifo_path, os.O_RDONLY)
         fd_rgb = os.open(rgb_fifo_path, os.O_RDONLY)
@@ -55,19 +55,12 @@ def main():
         img_data = np.frombuffer(data_total, dtype=np.float32).reshape((Config.nRows, Config.nBands, -1)) \
             .transpose(0, 2, 1)
         rgb_data = np.frombuffer(rgb_data_total, dtype=np.uint8).reshape((Config.nRgbRows, Config.nRgbCols, -1))
-        if SAVE_IMG:
-            SAVE_NUM -= 1
-            img_list.append((rgb_data, img_data))
-            if SAVE_NUM <= 0:
-                break
         # 光谱识别
         mask = spec_detector.predict(img_data)
         # rgb识别
         mask_rgb = rgb_detector.predict(rgb_data)
-        
         # 结果合并
         mask_result = (mask | mask_rgb).astype(np.uint8)
-        
         # mask_result = mask_rgb.astype(np.uint8)
         mask_result = mask_result.repeat(Config.blk_size, axis=0).repeat(Config.blk_size, axis=1).astype(np.uint8)
         t2 = time.time()
@@ -79,100 +72,37 @@ def main():
         os.close(fd_mask)
         t3 = time.time()
         print(f'total time is:{t3 - t1}')
-    for i, img in enumerate(img_list):
-        print(f"writing img {i}...")
-        cv2.imwrite(f"./{i}.png", img[0][..., ::-1])
-        np.save(f'./{i}.npy', img[1])
-        i += 1
 
 
-
+def read_c_captures(buffer_path, no_mask=True, nrows=256, ncols=1024, selected_bands=None):
-def save_main():
+    if os.path.isdir(buffer_path):
-    threshold = Config.spec_size_threshold
+        buffer_names = [buffer_name for buffer_name in os.listdir(buffer_path) if buffer_name.endswith('.raw')]
-    rgb_threshold = Config.rgb_size_threshold
+    else:
-    manual_tree = ManualTree(blk_model_path=Config.blk_model_path, pixel_model_path=Config.pixel_model_path)
+        buffer_names = [buffer_path, ]
-    tobacco_detector = AnonymousColorDetector(file_path=Config.rgb_tobacco_model_path)
+    for buffer_name in buffer_names:
-    background_detector = AnonymousColorDetector(file_path=Config.rgb_background_model_path)
+        with open(os.path.join(buffer_path, buffer_name), 'rb') as f:
-    total_len = Config.nRows * Config.nCols * Config.nBands * 4  # float型变量, 4个字节
+            data = f.read()
-    total_rgb = Config.nRgbRows * Config.nRgbCols * Config.nRgbBands * 1  # int型变量
+        img = np.frombuffer(data, dtype=np.float32).reshape((nrows, -1, ncols)) \
-    if not os.access(img_fifo_path, os.F_OK):
+            .transpose(0, 2, 1)
-        os.mkfifo(img_fifo_path, 0o777)
+        if selected_bands is not None:
-    if not os.access(mask_fifo_path, os.F_OK):
+            img = img[..., selected_bands]
-        os.mkfifo(mask_fifo_path, 0o777)
+        if img.shape[0] == 1:
-    if not os.access(rgb_fifo_path, os.F_OK):
+            img = img[0, ...]
-        os.mkfifo(rgb_fifo_path, 0o777)
+        if not no_mask:
-    img_list = []
+            mask_name = buffer_name.replace('buf', 'mask').replace('.raw', '')
-    idx = 0
+            with open(os.path.join(buffer_path, mask_name), 'rb') as f:
-    while idx <= 30:
+                data = f.read()
-        idx += 1
+            mask = np.frombuffer(data, dtype=np.uint8).reshape((nrows, ncols, -1))
-        fd_img = os.open(img_fifo_path, os.O_RDONLY)
-        fd_rgb = os.open(rgb_fifo_path, os.O_RDONLY)
-        data = os.read(fd_img, total_len)
-
-        # 读取(开启一个管道)
-        if len(data) < 3:
-            threshold = int(float(data))
-            print("[INFO] Get threshold: ", threshold)
-            continue
         else:
-            data_total = data
+            mask_name = "no mask"
-            rgb_data = os.read(fd_rgb, total_rgb)
+            mask = np.zeros_like(img)
-            if len(rgb_data) < 3:
+        # mask = cv2.resize(mask, (1024, 256))
-                rgb_threshold = int(float(rgb_data))
+        fig, axs = plt.subplots(2, 1)
-                print(rgb_threshold)
+        axs[0].matshow(img)
-                continue
+        axs[0].set_title(buffer_name)
-            else:
+        axs[1].imshow(mask)
-                rgb_data_total = rgb_data
+        axs[1].set_title(mask_name)
-        os.close(fd_img)
+        plt.show()
-        os.close(fd_rgb)
-
-        # 识别
-        t1 = time.time()
-        img_data = np.frombuffer(data_total, dtype=np.float32).reshape((Config.nRows, Config.nBands, -1)). \
-            transpose(0, 2, 1)
-        rgb_data = np.frombuffer(rgb_data_total, dtype=np.uint8).reshape((Config.nRgbRows, Config.nRgbCols, -1))
-        img_list.append((rgb_data.copy(), img_data.copy()))
-
-        pixel_predict_result = manual_tree.pixel_predict_ml_dilation(data=img_data, iteration=1)
-        blk_predict_result = manual_tree.blk_predict(data=img_data)
-        rgb_data = tobacco_detector.pretreatment(rgb_data)
-        # print(rgb_data.shape)
-        rgb_predict_result = 1 - (background_detector.predict(rgb_data, threshold_low=Config.threshold_low,
-                                                              threshold_high=Config.threshold_high) |
-                                  tobacco_detector.swell(tobacco_detector.predict(rgb_data,
-                                                                                  threshold_low=Config.threshold_low,
-                                                                                  threshold_high=Config.threshold_high)))
-        mask_rgb = rgb_predict_result.reshape(Config.nRows, Config.nCols // Config.blk_size, Config.blk_size) \
-            .sum(axis=2).reshape(Config.nRows // 4, Config.blk_size, Config.nCols // Config.blk_size) \
-            .sum(axis=1)
-        mask_rgb[mask_rgb <= rgb_threshold] = 0
-        mask_rgb[mask_rgb > rgb_threshold] = 1
-        mask = (pixel_predict_result & blk_predict_result).astype(np.uint8)
-        mask = mask.reshape(Config.nRows, Config.nCols // Config.blk_size, Config.blk_size) \
-            .sum(axis=2).reshape(Config.nRows // 4, Config.blk_size, Config.nCols // Config.blk_size) \
-            .sum(axis=1)
-        mask[mask <= threshold] = 0
-        mask[mask > threshold] = 1
-        mask_result = (mask | mask_rgb).astype(np.uint8)
-       # mask_result = mask_rgb
-        mask_result = mask_result.repeat(Config.blk_size, axis=0).repeat(Config.blk_size, axis=1).astype(np.uint8)
-        t2 = time.time()
-        print(f'rgb len = {len(rgb_data)}')
-
-        # 写出
-        fd_mask = os.open(mask_fifo_path, os.O_WRONLY)
-        os.write(fd_mask, mask_result.tobytes())
-        os.close(fd_mask)
-        t3 = time.time()
-        print(f'total time is:{t3 - t1}')
-    i = 0
-    print("Stop Serving")
-    for img in img_list:
-        print(f"writing img {i}...")
-        cv2.imwrite(f"./{i}.png", img[0][..., ::-1])
-        np.save(f'./{i}.npy', img[1])
-        i += 1
-    print("save success")
 
 
 if __name__ == '__main__':
@@ -183,3 +113,5 @@ if __name__ == '__main__':
 
     # 主函数
     main()
+    # read_c_captures('/home/lzy/2022.7.15/tobacco_v1_0/', no_mask=True, nrows=256, ncols=1024,
+    #                 selected_bands=[380, 300, 200])

models.py

@@ -5,10 +5,12 @@
 # @Software:PyCharm、
 import datetime
 import pickle
+from queue import Queue
 
 import cv2
 import numpy as np
 import scipy.io
+import threading
 from scipy.ndimage import binary_dilation
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.metrics import classification_report
@@ -17,6 +19,7 @@ from sklearn.model_selection import train_test_split
 from config import Config
 from utils import lab_scatter, read_labeled_img, size_threshold
 
+
 deploy = True
 if not deploy:
     print("Training env")
@@ -415,7 +418,7 @@ class SpecDetector(Detector):
             # 烟梗mask中将背景赋值为0,将烟梗赋值为2
             yellow_things[yellow_things] = tobacco
             yellow_things = yellow_things + 0
-            # yellow_things = binary_dilation(yellow_things, iterations=iteration)
+            yellow_things = binary_dilation(yellow_things, iterations=iteration)
             yellow_things = yellow_things + 0
             yellow_things[yellow_things == 1] = 2
 

transmit.py

@@ -1,11 +1,18 @@
 import os
 import threading
-from queue import Queue
+import time
+
+from utils import ImgQueue as Queue
 import numpy as np
 from config import Config
+from models import SpecDetector, RgbDetector
+import typing
+import logging
+logging.basicConfig(format='%(asctime)s %(levelname)s %(name)s %(message)s',
+                    level=logging.DEBUG)
 
 
-class Receiver(object):
+class Transmitter(object):
     def __init__(self):
         self.output = None
 
@@ -45,17 +52,49 @@ class Receiver(object):
         raise NotImplementedError
 
 
-class FifoReceiver(Receiver):
+class BeforeAfterMethods:
-    def __init__(self, fifo_path: str, output: Queue, read_max_num: int):
+    @classmethod
+    def mask_preprocess(cls, mask: np.ndarray):
+        logging.info(f"Send mask with size {mask.shape}")
+        return mask.tobytes()
+
+    @classmethod
+    def spec_data_post_process(cls, data):
+        if len(data) < 3:
+            threshold = int(float(data))
+            logging.info(f"Get Spec threshold: {threshold}")
+            return threshold
+        else:
+            spec_img = np.frombuffer(data, dtype=np.float32).\
+                reshape((Config.nRows, Config.nBands, -1)).transpose(0, 2, 1)
+            logging.info(f"Get SPEC image with size {spec_img.shape}")
+            return spec_img
+
+    @classmethod
+    def rgb_data_post_process(cls, data):
+        if len(data) < 3:
+            threshold = int(float(data))
+            logging.info(f"Get RGB threshold: {threshold}")
+            return threshold
+        else:
+            rgb_img = np.frombuffer(data, dtype=np.uint8).reshape((Config.nRgbRows, Config.nRgbCols, -1))
+            logging.info(f"Get RGB img with size {rgb_img.shape}")
+            return rgb_img
+
+
+class FifoReceiver(Transmitter):
+    def __init__(self, fifo_path: str, output: Queue, read_max_num: int, msg_queue=None):
         super().__init__()
         self._input_fifo_path = None
         self._output_queue = None
+        self._msg_queue = msg_queue
         self._max_len = read_max_num
 
         self.set_source(fifo_path)
         self.set_output(output)
         self._need_stop = threading.Event()
         self._need_stop.clear()
+        self._running_thread = None
 
     def set_source(self, fifo_path: str):
         if not os.access(fifo_path, os.F_OK):
@@ -66,9 +105,9 @@ class FifoReceiver(Receiver):
         self._output_queue = output
 
     def start(self, post_process_func=None, name='fifo_receiver'):
-        x = threading.Thread(target=self._receive_thread_func,
+        self._running_thread = threading.Thread(target=self._receive_thread_func,
-                             name=name, args=(post_process_func, ))
+                                                name=name, args=(post_process_func, ))
-        x.start()
+        self._running_thread.start()
 
     def stop(self):
         self._need_stop.set()
@@ -85,27 +124,170 @@ class FifoReceiver(Receiver):
             data = os.read(input_fifo, self._max_len)
             if post_process_func is not None:
                 data = post_process_func(data)
-            self._output_queue.put(data)
+            self._output_queue.safe_put(data)
             os.close(input_fifo)
         self._need_stop.clear()
 
-    @staticmethod
-    def spec_data_post_process(data):
-        if len(data) < 3:
-            threshold = int(float(data))
-            print("[INFO] Get Spec threshold: ", threshold)
-            return threshold
-        else:
-            spec_img = np.frombuffer(data, dtype=np.float32).\
-                reshape((Config.nRows, Config.nBands, -1)).transpose(0, 2, 1)
-            return spec_img
 
-    @staticmethod
+class FifoSender(Transmitter):
-    def rgb_data_post_process(data):
+    def __init__(self, output_fifo_path: str, source: Queue):
-        if len(data) < 3:
+        super().__init__()
-            threshold = int(float(data))
+        self._input_source = None
-            print("[INFO] Get RGB threshold: ", threshold)
+        self._output_fifo_path = None
-            return threshold
+        self.set_source(source)
-        else:
+        self.set_output(output_fifo_path)
-            rgb_img = np.frombuffer(data, dtype=np.uint8).reshape((Config.nRgbRows, Config.nRgbCols, -1))
+        self._need_stop = threading.Event()
-            return rgb_img
+        self._need_stop.clear()
+        self._running_thread = None
+
+    def set_source(self, source: Queue):
+        self._input_source = source
+
+    def set_output(self, output_fifo_path: str):
+        if not os.access(output_fifo_path, os.F_OK):
+            os.mkfifo(output_fifo_path, 0o777)
+        self._output_fifo_path = output_fifo_path
+
+    def start(self, pre_process=None, name='fifo_receiver'):
+        self._running_thread = threading.Thread(target=self._send_thread_func, name=name,
+                                                args=(pre_process, ))
+        self._running_thread.start()
+
+    def stop(self):
+        self._need_stop.set()
+
+    def _send_thread_func(self, pre_process=None):
+        """
+        接收线程
+
+        :param pre_process:
+        :return:
+        """
+        while not self._need_stop.is_set():
+            if self._input_source.empty():
+                continue
+            data = self._input_source.get()
+            if pre_process is not None:
+                data = pre_process(data)
+            output_fifo = os.open(self._output_fifo_path, os.O_WRONLY)
+            os.write(output_fifo, data)
+            os.close(output_fifo)
+        self._need_stop.clear()
+
+    def __del__(self):
+        self.stop()
+        if self._running_thread is not None:
+            self._running_thread.join()
+
+
+class CmdImgSplitMidware(Transmitter):
+    """
+    用于控制命令和图像的中间件
+    """
+    def __init__(self, subscribers: typing.Dict[str, Queue], rgb_queue: Queue, spec_queue: Queue):
+        super().__init__()
+        self._rgb_queue = None
+        self._spec_queue = None
+        self._subscribers = None
+        self._server_thread = None
+        self.set_source(rgb_queue, spec_queue)
+        self.set_output(subscribers)
+        self.thread_stop = threading.Event()
+
+    def set_source(self, rgb_queue: Queue, spec_queue: Queue):
+        self._rgb_queue = rgb_queue
+        self._spec_queue = spec_queue
+
+    def set_output(self, output: typing.Dict[str, Queue]):
+        self._subscribers = output
+
+    def start(self, name='CMD_thread'):
+        self._server_thread = threading.Thread(target=self._cmd_control_service, name=name)
+        self._server_thread.start()
+
+    def stop(self):
+        self.thread_stop.set()
+
+    def _cmd_control_service(self):
+        while not self.thread_stop.is_set():
+            # 判断是否有数据，如果没有数据那么就等下次吧，如果有数据来，必须保证同时
+            if self._rgb_queue.empty() or self._spec_queue.empty():
+                continue
+            rgb_data = self._rgb_queue.get()
+            spec_data = self._spec_queue.get()
+            if isinstance(rgb_data, int) and isinstance(spec_data, int):
+                # 看是不是命令需要执行如果是命令，就执行
+                Config.rgb_size_threshold = rgb_data
+                Config.spec_size_threshold = spec_data
+                continue
+            elif isinstance(spec_data, np.ndarray) and isinstance(rgb_data, np.ndarray):
+                # 如果是图片，交给预测的人
+                for name, subscriber in self._subscribers.items():
+                    item = (spec_data, rgb_data)
+                    subscriber.safe_put(item)
+            else:
+                # 否则程序出现毁灭性问题，立刻崩
+                raise Exception("两个相机传回的数据没有对上")
+        self.thread_stop.clear()
+
+
+class ImageSaver(Transmitter):
+    """
+    进行图片存储的中间件
+    """
+    def set_source(self, *args, **kwargs):
+        pass
+
+    def start(self, *args, **kwargs):
+        pass
+
+    def stop(self, *args, **kwargs):
+        pass
+
+
+class ThreadDetector(Transmitter):
+    def __init__(self, input_queue: Queue, output_queue: Queue):
+        super().__init__()
+        self._input_queue, self._output_queue = input_queue, output_queue
+        self._spec_detector = SpecDetector(blk_model_path=Config.blk_model_path,
+                                           pixel_model_path=Config.pixel_model_path)
+        self._rgb_detector = RgbDetector(tobacco_model_path=Config.rgb_tobacco_model_path,
+                                         background_model_path=Config.rgb_background_model_path)
+        self._predict_thread = None
+        self._thread_exit = threading.Event()
+
+    def set_source(self, img_queue: Queue):
+        self._input_queue = img_queue
+
+    def stop(self, *args, **kwargs):
+        self._thread_exit.set()
+
+    def start(self, name='predict_thread'):
+        self._predict_thread = threading.Thread(target=self._predict_server, name=name)
+        self._predict_thread.start()
+
+    def predict(self, spec: np.ndarray, rgb: np.ndarray):
+        logging.info(f'Detector get image with shape {spec.shape} and {rgb.shape}')
+        t1 = time.time()
+        mask = self._spec_detector.predict(spec)
+        t2 = time.time()
+        logging.info(f'Detector finish spec predict within {(t2 - t1) * 1000:.2f}ms')
+        # rgb识别
+        mask_rgb = self._rgb_detector.predict(rgb)
+        t3 = time.time()
+        logging.info(f'Detector finish rgb predict within {(t3 - t2) * 1000:.2f}ms')
+        # 结果合并
+        mask_result = (mask | mask_rgb).astype(np.uint8)
+        mask_result = mask_result.repeat(Config.blk_size, axis=0).repeat(Config.blk_size, axis=1).astype(np.uint8)
+        t4 = time.time()
+        logging.info(f'Detector finish merge within {(t4 - t3) * 1000: .2f}ms')
+        logging.info(f'Detector finish predict within {(time.time() -t1)*1000:.2f}ms')
+        return mask_result
+
+    def _predict_server(self):
+        while not self._thread_exit.is_set():
+            if not self._input_queue.empty():
+                spec, rgb = self._input_queue.get()
+                mask = self.predict(spec, rgb)
+                self._output_queue.safe_put(mask)
+        self._thread_exit.clear()

utils.py

@@ -5,6 +5,7 @@
 # @Software:PyCharm
 import glob
 import os
+from queue import Queue
 
 import cv2
 import numpy as np
@@ -26,6 +27,34 @@ class MergeDict(dict):
         return self
 
 
+class ImgQueue(Queue):
+    """
+        A custom queue subclass that provides a :meth:`clear` method.
+    """
+
+    def clear(self):
+        """
+        Clears all items from the queue.
+        """
+
+        with self.mutex:
+            unfinished = self.unfinished_tasks - len(self.queue)
+            if unfinished <= 0:
+                if unfinished < 0:
+                    raise ValueError('task_done() called too many times')
+                self.all_tasks_done.notify_all()
+            self.unfinished_tasks = unfinished
+            self.queue.clear()
+            self.not_full.notify_all()
+
+    def safe_put(self, item):
+        if self.full():
+            _ = self.get()
+            return False
+        self.put(item)
+        return True
+
+
 def read_labeled_img(dataset_dir: str, color_dict: dict, ext='.bmp', is_ps_color_space=True) -> dict:
     """
     根据dataset_dir下的文件创建数据集