87 lines
3.3 KiB
Python
87 lines
3.3 KiB
Python
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import matplotlib
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import matplotlib.pyplot as plt
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matplotlib.use('TkAgg')
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from plantcv import plantcv as pcv
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import numpy as np
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import cv2
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def process_images_background(full_bil_path, mask, outdir='None', debug="None"):
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# 初始化参数
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class options:
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def __init__(self):
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self.image = full_bil_path # 输入的光谱图像路径
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self.debug = debug # 设置调试模式：'plot' 或 'print'
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self.writeimg = False # 是否保存图片
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self.result = outdir # 输出结果文件路径
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self.outdir = outdir # 输出文件夹路径
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args = options()
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# 设置 PlantCV 的全局参数
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pcv.params.debug = args.debug
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pcv.params.dpi = 100 # 设置 DPI
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pcv.params.text_size = 1 # 文本大小
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pcv.params.text_thickness = 1 # 文本粗细
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# 读取光谱图像（ENVI 格式）
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spectral_array = pcv.readimage(filename=args.image, mode='envi')
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bath_100 = spectral_array.array_data[:, :, 100]
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bath_over = pcv.threshold.binary(gray_img=bath_100, threshold=5500, object_type='light')
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# 将PIL 16位掩膜转换为8位二值化格式
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# 如果mask是非零值则设置为255（塑料区域），否则为0（背景）
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mask_array = np.array(mask) if not isinstance(mask, np.ndarray) else mask
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b_thresh_unit8 = ((mask_array > 0).astype(np.uint8)) * 255
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# 填充小的孔洞，并反转掩膜
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b_fill = pcv.fill(bin_img=b_thresh_unit8, size=250)
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b_fill_inverted = pcv.invert(b_fill)
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# 创建形态统计和光谱统计掩膜
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dilate_img = pcv.dilate(gray_img=b_fill_inverted, ksize=3, i=4)
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bath_erode = pcv.erode(gray_img=bath_over, ksize=3, i=4)
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# 塑料掩膜的反转和非黑色背景的掩膜相乘
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dilate_img = dilate_img * bath_erode / 255
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dilate_img = pcv.fill(bin_img=dilate_img, size=10000)
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# 使用cv2.connectedComponents代替pcv.create_labels，避免污染plantcv outputs
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num1, labeled_spectral_mask = cv2.connectedComponents(dilate_img.astype(np.uint8))
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# plt.figure(figsize=(10, 8))
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# plt.imshow(dilate_img, cmap='nipy_spectral')
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# plt.colorbar(label='Object Label')
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# plt.title(f'Labeled Spectral Mask - {num1} objects detected')
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# plt.xlabel('X coordinate')
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# plt.ylabel('Y coordinate')
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# plt.show()
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print(f"Number of objects (second mask): {num1 - 1}") # -1 because first label is background
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# 直接计算每个对象的面积并找到最大面积的对象
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max_area = 0
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max_label = 0
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# 遍历所有标签（从1开始，0是背景）
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for label in range(1, num1):
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# 创建当前标签的掩膜
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obj_mask = labeled_spectral_mask == label
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# 计算面积
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area = np.sum(obj_mask)
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if area > max_area:
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max_area = area
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max_label = label
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# 提取最大面积对象的光谱
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if max_label > 0:
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max_obj_mask = labeled_spectral_mask == max_label
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# 获取最大对象在所有波段的光谱值
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spectral_values = spectral_array.array_data[max_obj_mask, :]
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# 计算平均光谱
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mean_spectrum = np.mean(spectral_values, axis=0)
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# 返回最大面积对象的平均光谱
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return mean_spectrum/10000
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else:
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# 如果没有找到有效背景对象，返回全零光谱
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return np.zeros(spectral_array.array_data.shape[-1]) / 10000
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