NanjingForestryUniversity/supermachine--tomato-passion_fruit
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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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TG 2024-06-30 16:19:11 +08:00
parent de9ffff6a2
commit e9ada7a266
4 changed files with 77 additions and 24 deletions
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23
.idea/supermachine--tomato-passion_fruit.iml generated
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@ -3,6 +3,29 @@
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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/main1不带资源文件夹使用原始py文件" />
<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" />
<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main4设置与main3一致，去掉了resnet权重" />
<excludeFolder url="file://$MODULE_DIR$/20240627Actual_deployed/封装exe/result/main5与main1一致，py文件使用20240529RGBtest3的main" />
<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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58
20240529RGBtest3/classifer.py
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@ -590,16 +590,19 @@ class Data_processing:
# 将处理后的图像转换为 RGB 格式 # 将处理后的图像转换为 RGB 格式
rp = cv2.cvtColor(nogreen, cv2.COLOR_BGR2RGB) rp = cv2.cvtColor(nogreen, cv2.COLOR_BGR2RGB)
#直径单位为cm #直径单位为cm
diameter = (long_axis + short_axis) * setting.pixel_length_ratio / 2 # diameter = (long_axis + short_axis) * setting.pixel_length_ratio / 2
#20240628与何工确定直径以长径为准
diameter = long_axis * setting.pixel_length_ratio
# print(f'直径：{diameter}') # print(f'直径：{diameter}')
# 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 ###异常判断改为发送结果前进行判断
if diameter < 2.5: # # 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
diameter = 0 # if diameter < 2.5:
green_percentage = 0 # diameter = 0
number_defects = 0 # green_percentage = 0
total_pixels = 0 # number_defects = 0
rp = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands), # total_pixels = 0
dtype=np.uint8), cv2.COLOR_BGR2RGB) # rp = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
# dtype=np.uint8), cv2.COLOR_BGR2RGB)
return diameter, green_percentage, number_defects, total_pixels, rp return diameter, green_percentage, number_defects, total_pixels, rp
def analyze_passion_fruit(self, img): def analyze_passion_fruit(self, img):
@ -639,16 +642,17 @@ class Data_processing:
org_defect = pf.bitwise_and_rgb_with_binary(edge, max_mask) org_defect = pf.bitwise_and_rgb_with_binary(edge, max_mask)
rp = cv2.cvtColor(org_defect, cv2.COLOR_BGR2RGB) rp = cv2.cvtColor(org_defect, cv2.COLOR_BGR2RGB)
#直径单位为cm #直径单位为cm
diameter = (long_axis + short_axis) * setting.pixel_length_ratio / 2 # diameter = (long_axis + short_axis) * setting.pixel_length_ratio / 2
diameter = long_axis * setting.pixel_length_ratio
# print(f'直径：{diameter}') # print(f'直径：{diameter}')
if diameter < 2.5: # if diameter < 2.5:
diameter = 0 # diameter = 0
green_percentage = 0 # green_percentage = 0
weight = 0 # weight = 0
number_defects = 0 # number_defects = 0
total_pixels = 0 # total_pixels = 0
rp = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands), # rp = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
dtype=np.uint8), cv2.COLOR_BGR2RGB) # dtype=np.uint8), cv2.COLOR_BGR2RGB)
return diameter, green_percentage, weight, number_defects, total_pixels, rp return diameter, green_percentage, weight, number_defects, total_pixels, rp
def process_data(seif, cmd: str, images: list, spec: any, pipe: Pipe, detector: Spec_predict) -> bool: def process_data(seif, cmd: str, images: list, spec: any, pipe: Pipe, detector: Spec_predict) -> bool:
@ -699,6 +703,14 @@ class Data_processing:
if cmd == 'TO': if cmd == 'TO':
brix = 0 brix = 0
weight = 0 weight = 0
# 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
if diameter < 3:
diameter = 0
gp = 0
max_defect_num = 0
max_total_defect_area = 0
rp_result = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
dtype=np.uint8), cv2.COLOR_BGR2RGB)
# 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};' # 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};'
# 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};') # 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};')
response = pipe.send_data(cmd=cmd, brix=brix, diameter=diameter, green_percentage=gp, weight=weight, response = pipe.send_data(cmd=cmd, brix=brix, diameter=diameter, green_percentage=gp, weight=weight,
@ -706,8 +718,16 @@ class Data_processing:
return response return response
elif cmd == 'PF': elif cmd == 'PF':
brix = detector.predict(spec) brix = detector.predict(spec)
if diameter == 0: # 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
if diameter < 2.5:
brix = 0 brix = 0
diameter = 0
gp= 0
weight = 0
max_defect_num = 0
max_total_defect_area = 0
rp_result = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
dtype=np.uint8), cv2.COLOR_BGR2RGB)
# 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};' # 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};'
# 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};') # 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};')
response = pipe.send_data(cmd=cmd, brix=brix, green_percentage=gp, diameter=diameter, weight=weight, response = pipe.send_data(cmd=cmd, brix=brix, green_percentage=gp, diameter=diameter, weight=weight,

16
20240529RGBtest3/main.py
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@ -6,6 +6,9 @@
import sys import sys
import os import os
import cv2
from root_dir import ROOT_DIR from root_dir import ROOT_DIR
from classifer import Spec_predict, Data_processing from classifer import Spec_predict, Data_processing
# from classifer import ImageClassifier # from classifer import ImageClassifier
@ -54,13 +57,15 @@ def main(is_debug=False):
if cmd == 'YR': if cmd == 'YR':
break break
#主循环 #主循环
# q = 1 q = 1
while True: while True:
#RGB图像部分 #RGB图像部分
# start_time = time.time() # start_time = time.time()
images = [] images = []
cmd = None cmd = None
for _ in range(5): #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
#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）
for i in range(5):
# start_time1 = time.time() # start_time1 = time.time()
data = pipe.receive_rgb_data(rgb_receive) data = pipe.receive_rgb_data(rgb_receive)
# end_time10 = time.time() # end_time10 = time.time()
@ -68,6 +73,11 @@ def main(is_debug=False):
# start_time11 = time.time() # start_time11 = time.time()
cmd, img = pipe.parse_img(data) cmd, img = pipe.parse_img(data)
#接收到的图像保存本地
# img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
# # cv2.imwrite(f'./{q}_{i}.bmp', img)
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# end_time1 = time.time() # end_time1 = time.time()
# print(f'解析第{q}组第{i}份RGB数据时间：{(end_time1 - start_time11) * 1000}毫秒') # print(f'解析第{q}组第{i}份RGB数据时间：{(end_time1 - start_time11) * 1000}毫秒')
# print(f'接收第{q}组第{i}张RGB图时间：{(end_time1 - start_time1) * 1000}毫秒') # print(f'接收第{q}组第{i}张RGB图时间：{(end_time1 - start_time1) * 1000}毫秒')
@ -115,7 +125,7 @@ def main(is_debug=False):
# end_time = time.time() # end_time = time.time()
# print(f'第{q}组全流程时间：{(end_time - start_time) * 1000}毫秒') # print(f'第{q}组全流程时间：{(end_time - start_time) * 1000}毫秒')
# q += 1 q += 1
if __name__ == '__main__': if __name__ == '__main__':
''' '''

4
20240529RGBtest3/qt_test.py
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@ -69,8 +69,8 @@ class MainWindow(QMainWindow):
:param image_dir: bmp和raw文件所在文件夹 :param image_dir: bmp和raw文件所在文件夹
:return: :return:
''' '''
rgb_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(('.bmp'))][:5] rgb_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(('.bmp'))][:20]
spec_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.raw')][:1] spec_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.raw')][:5]
self.send_YR() self.send_YR()
for _ in range(5): for _ in range(5):