[ext] 喷阀结果合并

添加了喷阀结果合并功能，并在utils.py的main当中添加了测试，测试已经通过。

config.py

@@ -32,6 +32,7 @@ class Config:
 
     # mask parameter
     target_size = (1024, 1024)  # (Width, Height) of mask
+    valve_merge_size = 2  # 每两个喷阀当中有任意一个出现杂质则认为都是杂质
 
     # save part
     offset_vertical = 0

main.py

@@ -3,6 +3,7 @@ import cv2
 import time
 import numpy as np
 
+import utils
 from config import Config
 from models import RgbDetector, SpecDetector
 
@@ -66,8 +67,10 @@ def main(only_spec=False, only_color=False):
             mask_spec = spec_detector.predict(img_data)
             mask_rgb = rgb_detector.predict(rgb_data)
 
-        # control the size of the output masks
-        masks = [cv2.resize(mask.astype(np.uint8), Config.target_size) for mask in [mask_spec, mask_rgb]]
+        # 进行喷阀的合并
+        masks = [utils.valve_merge(mask, merge_size=Config.valve_merge_size) for mask in [mask_spec, mask_rgb]]
+        # control the size of the output masks, 在resize前，图像的宽度是和喷阀对应的
+        masks = [cv2.resize(mask.astype(np.uint8), Config.target_size) for mask in masks]
         # 写出
         output_fifos = [mask_fifo_path, rgb_mask_fifo_path]
         for fifo, mask in zip(output_fifos, masks):

utils.py

@@ -150,6 +150,16 @@ def size_threshold(img, blk_size, threshold, last_end: np.ndarray = None) -> np.
     return mask
 
 
+def valve_merge(img: np.ndarray, merge_size: int = 2) -> np.ndarray:
+    assert img.shape[1] % merge_size == 0  # 列数必须能够被整除
+    img_shape = (img.shape[1], img.shape[0])
+    img = img.reshape((img.shape[0], img.shape[1]//merge_size, merge_size))
+    img = np.sum(img, axis=2)
+    img[img > 0] = 1
+    img = cv2.resize(img.astype(np.uint8), dsize=img_shape)
+    return img
+
+
 def read_envi_ascii(file_name, save_xy=False, hdr_file_name=None):
     """
     Read envi ascii file. Use ENVI ROI Tool -> File -> output ROIs to ASCII...
@@ -210,3 +220,7 @@ if __name__ == '__main__':
                   (255, 0, 255): "chengsebangbangtang", (0, 255, 255): "lvdianxian"}
     dataset = read_labeled_img("data/dataset", color_dict=color_dict, is_ps_color_space=False)
     lab_scatter(dataset, class_max_num=20000, is_3d=False, is_ps_color_space=False)
+    # a = np.array([[1, 1, 0, 0, 1, 0, 0, 1], [0, 0, 1, 0, 0, 1, 1, 1]]).astype(np.uint8)
+    # a.repeat(3, axis=0)
+    # b = valve_merge(a, 2)
+    # print(b)