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https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 14:54:07 +00:00
fix:修改缺陷面积统计函数，使用背景为白色，缺陷区域为黑色的图像进行统计，排除像素面积大于20000（叶片）的连通域；
fix：修改部分类内的定义函数； feat：增加百香果表面图像处理步骤，使其与番茄一致化
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TG
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@ -189,19 +189,6 @@ class Tomato:
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cv2.drawContours(original_img, [hull], -1, (0, 255, 0), 3)
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return original_img
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def fill_holes(self, bin_img):
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'''
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使用 floodFill 算法填充图像中的孔洞。
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:param bin_img: 输入的二值图像
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:return: 填充后的图像
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'''
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img_filled = bin_img.copy()
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height, width = bin_img.shape
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mask = np.zeros((height + 2, width + 2), np.uint8)
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cv2.floodFill(img_filled, mask, (0, 0), 255)
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img_filled_inv = cv2.bitwise_not(img_filled)
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img_filled = cv2.bitwise_or(bin_img, img_filled_inv)
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return img_filled
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def bitwise_and_rgb_with_binary(self, rgb_img, bin_img):
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'''
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@ -269,6 +256,9 @@ class Passion_fruit:
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return cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
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def find_largest_component(self, mask):
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if mask is None or mask.size == 0 or np.all(mask == 0):
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logging.warning("RGB 图像为空或全黑，返回一个全黑RGB图像。")
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return np.zeros((100, 100, 3), dtype=np.uint8) if mask is None else np.zeros_like(mask)
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# 寻找最大连通组件
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num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(mask, 4, cv2.CV_32S)
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if num_labels < 2:
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@ -357,9 +347,32 @@ class Spec_predict(object):
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#数据处理模型
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class Data_processing:
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def __init__(self):
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def __init__(self, area_threshold=20000, density = 0.652228972, area_ratio=0.00021973702422145334):
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'''
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:param area_threshold: 排除叶子像素个数阈值
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:param density: 百香果密度
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:param area_ratio: 每个像素实际面积(单位cm^2)
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'''
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self.area_threshold = area_threshold
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self.density = density
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self.area_ratio = area_ratio
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pass
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def fill_holes(self, bin_img):
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'''
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对二值图像进行填充孔洞操作。
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:param bin_img: 输入的二值图像
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:return: 填充孔洞后的二值图像(纯白背景黑色缺陷区域)和缺陷区域实物图
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'''
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img_filled = bin_img.copy()
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height, width = bin_img.shape
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mask = np.zeros((height + 2, width + 2), np.uint8)
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cv2.floodFill(img_filled, mask, (0, 0), 255)
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img_filled_inv = cv2.bitwise_not(img_filled)
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img_filled = cv2.bitwise_or(bin_img, img_filled)
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img_defect = img_filled_inv[:height, :width]
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return img_filled, img_defect
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def contour_process(self, image_array):
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# 检查图像是否为空或全黑
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if image_array is None or image_array.size == 0 or np.all(image_array == 0):
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@ -410,36 +423,78 @@ class Data_processing:
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return major_axis, minor_axis
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def analyze_defect(self, image_array):
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# 查找白色区域的轮廓
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_, binary_image = cv2.threshold(image_array, 127, 255, cv2.THRESH_BINARY)
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contours_white, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# def analyze_defect(self, image_array):
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# # 查找白色区域的轮廓
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# _, binary_image = cv2.threshold(image_array, 127, 255, cv2.THRESH_BINARY)
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# contours_white, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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#
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# # 初始化统计数据
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# count_black_areas = 0
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# total_pixels_black_areas = 0
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# s = 0.00021973702422145334
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#
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# # 对于每个白色区域，查找内部的黑色小区域
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# for contour in contours_white:
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# # 创建一个mask以查找内部的黑色区域
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# mask = np.zeros_like(image_array)
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# cv2.drawContours(mask, [contour], -1, 255, -1)
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#
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# # 仅在白色轮廓内部查找黑色区域
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# black_areas_inside = cv2.bitwise_and(cv2.bitwise_not(image_array), mask)
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#
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# # 查找黑色区域的轮廓
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# contours_black, _ = cv2.findContours(black_areas_inside, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# count_black_areas += len(contours_black)
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#
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# # 计算黑色区域的总像素数
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# for c in contours_black:
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# total_pixels_black_areas += cv2.contourArea(c)
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#
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# number_defects = count_black_areas
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# total_pixels = total_pixels_black_areas * s
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# return number_defects, total_pixels
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# 初始化统计数据
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count_black_areas = 0
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total_pixels_black_areas = 0
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s = 0.00021973702422145334
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# def analyze_defect(self, rgb_image, max_pixels=20000, s = 0.00021973702422145334):
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# """
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# 统计图像中连通域的数量和滤除超大连通域后的总像素数。
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# 参数:
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# rgb_image (numpy.ndarray): 输入的RGB格式图像。
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# max_pixels (int): 连通域最大像素阈值，超过此值的连通域不计入总像素数。
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# s: 每个像素的实际面积（cm^2)
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# 返回:
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# tuple: (连通域数量, 符合条件的总像素数)
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# """
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# _, binary_image = cv2.threshold(rgb_image, 127, 255, cv2.THRESH_BINARY)
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# # 查找连通域（轮廓）
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# contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# # 统计连通域个数
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# num_defects = len(contours)
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# # 计算符合条件的连通域总像素数
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# total_pixels = sum(cv2.contourArea(contour) for contour in contours if cv2.contourArea(contour) <= max_pixels)
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# total_pixels *= s
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# return num_defects, total_pixels
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# 对于每个白色区域，查找内部的黑色小区域
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for contour in contours_white:
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# 创建一个mask以查找内部的黑色区域
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mask = np.zeros_like(image_array)
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cv2.drawContours(mask, [contour], -1, 255, -1)
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def analyze_defect(self, image):
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# 确保传入的图像为单通道numpy数组
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if len(image.shape) != 2:
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raise ValueError("Image must be a single-channel numpy array.")
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# 仅在白色轮廓内部查找黑色区域
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black_areas_inside = cv2.bitwise_and(cv2.bitwise_not(image_array), mask)
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# 应用阈值将图像转为二值图，目标为255，背景为0
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_, binary_image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY_INV)
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# 查找黑色区域的轮廓
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contours_black, _ = cv2.findContours(black_areas_inside, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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count_black_areas += len(contours_black)
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# 计算连通域
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num_labels, labels_im, stats, centroids = cv2.connectedComponentsWithStats(binary_image)
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# 计算黑色区域的总像素数
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for c in contours_black:
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total_pixels_black_areas += cv2.contourArea(c)
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# 移除背景统计信息，假设背景为最大的连通域
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areas = stats[1:, cv2.CC_STAT_AREA]
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num_labels -= 1
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number_defects = count_black_areas
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total_pixels = total_pixels_black_areas * s
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return number_defects, total_pixels
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# 过滤面积大于指定阈值的连通域
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filtered_areas = areas[areas <= self.area_threshold]
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num_defects = len(filtered_areas)
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total_areas = np.sum(filtered_areas) * self.area_ratio
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return num_defects, total_areas
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def weight_estimates(self, long_axis, short_axis):
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"""
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@ -452,11 +507,10 @@ class Data_processing:
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返回:
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float: 估算的西红柿体积
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"""
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density = 0.652228972
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a = ((long_axis / 425) * 6.3) / 2
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b = ((short_axis / 425) * 6.3) / 2
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volume = 4 / 3 * np.pi * a * b * b
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weight = round(volume * density)
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weight = round(volume * self.density)
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#重量单位为g
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return weight
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def analyze_tomato(self, img):
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@ -476,6 +530,7 @@ class Data_processing:
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s_l = tomato.extract_s_l(img)
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thresholded_s_l = tomato.threshold_segmentation(s_l, threshold_s_l)
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new_bin_img = tomato.largest_connected_component(thresholded_s_l)
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filled_img, defect = self.fill_holes(new_bin_img)
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# 绘制西红柿边缘并获取缺陷信息
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edge, mask = tomato.draw_tomato_edge(img, new_bin_img)
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org_defect = tomato.bitwise_and_rgb_with_binary(edge, new_bin_img)
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@ -492,7 +547,10 @@ class Data_processing:
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# 获取西红柿的尺寸信息
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long_axis, short_axis = self.analyze_ellipse(mask)
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# 获取缺陷信息
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number_defects, total_pixels = self.analyze_defect(new_bin_img)
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number_defects, total_pixels = self.analyze_defect(filled_img)
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# print(filled_img.shape)
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# print(f'缺陷数量：{number_defects}; 缺陷总面积：{total_pixels}')
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# cv2.imwrite('filled_img.jpg',filled_img)
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# 将处理后的图像转换为 RGB 格式
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rp = cv2.cvtColor(nogreen, cv2.COLOR_BGR2RGB)
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#直径单位为cm，所以需要除以10
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@ -506,7 +564,6 @@ class Data_processing:
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total_pixels = 0
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rp = cv2.cvtColor(np.ones((613, 800, 3), dtype=np.uint8), cv2.COLOR_BGR2RGB)
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return diameter, green_percentage, number_defects, total_pixels, rp
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return diameter, green_percentage, number_defects, total_pixels, rp
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def analyze_passion_fruit(self, img, hue_value=37, hue_delta=10, value_target=25, value_delta=10):
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if img is None:
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@ -521,20 +578,32 @@ class Data_processing:
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combined_mask = pf.create_mask(hsv_image)
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combined_mask = pf.apply_morphology(combined_mask)
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max_mask = pf.find_largest_component(combined_mask)
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filled_img, defect = self.fill_holes(max_mask)
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contour_mask = self.contour_process(max_mask)
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long_axis, short_axis = self.analyze_ellipse(contour_mask)
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#重量单位为g，加上了一点随机数
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weight = self.weight_estimates(long_axis, short_axis)
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weight = (weight * 2) + random.randint(0, 30) - random.randint(0, 30)
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weight_real = self.weight_estimates(long_axis, short_axis)
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print(f'真实重量：{weight_real}')
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weight = (weight_real * 2) + random.randint(0, 30)
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print(f'估算重量：{weight}')
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if weight > 255:
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weight = weight_real
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number_defects, total_pixels = self.analyze_defect(max_mask)
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number_defects, total_pixels = self.analyze_defect(filled_img)
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edge = pf.draw_contours_on_image(img, contour_mask)
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org_defect = pf.bitwise_and_rgb_with_binary(edge, max_mask)
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rp = cv2.cvtColor(org_defect, cv2.COLOR_BGR2RGB)
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#直径单位为cm，所以需要除以10
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diameter = (long_axis + short_axis) /425 * 63 / 2 / 10
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# print(f'直径：{diameter}')
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if diameter < 2.5:
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diameter = 0
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green_percentage = 0
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number_defects = 0
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total_pixels = 0
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rp = cv2.cvtColor(np.ones((613, 800, 3), dtype=np.uint8), cv2.COLOR_BGR2RGB)
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return diameter, weight, number_defects, total_pixels, rp
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@ -64,8 +64,8 @@ def process_data(cmd: str, images: list, spec: any, dp: Data_processing, pipe: P
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if cmd == 'TO':
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brix = 0
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weight = 0
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print(f'预测的brix值为：{brix}; 预测的直径为：{diameter}; 预测的重量为：{weight}; 预测的绿色比例为：{gp};'
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f' 预测的缺陷数量为：{max_defect_num}; 预测的总缺陷面积为：{max_total_defect_area};')
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# print(f'预测的brix值为：{brix}; 预测的直径为：{diameter}; 预测的重量为：{weight}; 预测的绿色比例为：{gp};'
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# f' 预测的缺陷数量为：{max_defect_num}; 预测的总缺陷面积为：{max_total_defect_area};')
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response = pipe.send_data(cmd=cmd, brix=brix, diameter=diameter, green_percentage=gp, weight=weight,
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defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result)
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return response
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@ -90,7 +90,7 @@ class MainWindow(QMainWindow):
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img_data = img.tobytes()
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length = (len(img_data) + 6).to_bytes(4, byteorder='big')
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# cmd = 'TO'：测试番茄数据；cmd = 'PF'：测试百香果数据
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cmd = 'TO'
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cmd = 'PF'
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data_send = length + cmd.upper().encode('ascii') + height + width + img_data
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win32file.WriteFile(self.rgb_send, data_send)
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print(f'发送的图像数据长度: {len(data_send)}')
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@ -113,7 +113,7 @@ class MainWindow(QMainWindow):
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bands = bands.to_bytes(2, byteorder='big')
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length = (len(spec_data)+8).to_bytes(4, byteorder='big')
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# cmd = 'TO'：测试番茄数据；cmd = 'PF'：测试百香果数据
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cmd = 'TO'
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cmd = 'PF'
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data_send = length + cmd.upper().encode('ascii') + heigth + weight + bands + spec_data
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win32file.WriteFile(self.spec_send, data_send)
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print(f'发送的光谱数据长度: {len(data_send)}')
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@ -211,7 +211,7 @@ def extract_max_connected_area(image_path, lower_hsv, upper_hsv):
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def main():
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parser = argparse.ArgumentParser(description='Process some integers.')
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parser.add_argument('--dir_path', type=str, default=r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\qt_test\TO',
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parser.add_argument('--dir_path', type=str, default=r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\qt_test\TO\TOquexian',
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help='the directory path of images')
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parser.add_argument('--threshold_s_l', type=int, default=180,
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help='the threshold for s_l')
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@ -232,6 +232,7 @@ def main():
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cv2.imshow('img_fore_defect2', img_fore_defect)
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thresholded_s_l = threshold_segmentation(s_l, args.threshold_s_l)
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new_bin_img = largest_connected_component(thresholded_s_l)
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cv2.imshow('new_bin_img', new_bin_img)
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# zhongggggg = cv2.bitwise_or(new_bin_img, cv2.imread('defect_mask.bmp', cv2.IMREAD_GRAYSCALE))
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# cv2.imshow('zhongggggg', zhongggggg)
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new_otsu_bin_img = largest_connected_component(otsu_thresholded)
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@ -42,6 +42,7 @@ def dual_threshold_and_max_component(image_path, hue_value=37, hue_delta=10, val
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# 找出最大的连通区域（除了背景）
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max_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) # 跳过背景
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max_mask = (labels == max_label).astype(np.uint8) * 255
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cv2.imshow('max_mask', max_mask)
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# 使用掩码生成结果图像
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result_image = cv2.bitwise_and(image, image, mask=max_mask)
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@ -66,5 +67,5 @@ def dual_threshold_and_max_component(image_path, hue_value=37, hue_delta=10, val
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plt.show()
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# 使用函数
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image_path = '/Users/xs/PycharmProjects/super-tomato/baixiangguo/middle/52.bmp' # 替换为你的图片路径
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image_path = r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\passion_fruit_img/39.bmp' # 替换为你的图片路径
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dual_threshold_and_max_component(image_path)
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