Former-commit-id: 57a8fb7799ea7543b4af1ea49626346d50546f82
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2025-05-16 15:46:34 +08:00
parent a1a0392104
commit a6b9531df5
6 changed files with 215 additions and 13 deletions

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.gitattributes vendored
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# Auto detect text files and perform LF normalization
* text=auto
*.exe filter=lfs diff=lfs merge=lfs -text
*.pth filter=lfs diff=lfs merge=lfs -text
*.pkl filter=lfs diff=lfs merge=lfs -text

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*.exe filter=lfs diff=lfs merge=lfs -text
*.pth filter=lfs diff=lfs merge=lfs -text
*.pkl filter=lfs diff=lfs merge=lfs -text

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# 这是一个示例 Python 脚本。
import cv2
import numpy as np
from pathlib import Path
from train_2 import train_model,prepare_data,predict
import joblib
# 按 Shift+F10 执行或将其替换为您的代码。
# 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。
inp = Path("data/train")
val = Path("data/val")
proc = Path("data/proc")
proc.mkdir(exist_ok=True)
def print_hi(name):
# 在下面的代码行中使用断点来调试脚本。
print(f'Hi, {name}') # 按 Ctrl+F8 切换断点。
def preproc(dir:Path):
d={}
for file in dir.glob("*/*.jpg"):
im = cv2.imread(file)
if im is None:
print(f"Error reading image: {file}")
continue
cl = file.parents[0].name
if cl not in d:
d[cl] = []
hsv = cv2.cvtColor(im,cv2.COLOR_BGR2HSV)
mask = hsv[:,:,1] > 150
# cor = np.argwhere(mask)
# y_min,x_min = cor.min(axis=0)
# y_max,x_max = cor.max(axis=0)
mask = mask[:,:,np.newaxis]
# cv2.findCoun
# im = im[y_min:y_max,x_min:x_max]
cnt = np.count_nonzero(mask)
hsv*=mask
h = round(np.sum(hsv[:,:,0])/cnt)
s = round(np.sum(hsv[:,:,1])/cnt)
v = round(np.sum(hsv[:,:,2])/cnt)
name = f"{h}_{s}_{v}.jpg"
d[cl].append((h,s,v))
# (proc/cl).mkdir(exist_ok=True)
# cv2.imwrite(proc/cl/name,cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR))
return d
d:dict[str,list[tuple[int,int,int]]] = preproc(inp)
val:dict[str,list[tuple[int,int,int]]] = preproc(val)
# 按装订区域中的绿色按钮以运行脚本。
if __name__ == '__main__':
print_hi('PyCharm')
print("数据预处理完成")
# 访问 https://www.jetbrains.com/help/pycharm/ 获取 PyCharm 帮助
model, label_map = train_model(d)
print("训练完成")
joblib.dump(model, "model.pkl")
# model = joblib.load("model.pkl")
X_train, y_train = prepare_data(d)
print(predict(model, label_map, d))
print(predict(model, label_map, val))
# print(f"\n训练集准确率: {model.score(X_train, y_train):.5f}")
# X_train, y_train = prepare_data(val)
# print(f"\n训练集准确率: {model.score(X_train, y_train):.5f}")
# model.predict()
from src_predict import predictor
suc = cnt = 0
for file in Path("data/train").glob("*/*.jpg"):
cnt+=1
pcl,_=predictor(file)
acl = file.parents[0].name
if acl == pcl:
suc+=1
print(f"预测准确率: {suc/cnt:.4f}")

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Picture_Train/model.pkl Normal file
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version https://git-lfs.github.com/spec/v1
oid sha256:409653310924dc977ea2ae5dd46042ef144f4c8500c460ba5ca5b0f5ce68bed8
size 535673

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Picture_Train/show.py Normal file
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import matplotlib.pyplot as plt
import random
# 创建一个 3D 图形
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# 为每个 key 分配一个随机颜色
colors = {"orange":(1,0,0),"yellow":(0,1,0)}
# for key in d.keys():
# colors[key] = (random.random(), random.random(), random.random()) # 随机 RGB 颜色
# 绘制每个 key 的点
for key, points in d.items():
x_vals = [point[0] for point in points]
y_vals = [point[1] for point in points]
z_vals = [point[2] for point in points]
ax.scatter(x_vals, y_vals, z_vals, label=key, color=colors[key])
# 添加图例和标签
ax.set_xlabel('X轴')
ax.set_ylabel('Y轴')
ax.set_zlabel('Z轴')
ax.legend()
# 显示图形
plt.show()

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Picture_Train/train_2.py Normal file
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import numpy as np
from sklearn.ensemble import RandomForestClassifier
from typing import Dict, List, Tuple
import joblib
def prepare_data(data: Dict[str, List[np.ndarray]]) -> Tuple[np.ndarray, np.ndarray]:
"""
将dict[str, list[ndarray]]格式的数据转换为模型可用的特征矩阵和标签向量
参数:
data: 格式为 dict[str, list[ndarray]] 的数据,其中键为类别名,值为对应类别的特征数组列表
返回:
X: 特征矩阵
y: 标签向量
"""
features = []
labels = []
# 为每个类别分配一个数字标签
label_map = {class_name: i for i, class_name in enumerate(data.keys())}
for class_name, arrays_list in data.items():
label = label_map[class_name]
for arr in arrays_list:
# 处理每个数组中的每个样本
features.append(np.array(arr))
labels.append(label)
# if len(arr.shape) > 1:
# for sample in arr:
# features.append(sample)
# labels.append(label)
# else:
# # 处理单个样本的情况
# features.append(arr)
# labels.append(label)
return np.array(features), np.array(labels)
def train_model(data: Dict[str, List[np.ndarray]]):
"""
训练分类模型
参数:
data: 训练数据,格式为 dict[str, list[ndarray]]
返回:
训练好的模型和标签映射字典
"""
X, y = prepare_data(data)
# 创建并训练模型
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X, y)
# 创建逆向映射,用于将数字标签转回类别名
label_map = {i: class_name for i, class_name in enumerate(data.keys())}
return model, label_map
def predict(model, label_map: Dict[int, str], val_data: Dict[str, List[np.ndarray]]) -> Dict[str, List[List[str]]]:
"""
使用训练好的模型对验证数据进行预测
参数:
model: 训练好的模型
label_map: 标签映射字典,用于将数字标签转换回类别名
val_data: 验证数据,格式为 dict[str, list[ndarray]]
返回:
预测结果字典,格式为 dict[str, list[list[str]]],表示每个输入数组中样本的预测类别
"""
results = {}
suc = 0
cnt = 0
for class_name, arrays_list in val_data.items():
class_predictions = []
for arr in arrays_list:
# 确保数据格式正确
arr = np.array(arr)
cnt+=1
if len(arr.shape) == 1:
arr = arr.reshape(1, -1)
# 进行预测并转换为类别名
pred_labels = model.predict(arr)
pred_classes = [label_map[label] for label in pred_labels]
if len(pred_classes) > 1:continue
if class_name==pred_classes[0]:
suc+=1
# class_predictions.append(pred_classes)
results[class_name] = class_predictions
return suc/cnt
if __name__ == "__main__":
exit()
# 训练模型
model, label_map = train_model(d)
print("训练完成")
joblib.dump(model, "model.pkl")
# 在验证数据上进行预测
# predictions = predict(model, label_map, val)
# 输出预测结果
# print("预测结果:")
# for class_name, class_preds in predictions.items():
# print(f"{class_name}:")
# for i, arr_preds in enumerate(class_preds):
# print(f" 数组 {i}: {arr_preds}")
# 输出模型性能评估
# X_train, y_train = prepare_data(val)
# print(f"\n训练集准确率: {model.score(X_train, y_train):.4f}")