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https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-08 22:34:00 +00:00
fix：修复一些小问题，添加简单番茄背景判断，如果直径小于200，那么全赋0.返回（100,100,3）空图；由于耀哥&旻哥暂时没写管道预热，预热部分注释掉
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@ -359,7 +359,7 @@ class Data_processing:
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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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# print("输入的图像为空或全黑，返回一个全黑图像。")
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logging.error("输入的图像为空或全黑，返回一个全黑图像。")
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return np.zeros_like(image_array) if image_array is not None else np.zeros((100, 100), dtype=np.uint8)
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# 应用中值滤波
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image_filtered = cv2.medianBlur(image_array, 5)
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@ -488,11 +488,18 @@ class Data_processing:
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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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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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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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print("Error: 无图像数据.")
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logging.error("Error: 无图像数据.")
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return None
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# 创建PassionFruit类的实例
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@ -99,14 +99,14 @@ def main(is_debug=False):
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pipe = Pipe(rgb_receive_name, rgb_send_name, spec_receive_name)
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rgb_receive, rgb_send, spec_receive = pipe.create_pipes(rgb_receive_name, rgb_send_name, spec_receive_name)
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# 预热循环，只处理cmd为'YR'的数据
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while True:
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start_time00 = time.time()
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data = pipe.receive_rgb_data(rgb_receive)
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cmd, _ = pipe.parse_img(data)
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end_time00 = time.time()
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print(f'接收预热数据时间：{end_time00 - start_time00}秒')
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if cmd == 'YR':
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break # 当接收到的不是预热命令时，结束预热循环
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# while True:
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# start_time00 = time.time()
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# data = pipe.receive_rgb_data(rgb_receive)
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# cmd, _ = pipe.parse_img(data)
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# end_time00 = time.time()
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# print(f'接收预热数据时间：{end_time00 - start_time00}秒')
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# if cmd == 'YR':
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# break # 当接收到的不是预热命令时，结束预热循环
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while True:
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start_time = time.time()
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images = []
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@ -115,13 +115,13 @@ def main(is_debug=False):
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start_time1 = time.time()
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data = pipe.receive_rgb_data(rgb_receive)
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end_time10 = time.time()
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print(f'接收一份数据时间：{end_time10 - start_time1}秒')
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# print(f'接收一份数据时间：{end_time10 - start_time1}秒')
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start_time11 = time.time()
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cmd, img = pipe.parse_img(data)
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end_time1 = time.time()
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print(f'处理一份数据时间：{end_time1 - start_time11}秒')
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print(f'接收一张图时间：{end_time1 - start_time1}秒')
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# print(f'处理一份数据时间：{end_time1 - start_time11}秒')
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# print(f'接收一张图时间：{end_time1 - start_time1}秒')
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# 使用分类器进行预测
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# prediction = classifier.predict(img)
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@ -133,7 +133,7 @@ def main(is_debug=False):
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else:
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response = pipe.send_data(cmd='KO', brix=0, diameter=0, green_percentage=0, weigth=0, defect_num=0,
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total_defect_area=0, rp=np.zeros((100, 100, 3), dtype=np.uint8))
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print("图像中无果，跳过此图像")
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logging.info("图像中无果，跳过此图像")
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continue
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if cmd not in ['TO', 'PF', 'YR', 'KO']:
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@ -146,19 +146,19 @@ def main(is_debug=False):
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spec_data = pipe.receive_spec_data(spec_receive)
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_, spec = pipe.parse_spec(spec_data)
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end_time2 = time.time()
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print(f'接收光谱数据时间：{end_time2 - start_time2}秒')
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# print(f'接收光谱数据时间：{end_time2 - start_time2}秒')
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start_time3 = time.time()
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if images: # 确保images不为空
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response = process_data(cmd, images, spec, dp, pipe, detector)
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end_time3 = time.time()
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print(f'处理时间：{end_time3 - start_time3}秒')
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# print(f'处理时间：{end_time3 - start_time3}秒')
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if response:
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logging.info(f'处理成功，响应为: {response}')
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else:
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logging.error('处理失败')
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else:
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print("没有有效的图像进行处理")
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logging.error("没有有效的图像进行处理")
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end_time = time.time()
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print(f'全流程时间：{end_time - start_time}秒')
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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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