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https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
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feat:增加百香果空果拖判断逻辑（如果直径为0，则糖度值也为0，即返回全0数据）；添加尺寸转换，现在所有的返回数据均为显示尺寸单位数值（糖度值*1000，绿色占比*100，缺陷面积*100进行数据传输）
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@ -417,6 +417,7 @@ class Data_processing:
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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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# 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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@ -436,7 +437,7 @@ class Data_processing:
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total_pixels_black_areas += cv2.contourArea(c)
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number_defects = count_black_areas
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total_pixels = total_pixels_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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def weight_estimates(self, long_axis, short_axis):
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@ -451,11 +452,12 @@ class Data_processing:
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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 / 535) * 6.3) / 2
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b = ((short_axis /535) * 6.3) / 2
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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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weigth = round(volume * density)
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return weigth
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weight = round(volume * 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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"""
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分析给定图像，提取和返回西红柿的长径、短径、缺陷数量和缺陷总面积，并返回处理后的图像。
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@ -490,8 +492,10 @@ class Data_processing:
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number_defects, total_pixels = self.analyze_defect(new_bin_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(org_defect, cv2.COLOR_BGR2RGB)
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diameter = (long_axis + short_axis) / 2
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if diameter < 200:
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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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# 如果直径小于3，判断为空果拖异常图，则将所有值重置为0
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if diameter < 3:
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diameter = 0
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green_percentage = 0
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number_defects = 0
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@ -515,14 +519,15 @@ class Data_processing:
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max_mask = pf.find_largest_component(combined_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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weigth = self.weight_estimates(long_axis, short_axis)
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weight = self.weight_estimates(long_axis, short_axis)
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number_defects, total_pixels = self.analyze_defect(max_mask)
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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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diameter = (long_axis + short_axis) / 2
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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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return diameter, weigth, number_defects, total_pixels, rp
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return diameter, weight, number_defects, total_pixels, rp
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# #下面封装的是ResNet18和ResNet34的网络模型构建
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@ -46,14 +46,14 @@ def process_data(cmd: str, images: list, spec: any, dp: Data_processing, pipe: P
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elif cmd == 'PF':
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# 百香果
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diameter, weigth, number_defects, total_pixels, rp = dp.analyze_passion_fruit(img)
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diameter, weight, number_defects, total_pixels, rp = dp.analyze_passion_fruit(img)
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if i <= 2:
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diameter_axis_list.append(diameter)
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max_defect_num = max(max_defect_num, number_defects)
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max_total_defect_area = max(max_total_defect_area, total_pixels)
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if i == 1:
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rp_result = rp
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weigth = weigth
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weight = weight
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else:
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logging.error(f'错误指令，指令为{cmd}')
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@ -63,14 +63,16 @@ 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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weigth = 0
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response = pipe.send_data(cmd=cmd, brix=brix, diameter=diameter, green_percentage=gp, weigth=weigth,
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weight = 0
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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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elif cmd == 'PF':
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green_percentage = 0
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brix = detector.predict(spec)
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response = pipe.send_data(cmd=cmd, brix=brix, green_percentage=green_percentage, diameter=diameter, weigth=weigth,
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if diameter == 0:
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brix = 0
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response = pipe.send_data(cmd=cmd, brix=brix, green_percentage=green_percentage, diameter=diameter, 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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@ -150,17 +150,17 @@ class MainWindow(QMainWindow):
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# 解析数据
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cmd_result = data[:2].decode('ascii').strip().upper()
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brix = (int.from_bytes(data[2:4], byteorder='big')) / 1000
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green_percentage = int.from_bytes(data[4:5], byteorder='big')
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green_percentage = (int.from_bytes(data[4:5], byteorder='big')) / 100
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diameter = int.from_bytes(data[5:7], byteorder='big')
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weight = int.from_bytes(data[7:8], byteorder='big')
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defect_num = int.from_bytes(data[8:10], byteorder='big')
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total_defect_area = int.from_bytes(data[10:14], byteorder='big')
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total_defect_area = (int.from_bytes(data[10:14], byteorder='big')) / 100
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heigth = int.from_bytes(data[14:16], byteorder='big')
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width = int.from_bytes(data[16:18], byteorder='big')
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rp = data[18:]
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img = np.frombuffer(rp, dtype=np.uint8).reshape(heigth, width, -1)
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print(f"指令:{cmd_result}, 糖度值:{brix}, 绿色占比:{green_percentage}, 直径:{diameter}, "
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f"预估重量:{weight}, 缺陷个数:{defect_num}, 缺陷面积:{total_defect_area}, 结果图的尺寸:{img.shape}")
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print(f"指令:{cmd_result}, 糖度值:{brix}, 绿色占比:{green_percentage}, 直径:{diameter}cm, "
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f"预估重量:{weight}g, 缺陷个数:{defect_num}, 缺陷面积:{total_defect_area}cm^2, 结果图的尺寸:{img.shape}")
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# 显示结果图像
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@ -159,13 +159,13 @@ class Pipe:
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spec = np.frombuffer(spec, dtype=np.uint16).reshape((n_rows, n_bands, -1)).transpose(0, 2, 1)
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return cmd, spec
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def send_data(self,cmd:str, brix, green_percentage, weigth, diameter, defect_num, total_defect_area, rp):
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def send_data(self,cmd:str, brix, green_percentage, weight, diameter, defect_num, total_defect_area, rp):
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'''
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发送数据
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:param cmd:
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:param brix:
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:param green_percentage:
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:param weigth:
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:param weight:
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:param diameter:
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:param defect_num:
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:param total_defect_area:
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@ -195,6 +195,7 @@ class Pipe:
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img_bytes = img.tobytes()
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diameter = diameter.to_bytes(2, byteorder='big')
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defect_num = defect_num.to_bytes(2, byteorder='big')
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total_defect_area = total_defect_area * 1000
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total_defect_area = int(total_defect_area).to_bytes(4, byteorder='big')
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length = len(img_bytes) + 18
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length = length.to_bytes(4, byteorder='big')
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@ -202,22 +203,22 @@ class Pipe:
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brix = 0
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brix = brix.to_bytes(2, byteorder='big')
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gp = green_percentage.to_bytes(1, byteorder='big')
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weigth = 0
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weigth = weigth.to_bytes(1, byteorder='big')
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send_message = length + cmd_re + brix + gp + diameter + weigth + defect_num + total_defect_area + height + width + img_bytes
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weight = 0
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weight = weight.to_bytes(1, byteorder='big')
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send_message = length + cmd_re + brix + gp + diameter + weight + defect_num + total_defect_area + height + width + img_bytes
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elif cmd == 'PF':
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brix = int(brix * 1000).to_bytes(2, byteorder='big')
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gp = 0
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gp = gp.to_bytes(1, byteorder='big')
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weigth = weigth.to_bytes(1, byteorder='big')
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send_message = length + cmd_re + brix + gp + diameter + weigth + defect_num + total_defect_area + height + width + img_bytes
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weight = weight.to_bytes(1, byteorder='big')
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send_message = length + cmd_re + brix + gp + diameter + weight + defect_num + total_defect_area + height + width + img_bytes
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elif cmd == 'KO':
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brix = 0
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brix = brix.to_bytes(2, byteorder='big')
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gp = 0
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gp = gp.to_bytes(1, byteorder='big')
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weigth = 0
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weigth = weigth.to_bytes(1, byteorder='big')
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weight = 0
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weight = weight.to_bytes(1, byteorder='big')
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defect_num = 0
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defect_num = defect_num.to_bytes(2, byteorder='big')
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total_defect_area = 0
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@ -228,7 +229,7 @@ class Pipe:
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width = width.to_bytes(2, byteorder='big')
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img_bytes = np.zeros((100, 100, 3), dtype=np.uint8).tobytes()
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length = (18).to_bytes(4, byteorder='big')
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send_message = length + cmd_re + brix + gp + diameter + weigth + defect_num + total_defect_area + height + width + img_bytes
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send_message = length + cmd_re + brix + gp + diameter + weight + defect_num + total_defect_area + height + width + img_bytes
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try:
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win32file.WriteFile(self.rgb_send, send_message)
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# time.sleep(0.01)
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