NanjingForestryUniversity/supermachine-tobacco
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fix：增加了光谱伪彩色图lab

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feijinti 2023-09-24 12:59:41 +08:00
parent c4191be192
commit 192bb8db99
2 changed files with 14 additions and 1 deletions
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3
config.py
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@ -26,6 +26,9 @@ class Config:
# blk_model_path = r"/home/dt/tobacco-color/weights/rf_4x4_c22_20_sen8_9.model" # 机器上部署的路径 # blk_model_path = r"/home/dt/tobacco-color/weights/rf_4x4_c22_20_sen8_9.model" # 机器上部署的路径
spec_size_threshold = 3 spec_size_threshold = 3
s_threshold_a = 125 # s_a的最高允许值
s_threshold_b = 125 # s_b的最高允许值
# rgb模型参数 # rgb模型参数
rgb_tobacco_model_path = r"weights/tobacco_dt_2022-08-27_14-43.model" # 开发时的路径 rgb_tobacco_model_path = r"weights/tobacco_dt_2022-08-27_14-43.model" # 开发时的路径
# rgb_tobacco_model_path = r"/home/dt/tobacco-color/weights/tobacco_dt_2022-08-27_14-43.model" # 机器上部署的路径 # rgb_tobacco_model_path = r"/home/dt/tobacco-color/weights/tobacco_dt_2022-08-27_14-43.model" # 机器上部署的路径

12
models/__init__.py
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@ -337,7 +337,7 @@ class RgbDetector(Detector):
lab_b = cv2.cvtColor(rgb_data, cv2.COLOR_RGB2LAB)[..., 2] < Config.threshold_b lab_b = cv2.cvtColor(rgb_data, cv2.COLOR_RGB2LAB)[..., 2] < Config.threshold_b
lab_predict_result = lab_a | lab_b lab_predict_result = lab_a | lab_b
mask_lab = size_threshold(lab_predict_result, Config.blk_size, Config.lab_size_threshold) mask_lab = size_threshold(lab_predict_result, Config.blk_size, Config.lab_size_threshold)
mask_rgb = mask_rgb | mask_lab mask_rgb = mask_rgb.astype(np.uint8) | mask_lab.astype(np.uint8)
# # 测试时间 # # 测试时间
# end = time.time() # end = time.time()
# print("lab time: ", end - start) # print("lab time: ", end - start)
@ -370,9 +370,19 @@ class SpecDetector(Detector):
pixel_predict_result = self.pixel_predict_ml_dilation(data=img_data, iteration=1) pixel_predict_result = self.pixel_predict_ml_dilation(data=img_data, iteration=1)
blk_predict_result = self.blk_predict(data=img_data) blk_predict_result = self.blk_predict(data=img_data)
mask = (pixel_predict_result & blk_predict_result).astype(np.uint8) mask = (pixel_predict_result & blk_predict_result).astype(np.uint8)
spec_cv = np.clip(img_data[..., [21, 3, 0]], a_min=0, a_max=1) * 255
spec_cv = spec_cv.astype(np.uint8)
# spec转lab 提取a通道，识别绿色杂质
lab_a = cv2.cvtColor(spec_cv, cv2.COLOR_RGB2LAB)[..., 1] < Config.s_threshold_a
# spec转lab 提取b通道，识别蓝色杂质
lab_b = cv2.cvtColor(spec_cv, cv2.COLOR_RGB2LAB)[..., 2] < Config.s_threshold_b
lab_predict_result = lab_a | lab_b
mask_lab = size_threshold(lab_predict_result, Config.blk_size, Config.lab_size_threshold)
if save_part: if save_part:
self.spare_part = mask[-(Config.blk_size//2):, :] self.spare_part = mask[-(Config.blk_size//2):, :]
mask = size_threshold(mask, Config.blk_size, Config.spec_size_threshold, self.spare_part) mask = size_threshold(mask, Config.blk_size, Config.spec_size_threshold, self.spare_part)
mask = mask.astype(np.uint8) | mask_lab.astype(np.uint8)
return mask return mask
def save(self, *args, **kwargs): def save(self, *args, **kwargs):