如何在Python OpenCV中基于模型预测概率绘制情绪识别仪表盘

直接通过OpenCV内置绘图接口即可实现你要的半圆仪表盘效果，无需引入额外依赖，可直接调整参数适配你的画布尺寸，修改后的完整代码如下：

import cv2
import numpy as np

# 此处保留你原本的模型加载、emotion_dict定义逻辑
# emotion_dict = {0: 'Angry', 1: 'Disgusted', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprised', 6: 'Neutral'}
# model = 你训练好的情绪识别模型

# 仪表盘参数配置，可自行调整位置、大小
GAUGE_CENTER = (200, 400)  # 仪表盘在canvasImage上的中心坐标
GAUGE_RADIUS = 150  # 仪表盘半径
GAUGE_START_ANGLE = 180  # 半圆起始角度，对应0%概率
GAUGE_END_ANGLE = 360  # 半圆结束角度，对应100%概率

cap = cv2.VideoCapture(1)
canvasImage = cv2.imread("fg2.png")
x0, x1 = 330, 1290
y0, y1 = 155, 700

prediction_history = []
LOOKBACK = 5  # 历史预测帧回溯长度

while True:
    ret, frame = cap.read()
    frame=cv2.flip(frame,3)
    if not ret:
        break
    facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = facecasc.detectMultiScale(gray,scaleFactor=1.3, minNeighbors=5)
    current_prob = 0
    current_text = "No face detected"

    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (255, 0, 0), 2)
        roi_gray = gray[y:y + h, x:x + w]
        cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray, (48, 48)), -1), 0)
        prediction = model.predict(cropped_img, verbose=0)  # 加verbose=0关闭预测日志输出
        
        maxindex = int(np.argmax(prediction))
        current_prob = round(prediction[0][3]*100, 2)
        
        prediction_history.append(maxindex)
        most_common_index = max(set(prediction_history[-LOOKBACK:][::-1]), key = prediction_history.count)
        current_text = emotion_dict[most_common_index]
        
        if ("Happy" in current_text) or ("Sad" in current_text):
            cv2.putText(frame, current_text+": "+str(current_prob), (x+20, y-60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
  
    frame_shrunk = cv2.resize(frame, (x1 - x0, y1 - y0))
    canvasImage[y0:y1, x0:x1] = frame_shrunk

    # --------------- 新增仪表盘绘制逻辑 ---------------
    # 1. 绘制半圆仪表盘底座
    cv2.ellipse(canvasImage, GAUGE_CENTER, (GAUGE_RADIUS, GAUGE_RADIUS), 0, GAUGE_START_ANGLE, GAUGE_END_ANGLE, (200,200,200), 3)
    # 2. 绘制刻度标记（0%、50%、100%）
    for percent in [0, 50, 100]:
        angle = GAUGE_START_ANGLE + (percent/100)*(GAUGE_END_ANGLE - GAUGE_START_ANGLE)
        rad = np.deg2rad(angle)
        # 刻度线外端点
        outer_x = int(GAUGE_CENTER[0] + GAUGE_RADIUS * np.cos(rad))
        outer_y = int(GAUGE_CENTER[1] + GAUGE_RADIUS * np.sin(rad))
        # 刻度线内端点
        inner_x = int(GAUGE_CENTER[0] + GAUGE_RADIUS * 0.85 * np.cos(rad))
        inner_y = int(GAUGE_CENTER[1] + GAUGE_RADIUS * 0.85 * np.sin(rad))
        cv2.line(canvasImage, (outer_x, outer_y), (inner_x, inner_y), (200,200,200), 2)
        # 刻度数值
        text_x = int(GAUGE_CENTER[0] + GAUGE_RADIUS * 1.1 * np.cos(rad)) - 15
        text_y = int(GAUGE_CENTER[1] + GAUGE_RADIUS * 1.1 * np.sin(rad)) + 5
        cv2.putText(canvasImage, f"{percent}%", (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255,255,255), 2)
    # 3. 绘制指针
    pointer_angle = GAUGE_START_ANGLE + (current_prob/100)*(GAUGE_END_ANGLE - GAUGE_START_ANGLE)
    pointer_rad = np.deg2rad(pointer_angle)
    pointer_end_x = int(GAUGE_CENTER[0] + GAUGE_RADIUS * 0.8 * np.cos(pointer_rad))
    pointer_end_y = int(GAUGE_CENTER[1] + GAUGE_RADIUS * 0.8 * np.sin(pointer_rad))
    cv2.line(canvasImage, GAUGE_CENTER, (pointer_end_x, pointer_end_y), (0,0,255), 3)
    # 4. 绘制仪表盘中心圆点和当前情绪+概率文字
    cv2.circle(canvasImage, GAUGE_CENTER, 8, (0,0,255), -1)
    cv2.putText(canvasImage, f"{current_text}: {current_prob}%", (GAUGE_CENTER[0] - 80, GAUGE_CENTER[1] + 50), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 2)
    # -------------------------------------------------

    cv2.imshow('Demo', canvasImage)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

你可以根据自己的画布布局调整GAUGE_CENTER和GAUGE_RADIUS参数修改仪表盘的位置和大小，也可以修改绘图时的颜色参数自定义样式。

内容的提问来源于stack exchange，提问作者angel_30