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https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 14:54:07 +00:00
fix:修改空果拖判别时刻，改为一个果子最后汇总指标信息后进行判别；根据何工确定，直径以长径为准
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.idea/supermachine--tomato-passion_fruit.iml
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23
.idea/supermachine--tomato-passion_fruit.iml
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@ -3,6 +3,29 @@
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$">
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<excludeFolder url="file://$MODULE_DIR$/.idea/copilot/chatSessions" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/data_pyc" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main0带资源文件/_internal/cv2/gapi" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main0带资源文件/_internal/cv2/mat_wrapper" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main0带资源文件/_internal/cv2/misc" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main0带资源文件/_internal/cv2/utils" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main0带资源文件/_internal/numpy/.libs" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main0带资源文件/_internal/numpy/fft" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main0带资源文件/_internal/scipy/_lib/_uarray" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main0带资源文件/_internal/scipy/fft" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main0带资源文件/_internal/scipy/optimize/_highs" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main0带资源文件/_internal/scipy/spatial/transform" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main0带资源文件/_internal/sklearn/_loss" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main0带资源文件/_internal/sklearn/datasets/tests/data/openml/id_40945" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main0带资源文件/_internal/sklearn/preprocessing" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main1不带资源文件夹使用原始py文件" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main3不带资源文件夹使用pyc文件，修改log不报空果托错误信息，换图标" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main4设置与main3一致，去掉了resnet权重" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main5与main1一致，py文件使用20240529RGBtest3的main" />
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<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main6不带资源文件夹，py文件使用20240529RGBtest3，配置文件加载的是生成的pyc文件" />
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</content>
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<orderEntry type="jdk" jdkName="tengg" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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@ -590,16 +590,19 @@ 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(nogreen, cv2.COLOR_BGR2RGB)
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#直径单位为cm
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diameter = (long_axis + short_axis) * setting.pixel_length_ratio / 2
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# diameter = (long_axis + short_axis) * setting.pixel_length_ratio / 2
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#20240628与何工确定直径以长径为准
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diameter = long_axis * setting.pixel_length_ratio
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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 < 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((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
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dtype=np.uint8), cv2.COLOR_BGR2RGB)
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###异常判断改为发送结果前进行判断
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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 < 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((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
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# dtype=np.uint8), cv2.COLOR_BGR2RGB)
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return diameter, green_percentage, number_defects, total_pixels, rp
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def analyze_passion_fruit(self, img):
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@ -639,16 +642,17 @@ class Data_processing:
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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
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diameter = (long_axis + short_axis) * setting.pixel_length_ratio / 2
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# diameter = (long_axis + short_axis) * setting.pixel_length_ratio / 2
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diameter = long_axis * setting.pixel_length_ratio
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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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weight = 0
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number_defects = 0
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total_pixels = 0
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rp = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
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dtype=np.uint8), cv2.COLOR_BGR2RGB)
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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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# weight = 0
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# number_defects = 0
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# total_pixels = 0
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# rp = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
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# dtype=np.uint8), cv2.COLOR_BGR2RGB)
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return diameter, green_percentage, weight, number_defects, total_pixels, rp
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def process_data(seif, cmd: str, images: list, spec: any, pipe: Pipe, detector: Spec_predict) -> bool:
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@ -699,6 +703,14 @@ class Data_processing:
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if cmd == 'TO':
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brix = 0
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weight = 0
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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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gp = 0
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max_defect_num = 0
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max_total_defect_area = 0
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rp_result = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
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dtype=np.uint8), cv2.COLOR_BGR2RGB)
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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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@ -706,8 +718,16 @@ class Data_processing:
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return response
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elif cmd == 'PF':
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brix = detector.predict(spec)
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if diameter == 0:
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# 如果直径小于2.5，判断为空果拖异常图，则将所有值重置为0
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if diameter < 2.5:
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brix = 0
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diameter = 0
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gp= 0
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weight = 0
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max_defect_num = 0
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max_total_defect_area = 0
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rp_result = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
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dtype=np.uint8), cv2.COLOR_BGR2RGB)
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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=gp, diameter=diameter, weight=weight,
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@ -6,6 +6,9 @@
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import sys
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import os
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import cv2
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from root_dir import ROOT_DIR
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from classifer import Spec_predict, Data_processing
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# from classifer import ImageClassifier
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@ -54,13 +57,15 @@ def main(is_debug=False):
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if cmd == 'YR':
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break
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#主循环
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# q = 1
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q = 1
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while True:
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#RGB图像部分
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# start_time = time.time()
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images = []
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cmd = None
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for _ in range(5):
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#三个相机产生5张图，qt发送方顺序为上方相机3张，左右相机各1张
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#实际使用时，并未对最后两张两侧相机所得结果进行统计，因此也可改为3（qt发送方顺序为上方相机3张）
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for i in range(5):
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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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@ -68,6 +73,11 @@ def main(is_debug=False):
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# start_time11 = time.time()
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cmd, img = pipe.parse_img(data)
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#接收到的图像保存本地
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# img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
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# # cv2.imwrite(f'./{q}_{i}.bmp', img)
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# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# end_time1 = time.time()
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# print(f'解析第{q}组第{i}份RGB数据时间：{(end_time1 - start_time11) * 1000}毫秒')
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# print(f'接收第{q}组第{i}张RGB图时间：{(end_time1 - start_time1) * 1000}毫秒')
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@ -115,7 +125,7 @@ def main(is_debug=False):
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# end_time = time.time()
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# print(f'第{q}组全流程时间：{(end_time - start_time) * 1000}毫秒')
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# q += 1
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q += 1
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if __name__ == '__main__':
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'''
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@ -69,8 +69,8 @@ class MainWindow(QMainWindow):
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:param image_dir: bmp和raw文件所在文件夹
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:return:
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'''
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rgb_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(('.bmp'))][:5]
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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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rgb_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(('.bmp'))][:20]
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spec_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.raw')][:5]
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self.send_YR()
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for _ in range(5):
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