OpenCV 示例

主要介绍 OpenCV 图片的预览、摄像头调用以及视觉玩法示例。

图像预览

演示如何读取图片和摄像头画面并显示。

图片预览

读取图片并显示出来。

运行案例步骤：

1. 新建 preview_image.py 文件
2. 将代码复制到 preview_image.py 文件
3. 使用 python3 preview_image.py 运行案例
4. 鼠标点击预览窗口，按 q 键终止程序

preview_image.py

#!/usr/bin/env python3
# -_- encoding: utf-8 -_-

import cv2

# Read the image

image_path = './radxa_logo.png' # Replace with your image path
image = cv2.imread(image_path)

# Check if the image is loaded successfully

if image is None:
print("Failed to load image. Please check the file path.")
else: # Display the image in a window
while True:
cv2.imshow('Preview', image) # Wait for 'q' key to quit
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()

预览效果：

实时预览

读取摄像头画面并显示。

运行案例步骤：

1. 新建 preview_camera.py 文件
2. 将代码复制到 preview_camera.py 文件
3. 使用 python3 preview_camera.py 运行案例
4. 鼠标点击预览窗口，按 q 键终止程序

提示

其中代码中的 cv2.VideoCapture(0) 表示打开 /dev/video0 摄像头，若你有多个摄像头或者摄像头设备文件不为 /dev/video0，请根据实际情况修改代码。

preview_camera.py

#!/usr/bin/env python3

# -_- encoding: utf-8 -_-

import cv2

# Open the camera

video_capture = cv2.VideoCapture(0)

while True: # Read a frame from the camera
ret, frame = video_capture.read()

    # Check if the frame is read successfully
    if not ret:
        print("Failed to read frame. Please check the camera connection.")
        break

    # Display the frame in a window
    cv2.imshow('Preview', frame)

    # Wait for 'q' key to quit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the camera and close the window

video_capture.release()
cv2.destroyAllWindows()

预览效果：

Harris 角点检测

Harris 角点检测（Harris Corner Detection）是一种经典的图像特征点检测算法，用于检测图像中的角点。

图片识别

读取图片并进行 Harris 角点检测。

运行案例步骤：

1. 新建 harris_image.py 文件
2. 将代码复制到 harris_image.py 文件
3. 使用 python3 harris_image.py 运行案例
4. 鼠标点击预览窗口，按 q 键终止程序

harris_image.py

#!/usr/bin/env python3

# -_- encoding: utf-8 -_-

import cv2
import numpy as np

# Harris corner detection parameters

block_size = 2
ksize = 3
k = 0.04
threshold = 0.01

# Read the image

image_path = './radxa_logo.png' # Replace with your image path
image = cv2.imread(image_path)

# Check if the image is loaded successfully

if image is None:
print("Failed to load image. Please check the file path.")
else: # Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)

    # Apply Harris corner detection
    dst = cv2.cornerHarris(gray, block_size, ksize, k)

    # Dilate corner points for better visualization
    dst = cv2.dilate(dst, None)

    # Mark corners in red
    image[dst > threshold * dst.max()] = [0, 0, 255]

    # Display the result
    while True:
        cv2.imshow('Harris Corners', image)
        # Wait for 'q' key to quit
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

cv2.destroyAllWindows()

预览效果：

实时检测

读取摄像头画面并进行 Harris 角点检测。

运行案例步骤：

1. 新建 harris_camera.py 文件
2. 将代码复制到 harris_camera.py 文件
3. 使用 python3 harris_camera.py 运行案例
4. 鼠标点击预览窗口，按 q 键终止程序

提示

其中代码中的 cv2.VideoCapture(0) 表示打开 /dev/video0 摄像头，若你有多个摄像头或者摄像头设备文件不为 /dev/video0，请根据实际情况修改代码。

harris_camera.py

#!/usr/bin/env python3

# -_- encoding: utf-8 -_-

import cv2
import numpy as np

# Harris corner detection parameters

block_size = 2
ksize = 3
k = 0.04
threshold = 0.01

# Initialize the camera

cap = cv2.VideoCapture(0)

# Check if the camera is opened successfully

if not cap.isOpened():
print("Error: Could not open camera.")
exit()

while True: # Capture frame-by-frame
ret, frame = cap.read()

    # If frame is read correctly, ret is True
    if not ret:
        print("Error: Failed to capture frame.")
        break

    # Create a copy of the frame for processing
    display_frame = frame.copy()

    # Convert to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray = np.float32(gray)

    # Apply Harris corner detection
    dst = cv2.cornerHarris(gray, block_size, ksize, k)

    # Dilate corner points for better visualization
    dst = cv2.dilate(dst, None)

    # Mark corners in red
    display_frame[dst > threshold * dst.max()] = [0, 0, 255]

    # Display the resulting frame
    cv2.imshow('Harris Corners - Camera', display_frame)

    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything done, release the capture and close windows

cap.release()
cv2.destroyAllWindows()

预览效果：

Shi-Tomasi 角点检测

Shi-Tomasi 角点检测（Shi-Tomasi Corner Detection）是 Harris 角点检测的改进算法，用于检测图像中的角点。

图片识别

读取图片并进行 Shi-Tomasi 角点检测。

运行案例步骤：

1. 新建 shi_tomasi_image.py 文件
2. 将代码复制到 shi_tomasi_image.py 文件
3. 使用 python3 shi_tomasi_image.py 运行案例
4. 鼠标点击预览窗口，按 q 键终止程序

shi_tomasi_image.py

#!/usr/bin/env python3

# -_- encoding: utf-8 -_-

import cv2
import numpy as np

# Shi-Tomasi corner detection parameters

max_corners = 100
quality_level = 0.01
min_distance = 10

# Read the image

image_path = './radxa_logo.png' # Replace with your image path
image = cv2.imread(image_path)

# Check if the image is loaded successfully

if image is None:
print("Failed to load image. Please check the file path.")
else: # Create a copy of the image for display
display_image = image.copy()

    # Convert to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Detect corners using Shi-Tomasi
    corners = cv2.goodFeaturesToTrack(gray, max_corners, quality_level, min_distance)

    # Draw circles around detected corners
    if corners is not None:
        corners = np.int0(corners)
        for corner in corners:
            x, y = corner.ravel()
            cv2.circle(display_image, (x, y), 5, (0, 255, 0), -1)  # Green circles

    # Display the result
    while True:
        cv2.imshow('Shi-Tomasi Corners', display_image)
        # Wait for 'q' key to quit
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

cv2.destroyAllWindows()

预览效果：

实时检测

读取摄像头画面并进行 Shi-Tomasi 角点检测。

运行案例步骤：

1. 新建 shi_tomasi_camera.py 文件
2. 将代码复制到 shi_tomasi_camera.py 文件
3. 使用 python3 shi_tomasi_camera.py 运行案例
4. 鼠标点击预览窗口，按 q 键终止程序

提示

其中代码中的 cv2.VideoCapture(0) 表示打开 /dev/video0 摄像头，若你有多个摄像头或者摄像头设备文件不为 /dev/video0，请根据实际情况修改代码。

shi_tomasi_camera.py

#!/usr/bin/env python3

# -_- encoding: utf-8 -_-

import cv2
import numpy as np

# Shi-Tomasi corner detection parameters

max_corners = 100
quality_level = 0.01
min_distance = 10

# Initialize the camera

cap = cv2.VideoCapture(0)

# Check if the camera is opened successfully

if not cap.isOpened():
print("Error: Could not open camera.")
exit()

while True: # Capture frame-by-frame
ret, frame = cap.read()

    # If frame is read correctly, ret is True
    if not ret:
        print("Error: Failed to capture frame.")
        break

    # Create a copy of the frame for display
    display_frame = frame.copy()

    # Convert to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect corners using Shi-Tomasi
    corners = cv2.goodFeaturesToTrack(gray, max_corners, quality_level, min_distance)

    # Draw circles around detected corners
    if corners is not None:
        corners = np.int0(corners)
        for corner in corners:
            x, y = corner.ravel()
            cv2.circle(display_frame, (x, y), 5, (0, 255, 0), -1)  # Green circles

    # Display the resulting frame
    cv2.imshow('Shi-Tomasi Corners - Camera', display_frame)

    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything done, release the capture and close windows

cap.release()
cv2.destroyAllWindows()

预览效果：

ORB 特征检测

ORB（Oriented FAST and Rotated BRIEF）是一种高效的特征检测与描述算法，结合了 FAST 关键点检测器 和 BRIEF 描述符，并进行了改进以提升性能，尤其在计算效率和旋转不变性方面表现突出。

图片识别

读取图片并进行 ORB 特征检测。

运行案例步骤：

1. 新建 orb_image.py 文件
2. 将代码复制到 orb_image.py 文件
3. 使用 python3 orb_image.py 运行案例
4. 鼠标点击预览窗口，按 q 键终止程序

orb_image.py

#!/usr/bin/env python3

# -_- encoding: utf-8 -_-

import cv2
import numpy as np

# Read the image

image_path = './radxa_logo.png' # Replace with your image path
image = cv2.imread(image_path)

# Check if the image is loaded successfully

if image is None:
print("Failed to load image. Please check the file path.")
else: # Create a copy of the image for display
display_image = image.copy()

    # Convert to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Initialize ORB detector
    orb = cv2.ORB_create(nfeatures=500)

    # Detect keypoints and compute descriptors
    keypoints, descriptors = orb.detectAndCompute(gray, None)

    # Draw keypoints on the image
    display_image = cv2.drawKeypoints(image, keypoints, None, color=(0, 255, 0),
                                    flags=cv2.DRAW_MATCHES_FLAGS_DEFAULT)

    # Display the result
    while True:
        cv2.imshow('ORB Features', display_image)
        # Wait for 'q' key to quit
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

cv2.destroyAllWindows()

预览效果：

实时检测

读取摄像头画面并进行 ORB 特征检测。

运行案例步骤：

1. 新建 orb_camera.py 文件
2. 将代码复制到 orb_camera.py 文件
3. 使用 python3 orb_camera.py 运行案例
4. 鼠标点击预览窗口，按 q 键终止程序

提示

其中代码中的 cv2.VideoCapture(0) 表示打开 /dev/video0 摄像头，若你有多个摄像头或者摄像头设备文件不为 /dev/video0，请根据实际情况修改代码。

orb_camera.py

#!/usr/bin/env python3

# -_- encoding: utf-8 -_-

import cv2
import numpy as np

# Initialize ORB detector

orb = cv2.ORB_create(nfeatures=500)

# Initialize the camera

cap = cv2.VideoCapture(0)

# Check if the camera is opened successfully

if not cap.isOpened():
print("Error: Could not open camera.")
exit()

while True: # Capture frame-by-frame
ret, frame = cap.read()

    # If frame is read correctly, ret is True
    if not ret:
        print("Error: Failed to capture frame.")
        break

    # Convert to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect keypoints and compute descriptors
    keypoints, descriptors = orb.detectAndCompute(gray, None)

    # Draw keypoints on the frame
    display_frame = cv2.drawKeypoints(frame, keypoints, None,
                                    color=(0, 255, 0),
                                    flags=cv2.DRAW_MATCHES_FLAGS_DEFAULT)

    # Display the number of keypoints
    cv2.putText(display_frame, f'Keypoints: {len(keypoints)}', (10, 30),
                cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

    # Display the resulting frame
    cv2.imshow('ORB Features - Camera', display_frame)

    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything done, release the capture and close windows

cap.release()
cv2.destroyAllWindows()

预览效果：

人脸识别

基于 Haar Cascade 分类器实现的人脸检测功能，其核心原理是通过机器学习训练的级联分类器在图像中快速定位人脸区域。

图片识别

读取图片并进行人脸识别。

运行案例步骤：

1. 新建 face_recognition_image.py 文件
2. 将代码复制到 face_recognition_image.py 文件
3. 使用 python3 face_recognition_image.py 运行案例
4. 鼠标点击预览窗口，按 q 键终止程序

face_recognition_image.py

#!/usr/bin/env python3

# -_- encoding: utf-8 -_-

import cv2
import os

# Load the pre-trained Haar Cascade classifier for face detection

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Read the image

image_path = 'people.png' # Replace with your image path
image = cv2.imread(image_path)

# Check if the image is loaded successfully

if image is None:
print(f"Error: Could not read image at {image_path}")
exit()

# Convert to grayscale (face detection works on grayscale images)

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Detect faces in the image

faces = face_cascade.detectMultiScale(
gray,
scaleFactor=1.1, # Scale factor for image pyramid
minNeighbors=5, # How many neighbors each candidate rectangle should have
minSize=(30, 30) # Minimum possible object size
)

# Draw rectangles around the faces

for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)

# Display the number of faces detected

print(f"Number of faces detected: {len(faces)}")

# Display the result

while True:
cv2.imshow('Face Detection', image) # Wait for 'q' key to quit
if cv2.waitKey(1) & 0xFF == ord('q'):
break

cv2.destroyAllWindows()

预览效果：

实时检测

读取摄像头画面并进行人脸识别。

运行案例步骤：

1. 新建 face_recognition_camera.py 文件
2. 将代码复制到 face_recognition_camera.py 文件
3. 使用 python3 face_recognition_camera.py 运行案例
4. 鼠标点击预览窗口，按 q 键终止程序

提示

其中代码中的 cv2.VideoCapture(0) 表示打开 /dev/video0 摄像头，若你有多个摄像头或者摄像头设备文件不为 /dev/video0，请根据实际情况修改代码。

face_recognition_camera.py

#!/usr/bin/env python3

# -_- encoding: utf-8 -_-

import cv2

# Load the pre-trained Haar Cascade classifier for face detection

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Initialize the camera

cap = cv2.VideoCapture(0)

# Check if the camera is opened successfully

if not cap.isOpened():
print("Error: Could not open camera.")
exit()

while True: # Capture frame-by-frame
ret, frame = cap.read()

    # If frame is read correctly, ret is True
    if not ret:
        print("Error: Failed to capture frame.")
        break

    # Convert to grayscale (face detection works on grayscale images)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the frame
    faces = face_cascade.detectMultiScale(
        gray,
        scaleFactor=1.1,  # Scale factor for image pyramid
        minNeighbors=5,   # How many neighbors each candidate rectangle should have
        minSize=(30, 30)  # Minimum possible object size
    )

    # Draw rectangles around the faces
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

    # Display the number of faces detected
    cv2.putText(frame, f'Faces: {len(faces)}', (10, 30),
               cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

    # Display the resulting frame
    cv2.imshow('Face Detection - Camera', frame)

    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything done, release the capture and close windows

cap.release()
cv2.destroyAllWindows()

预览效果：