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Martin Hope
rain snow
Asked: 2025-04-28 08:17:38 +0800 CST
  • 6

我有数据框，其中给出了 x 和 y 列，我想在它们之间绘制回归模型，主要想法是我应该使用 dash 框架，因为根据 chow 检验，两个回归模型在不同实例值下可能会有差异，基于以下链接：dash 模型

我写了下面的代码：

import pandas as pd
from dash import Dash,html,dcc,callback,Output,Input
from sklearn.linear_model import LinearRegression
import plotly.express as px
data =pd.read_csv("regression.csv")
model =LinearRegression()
print(data)
app = Dash()

# Requires Dash 2.17.0 or later
app.layout = [
    html.H1(children='Our regression Model', style={'textAlign':'center'}),
    dcc.Dropdown(data.Year.unique(), '2004', id='dropdown-selection'),
    dcc.Graph(id='graph-content')
]

@callback(
    Output('graph-content', 'figure'),
    Input('dropdown-selection', 'value')
)
def  scatter_graph(value):
    selected =data[data.Year==value]
    return px.scatter(selected,x='x',y='y')
@callback(
    Output('graph-content', 'figure'),
    Input('dropdown-selection', 'value')
)
def  Regression_graph(value):
    selected =data[data.Year==value]
    X =selected['x'].values
    X =X.reshape(-1,1)
    y =selected['y'].values
    model.fit(X,y)
    y_predicted =model.predict(X)
    return px.line(selected,x='x',y=y_predicted)

if __name__ =='__main__':
    app.run(debug=True)

这部分工作正常：

@callback(
    Output('graph-content', 'figure'),
    Input('dropdown-selection', 'value')
)
def  scatter_graph(value):
    selected =data[data.Year==value]
    return px.scatter(selected,x='x',y='y')

但是回归图的第二个装饰器不起作用，下面是示例： 在此处输入图片描述

请帮我解决该如何解决？

python
Martin Hope
rain snow
Asked: 2025-03-03 20:18:33 +0800 CST
  • 6

这是我使用 mediapipe 绘制地标的代码

import cv2
import time
import mediapipe as mp

mp_holistic = mp.solutions.holistic

holistic_model = mp_holistic.Holistic(
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5
)

# Initializing the drawing utils for drawing the facial landmarks on image
mp_drawing = mp.solutions.drawing_utils

# (0) in VideoCapture is used to connect to your computer's default camera
capture = cv2.VideoCapture(0)

# Initializing current time and precious time for calculating the FPS
previousTime = 0
currentTime = 0

while capture.isOpened():
    # capture frame by frame
    ret, frame = capture.read()

    # resizing the frame for better view
    frame = cv2.resize(frame, (800, 600))

    # Converting the from BGR to RGB
    image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # Making predictions using holistic model
    # To improve performance, optionally mark the image as not writeable to
    # pass by reference.
    image.flags.writeable = False
    results = holistic_model.process(image)
    image.flags.writeable = True

    # Converting back the RGB image to BGR
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

    # Drawing the Facial Landmarks
    # mp_drawing.draw_landmarks(
    #     image,
    #     results.face_landmarks,
    #     mp_holistic.FACEMESH_CONTOURS,
    #     mp_drawing.DrawingSpec(
    #         color=(255, 0, 255),
    #         thickness=1,
    #         circle_radius=1
    #     ),
    #     mp_drawing.DrawingSpec(
    #         color=(0, 255, 255),
    #         thickness=1,
    #         circle_radius=1
    #     )
    # )

    # Drawing Right hand Land Marks
    mp_drawing.draw_landmarks(
        image,
        results.right_hand_landmarks,
        mp_holistic.HAND_CONNECTIONS
    )
    for landmark in mp_holistic.HandLandmark:
        print(landmark,landmark.value)

    # Drawing Left hand Land Marks
    mp_drawing.draw_landmarks(
        image,
        results.left_hand_landmarks,
        mp_holistic.HAND_CONNECTIONS
    )

    # Calculating the FPS
    currentTime = time.time()
    fps = 1 / (currentTime - previousTime)
    previousTime = currentTime

    # Displaying FPS on the image
    cv2.putText(image, str(int(fps)) + " FPS", (10, 70), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)

    # Display the resulting image
    cv2.imshow("Facial and Hand Landmarks", image)

    # Enter key 'q' to break the loop
    if cv2.waitKey(5) & 0xFF == ord('q'):
        break

# When all the process is done
# Release the capture and destroy all windows
capture.release()
cv2.destroyAllWindows()

适用于左手和右手，但我的目标是：在手之间画直线，为了理解我的问题，让我们讨论以下图像：

1

从图中我们可以说，例如，双手之间的线应该是完全水平的（因此角度应该为零，就好像我们考虑以下方程：

2

很容易说，为了成为完美的水平线，y 坐标应该彼此相等，所以请帮我检测如何确定坐标以及如何在两点之间画线？由于 cv2.line 存在，绘制很容易，但是坐标呢？

python