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https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-08 22:34:00 +00:00
fix:修复番茄处理模型bug（返回结果图并未去除绿色叶子部分）
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TG
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93ae89ece3
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@ -481,27 +481,30 @@ class Data_processing:
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org_defect = tomato.bitwise_and_rgb_with_binary(edge, new_bin_img)
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fore = tomato.bitwise_and_rgb_with_binary(img, mask)
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fore_g_r_t = tomato.threshold_segmentation(tomato.extract_g_r(fore), threshold=threshold_fore_g_r_t)
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res = cv2.bitwise_or(new_bin_img, fore_g_r_t)
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nogreen = tomato.bitwise_and_rgb_with_binary(edge, res)
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# 统计白色像素点个数
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# print(np.sum(fore_g_r_t == 255))
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# print(np.sum(mask == 255))
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# print(np.sum(fore_g_r_t == 255) / np.sum(mask == 255))
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green_percentage = np.sum(fore_g_r_t == 255) / np.sum(mask == 255)
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green_percentage = round(green_percentage, 2) * 100
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green_percentage = round(green_percentage, 2)
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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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# 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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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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diameter = (long_axis + short_axis) /425 * 63 / 2 / 10
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# print(f'直径：{diameter}')
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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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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((100, 100, 3), dtype=np.uint8), cv2.COLOR_BGR2RGB)
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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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@ -530,6 +533,7 @@ class Data_processing:
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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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return diameter, weight, number_defects, total_pixels, rp
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@ -42,7 +42,7 @@ def process_data(cmd: str, images: list, spec: any, dp: Data_processing, pipe: P
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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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gp = round(green_percentage)
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gp = round(green_percentage, 2)
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elif cmd == 'PF':
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# 百香果
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@ -59,11 +59,13 @@ def process_data(cmd: str, images: list, spec: any, dp: Data_processing, pipe: P
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logging.error(f'错误指令，指令为{cmd}')
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return False
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diameter = round(sum(diameter_axis_list) / 3)
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diameter = round(sum(diameter_axis_list) / 3, 2)
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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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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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@ -72,6 +74,8 @@ def process_data(cmd: str, images: list, spec: any, dp: Data_processing, pipe: P
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brix = detector.predict(spec)
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if diameter == 0:
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brix = 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：{green_percentage};'
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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, 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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@ -74,7 +74,7 @@ class MainWindow(QMainWindow):
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spec_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.raw')][:1]
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self.send_YR()
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for _ in range(20):
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for _ in range(5):
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for image_path in rgb_files:
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img = cv2.imread(image_path, cv2.IMREAD_COLOR)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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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 = 'PF'
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cmd = 'TO'
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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 = 'PF'
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cmd = 'TO'
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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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@ -151,10 +151,10 @@ class MainWindow(QMainWindow):
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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')) / 100
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diameter = int.from_bytes(data[5:7], byteorder='big')
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diameter = (int.from_bytes(data[5:7], byteorder='big')) / 100
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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')) / 100
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total_defect_area = (int.from_bytes(data[10:14], byteorder='big')) / 1000
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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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@ -193,16 +193,15 @@ class Pipe:
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height = height.to_bytes(2, byteorder='big')
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width = width.to_bytes(2, byteorder='big')
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img_bytes = img.tobytes()
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diameter = diameter.to_bytes(2, byteorder='big')
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diameter = int(diameter * 100).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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total_defect_area = int(total_defect_area * 1000).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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if cmd == 'TO':
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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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gp = int(green_percentage * 100).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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send_message = length + cmd_re + brix + gp + diameter + weight + defect_num + total_defect_area + height + width + img_bytes
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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\20240419RGBtest2\data',
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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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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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