diff --git a/20240529RGBtest3/classifer.py b/20240529RGBtest3/classifer.py index cd6a06e..28ac842 100644 --- a/20240529RGBtest3/classifer.py +++ b/20240529RGBtest3/classifer.py @@ -13,6 +13,7 @@ import logging import random import numpy as np from PIL import Image +from utils import Pipe from sklearn.ensemble import RandomForestRegressor #图像分类网络所需库，实际并未使用分类网络 # import torch @@ -585,9 +586,9 @@ class Data_processing: long_axis, short_axis = self.analyze_ellipse(contour_mask) #重量单位为g，加上了一点随机数 weight_real = self.weight_estimates(long_axis, short_axis) - print(f'真实重量：{weight_real}') + # print(f'真实重量：{weight_real}') weight = (weight_real * 2) + random.randint(0, 30) - print(f'估算重量：{weight}') + # print(f'估算重量：{weight}') if weight > 255: weight = weight_real @@ -606,6 +607,71 @@ class Data_processing: rp = cv2.cvtColor(np.ones((613, 800, 3), dtype=np.uint8), cv2.COLOR_BGR2RGB) return diameter, weight, number_defects, total_pixels, rp + def process_data(seif, cmd: str, images: list, spec: any, pipe: Pipe, detector: Spec_predict) -> bool: + """ + 处理指令 + + :param cmd: 指令类型 + :param images: 图像数据列表 + :param spec: 光谱数据 + :param detector: 模型 + :return: 是否处理成功 + """ + # pipe = Pipe() + diameter_axis_list = [] + max_defect_num = 0 # 初始化最大缺陷数量为0 + max_total_defect_area = 0 # 初始化最大总像素数为0 + + for i, img in enumerate(images): + if cmd == 'TO': + # 番茄 + diameter, green_percentage, number_defects, total_pixels, rp = seif.analyze_tomato(img) + if i <= 2: + diameter_axis_list.append(diameter) + max_defect_num = max(max_defect_num, number_defects) + max_total_defect_area = max(max_total_defect_area, total_pixels) + if i == 1: + rp_result = rp + gp = round(green_percentage, 2) + + elif cmd == 'PF': + # 百香果 + diameter, weight, number_defects, total_pixels, rp = seif.analyze_passion_fruit(img) + if i <= 2: + diameter_axis_list.append(diameter) + max_defect_num = max(max_defect_num, number_defects) + max_total_defect_area = max(max_total_defect_area, total_pixels) + if i == 1: + rp_result = rp + weight = weight + + else: + logging.error(f'错误指令，指令为{cmd}') + return False + + diameter = round(sum(diameter_axis_list) / 3, 2) + + if cmd == 'TO': + brix = 0 + weight = 0 + # 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};') + response = pipe.send_data(cmd=cmd, brix=brix, diameter=diameter, green_percentage=gp, weight=weight, + defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result) + return response + elif cmd == 'PF': + green_percentage = 0 + brix = detector.predict(spec) + if diameter == 0: + brix = 0 + # 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};') + response = pipe.send_data(cmd=cmd, brix=brix, green_percentage=green_percentage, diameter=diameter, + weight=weight, + defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result) + return response + + # #下面封装的是ResNet18和ResNet34的网络模型构建 # #原定用于构建RGB图像有果无果判断，后续发现存在纰漏，暂时搁置并未实际使用 diff --git a/20240529RGBtest3/main.py b/20240529RGBtest3/main.py index bbab515..ca53002 100644 --- a/20240529RGBtest3/main.py +++ b/20240529RGBtest3/main.py @@ -18,68 +18,6 @@ import time -def process_data(cmd: str, images: list, spec: any, dp: Data_processing, pipe: Pipe, detector: Spec_predict) -> bool: - """ - 处理指令 - - :param cmd: 指令类型 - :param images: 图像数据列表 - :param spec: 光谱数据 - :param detector: 模型 - :return: 是否处理成功 - """ - diameter_axis_list = [] - max_defect_num = 0 # 初始化最大缺陷数量为0 - max_total_defect_area = 0 # 初始化最大总像素数为0 - - for i, img in enumerate(images): - if cmd == 'TO': - # 番茄 - diameter, green_percentage, number_defects, total_pixels, rp = dp.analyze_tomato(img) - if i <= 2: - diameter_axis_list.append(diameter) - max_defect_num = max(max_defect_num, number_defects) - max_total_defect_area = max(max_total_defect_area, total_pixels) - if i == 1: - rp_result = rp - gp = round(green_percentage, 2) - - elif cmd == 'PF': - # 百香果 - diameter, weight, number_defects, total_pixels, rp = dp.analyze_passion_fruit(img) - if i <= 2: - diameter_axis_list.append(diameter) - max_defect_num = max(max_defect_num, number_defects) - max_total_defect_area = max(max_total_defect_area, total_pixels) - if i == 1: - rp_result = rp - weight = weight - - else: - logging.error(f'错误指令，指令为{cmd}') - return False - - diameter = round(sum(diameter_axis_list) / 3, 2) - - if cmd == 'TO': - brix = 0 - weight = 0 - # 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};') - response = pipe.send_data(cmd=cmd, brix=brix, diameter=diameter, green_percentage=gp, weight=weight, - defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result) - return response - elif cmd == 'PF': - green_percentage = 0 - brix = detector.predict(spec) - if diameter == 0: - brix = 0 - # 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};') - response = pipe.send_data(cmd=cmd, brix=brix, green_percentage=green_percentage, diameter=diameter, weight=weight, - defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result) - return response - def main(is_debug=False): file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'tomato.log'), encoding='utf-8') file_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING) @@ -165,8 +103,8 @@ def main(is_debug=False): #数据处理部分 # start_time3 = time.time() if images: # 确保images不为空 - response = process_data(cmd, images, spec, dp, pipe, detector) - end_time3 = time.time() + response = dp.process_data(cmd, images, spec, pipe, detector) + # end_time3 = time.time() # print(f'第{q}组处理时间：{(end_time3 - start_time3) * 1000}毫秒') if response: logging.info(f'处理成功，响应为: {response}') diff --git a/20240529RGBtest3/utils.py b/20240529RGBtest3/utils.py index c454e54..fd7ecdc 100644 --- a/20240529RGBtest3/utils.py +++ b/20240529RGBtest3/utils.py @@ -241,9 +241,6 @@ class Pipe: return True - - - def create_file(file_name): """ 创建文件