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https://github.com/NanjingForestryUniversity/supermachine-tobacco.git
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c4191be192
添加了lab颜色空间识别绿色杂质、蓝色杂质，添加a、b的阈值为127，尺寸限制改为6。 优化了lab色彩空间内绘制3维数据分布情况。 喷阀横向膨胀尺寸改为5。 添加test_rgb测试单张图片
48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
import os
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import numpy as np
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from config import Config
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from models import Detector, AnonymousColorDetector, RgbDetector
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import cv2
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# 测试单张图片使用RGB进行预测的效果
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# # 测试时间
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# import time
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# start_time = time.time()
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# 读取图片
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file_path = r"E:\Tobacco\data\testImgs\Image_2022_0726_1413_46_400-001165.bmp"
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img = cv2.imread(file_path)[..., ::-1]
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print("img.shape:", img.shape)
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# 初始化和加载色彩模型
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print('Initializing color model...')
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rgb_detector = RgbDetector(tobacco_model_path=r'../weights/tobacco_dt_2022-08-27_14-43.model',
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background_model_path=r"../weights/background_dt_2022-08-22_22-15.model",
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ai_path='../weights/best0827.pt')
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_ = rgb_detector.predict(np.ones((Config.nRgbRows, Config.nRgbCols, Config.nRgbBands), dtype=np.uint8) * 40)
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print('Color model loaded.')
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# 预测单张图片
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print('Predicting...')
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mask_rgb = rgb_detector.predict(img).astype(np.uint8)
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# # 测试时间
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# end_time = time.time()
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# print("time cost:", end_time - start_time)
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# 使用matplotlib展示两个图片的对比
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import matplotlib.pyplot as plt
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# 切换matplotlib的后端为qt，否则会报错
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plt.switch_backend('qt5agg')
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fig, ax = plt.subplots(1, 2)
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ax[0].imshow(img)
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ax[1].matshow(mask_rgb)
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plt.show()
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