Merge remote-tracking branch 'origin/master'

# Conflicts:
#	config.py

config.py

@@ -26,6 +26,6 @@ class Config:
     # rgb模型参数
     rgb_tobacco_model_path = r"models/tobacco_dt_2022-07-26_15-57.model"
     rgb_background_model_path = r"models/background_dt_2022-07-27_08-11.model"
-    threshold_low, threshold_high = 5, 230
+    threshold_low, threshold_high = 10, 230
     threshold_s = 175
     rgb_size_threshold = 4

main.py

@@ -8,6 +8,9 @@ from models import RgbDetector, SpecDetector, ManualTree, AnonymousColorDetector
 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,
@@ -20,39 +23,52 @@ 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)
+
+        # spec data read
         data = os.read(fd_img, total_len)
-        # 读取(开启一个管道)
         if len(data) < 3:
             threshold = int(float(data))
             Config.spec_size_threshold = threshold
-            print("[INFO] Get threshold: ", threshold)
+            print("[INFO] Get spec threshold: ", threshold)
-            continue
         else:
             data_total = data
-            rgb_data = os.read(fd_rgb, total_rgb)
-            if len(rgb_data) < 3:
-                rgb_threshold = int(float(rgb_data))
-                Config.rgb_size_threshold = rgb_threshold
-                print(rgb_threshold)
-                continue
-            else:
-                rgb_data_total = rgb_data
         os.close(fd_img)
+        
+        # rgb data read
+        rgb_data = os.read(fd_rgb, total_rgb)
+        if len(rgb_data) < 3:
+            rgb_threshold = int(float(rgb_data))
+            Config.rgb_size_threshold = rgb_threshold
+            print("[INFO] Get rgb threshold", rgb_threshold)
+            continue
+        else:
+            rgb_data_total = rgb_data
         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))
+        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()
         print(f'rgb len = {len(rgb_data)}')
@@ -63,6 +79,12 @@ 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 save_main():
@@ -131,8 +153,8 @@ def save_main():
             .sum(axis=1)
         mask[mask <= threshold] = 0
         mask[mask > threshold] = 1
-        # mask_result = (mask | mask_rgb).astype(np.uint8)
+        mask_result = (mask | mask_rgb).astype(np.uint8)
-        mask_result = mask_rgb
+       # 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)}')

models.py

@@ -17,7 +17,7 @@ from sklearn.model_selection import train_test_split
 from config import Config
 from utils import lab_scatter, read_labeled_img, size_threshold
 
-deploy = False
+deploy = True
 if not deploy:
     print("Training env")
     from tqdm import tqdm
@@ -415,7 +415,7 @@ 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