import cv2
# 創(chuàng)建VideoCapture對象
cap = cv2.VideoCapture('video.mp4')  # 傳入視頻文件路徑
# 檢查視頻是否成功打開
if not cap.isOpened():
    print("Error: 無法打開視頻文件")
    exit()
# 循環(huán)讀取視頻幀
while True:
    ret, frame = cap.read()
    # 如果視頻結(jié)束或出現(xiàn)錯誤，退出循環(huán)
    if not ret:
        break
    # 顯示每一幀
    cv2.imshow('Video Frame', frame)
    # 按下'q'鍵退出播放
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break
# 釋放資源
cap.release()
cv2.destroyAllWindows()

while True:
    ret, frame = cap.read()
    if not ret:
        break
    # 轉(zhuǎn)換為灰度圖像
    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    # 顯示灰度幀
    cv2.imshow('Gray Video Frame', gray_frame)
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

while True:
    ret, frame = cap.read()
    if not ret:
        break
    # 轉(zhuǎn)換為灰度圖像
    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    # 應(yīng)用Canny邊緣檢測
    edges = cv2.Canny(gray_frame, 100, 200)
    # 顯示邊緣檢測結(jié)果
    cv2.imshow('Edges', edges)
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

# 創(chuàng)建背景減除器
fgbg = cv2.createBackgroundSubtractorMOG2()
while True:
    ret, frame = cap.read()
    if not ret:
        break
    # 應(yīng)用背景減除
    fgmask = fgbg.apply(frame)
    # 顯示背景減除結(jié)果
    cv2.imshow('Foreground Mask', fgmask)
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

# 降低視頻分辨率
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

import cv2.cuda as cuda
# 使用CUDA加速某些操作
gpu_frame = cuda_GpuMat()
gpu_frame.upload(frame)
gpu_gray = cuda.cvtColor(gpu_frame, cv2.COLOR_BGR2GRAY)

import cv2
# 創(chuàng)建VideoCapture對象
cap = cv2.VideoCapture('input_video.mp4')
# 獲取視頻的寬度、高度和幀率
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
# 創(chuàng)建VideoWriter對象
output_filename = 'output_video.avi'
fourcc = cv2.VideoWriter_fourcc(*'XVID')  # 使用XVID編碼
out = cv2.VideoWriter(output_filename, fourcc, fps, (frame_width, frame_height))
while True:
    ret, frame = cap.read()
    if not ret:
        break
    # 在此處添加處理代碼，例如灰度轉(zhuǎn)換、邊緣檢測等
    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    colored_frame = cv2.cvtColor(gray_frame, cv2.COLOR_GRAY2BGR)  # 轉(zhuǎn)換回三通道以便保存
    # 將處理后的幀寫入輸出視頻
    out.write(colored_frame)
    cv2.imshow('Processed Video', colored_frame)
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break
# 釋放資源
cap.release()
out.release()
cv2.destroyAllWindows()

# 使用H264編碼
fourcc = cv2.VideoWriter_fourcc(*'H264')
out = cv2.VideoWriter('output_h264.mp4', fourcc, fps, (frame_width, frame_height))

import cv2
import numpy as np
# 加載預(yù)訓(xùn)練的YOLO模型
net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
while True:
    ret, frame = cap.read()
    if not ret:
        break
    # 預(yù)處理幀以適應(yīng)YOLO模型
    blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
    net.setInput(blob)
    # 前向傳播以獲得檢測結(jié)果
    outs = net.forward(output_layers)
    # 分析檢測結(jié)果并繪制邊界框
    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5:
                center_x = int(detection[0] * frame_width)
                center_y = int(detection[1] * frame_height)
                w = int(detection[2] * frame_width)
                h = int(detection[3] * frame_height)
                x = int(center_x - w / 2)
                y = int(center_y - h / 2)
                # 繪制檢測到的對象的邊界框
                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
                text = f'{class_id}: {int(confidence * 100)}%'
                cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
    cv2.imshow('Object Detection', frame)
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

# 創(chuàng)建KCF跟蹤器
tracker = cv2.TrackerKCF_create()
# 初始化跟蹤器
ret, frame = cap.read()
bbox = cv2.selectROI('Frame', frame, False)
tracker.init(frame, bbox)
while True:
    ret, frame = cap.read()
    if not ret:
        break
    # 更新跟蹤器
    ret, bbox = tracker.update(frame)
    if ret:
        x, y, w, h = [int(v) for v in bbox]
        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
    else:
        cv2.putText(frame, 'Tracking Failure', (100, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
    cv2.imshow('Multi-Object Tracking', frame)
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

import cv2
import numpy as np
# 讀取視頻
cap = cv2.VideoCapture('shaky_video.mp4')
# 用于保存處理后的視頻
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('stabilized_video.avi', fourcc, fps, (frame_width, frame_height))
# 獲取第一幀
ret, prev_frame = cap.read()
prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
# 初始化轉(zhuǎn)換矩陣
transforms = []
while True:
    ret, frame = cap.read()
    if not ret:
        break
    # 轉(zhuǎn)換為灰度圖
    curr_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    # 計(jì)算光流
    prev_pts = cv2.goodFeaturesToTrack(prev_gray, maxCorners=200, qualityLevel=0.01, minDistance=30, blockSize=3)
    curr_pts, status, err = cv2.calcOpticalFlowPyrLK(prev_gray, curr_gray, prev_pts, None)
    # 過濾有效點(diǎn)
    valid_prev_pts = prev_pts[status == 1]
    valid_curr_pts = curr_pts[status == 1]
    # 計(jì)算變換矩陣
    matrix = cv2.estimateAffinePartial2D(valid_prev_pts, valid_curr_pts)[0]
    transforms.append(matrix)
    # 將當(dāng)前幀應(yīng)用到轉(zhuǎn)換矩陣上
    stabilized_frame = cv2.warpAffine(frame, matrix, (frame_width, frame_height))
    out.write(stabilized_frame)
    cv2.imshow('Stabilized Video', stabilized_frame)
    prev_gray = curr_gray
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break
# 釋放資源
cap.release()
out.release()
cv2.destroyAllWindows()

ffmpeg -i input_video.mov -vcodec libx264 output_video.mp4

cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

fourcc = cv2.VideoWriter_fourcc(*'H264')
out = cv2.VideoWriter('high_quality_output.mp4', fourcc, 20, (frame_width, frame_height))

ffmpeg -i input_video.mp4 -b:v 2M output_video.mp4

cap.release()
out.release()
cv2.destroyAllWindows()

import cv2
def detect_objects_and_alert(frame, net, output_layers, confidence_threshold=0.5):
    blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
    net.setInput(blob)
    outs = net.forward(output_layers)
    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > confidence_threshold:
                center_x = int(detection[0] * frame_width)
                center_y = int(detection[1] * frame_height)
                w = int(detection[2] * frame_width)
                h = int(detection[3] * frame_height)
                x = int(center_x - w / 2)
                y = int(center_y - h / 2)
                # 繪制邊界框
                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
                # 發(fā)出報(bào)警（可以是聲音、發(fā)送信息等）
                print("Object detected: Alert!")
    return frame
# 主循環(huán)
while True:
    ret, frame = cap.read()
    if not ret:
        break
    processed_frame = detect_objects_and_alert(frame, net, output_layers)
    cv2.imshow('Security Monitoring', processed_frame)
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

import cv2
import numpy as np
def detect_lane(frame):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    edges = cv2.Canny(blur, 50, 150)
    # 定義感興趣區(qū)域
    height, width = edges.shape
    mask = np.zeros_like(edges)
    triangle = np.array([[(0, height), (width, height), (width // 2, height // 2)]])
    cv2.fillPoly(mask, triangle, 255)
    masked_edges = cv2.bitwise_and(edges, mask)
    # Hough變換檢測車道線
    lines = cv2.HoughLinesP(masked_edges, 1, np.pi/180, threshold=100, minLineLength=40, maxLineGap=5)
    if lines is not None:
        for line in lines:
            x1, y1, x2, y2 = line[0]
            cv2.line(frame, (x1, y1), (x2, y2), (255, 0, 0), 5)
    return frame
# 主循環(huán)
while True:
    ret, frame = cap.read()
    if not ret:
        break
    lane_frame = detect_lane(frame)
    cv2.imshow('Lane Detection', lane_frame)
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

import cv2
# 加載人臉檢測器和人臉識別模型
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('face_trainer.yml')
def recognize_face(frame, face_cascade, recognizer):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
    for (x, y, w, h) in faces:
        roi_gray = gray[y:y+h, x:x+w]
        id_, confidence = recognizer.predict(roi_gray)
        if confidence >= 45:  # 根據(jù)實(shí)際情況調(diào)整閾值
            name = 'User'  # 從數(shù)據(jù)庫獲取名字
            cv2.putText(frame, name, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 
0, 0), 2)
            # 執(zhí)行身份驗(yàn)證通過后的操作
            print("Access Granted!")
        else:
            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
            print("Access Denied!")
    return frame
# 主循環(huán)
while True:
    ret, frame = cap.read()
    if not ret:
        break
    recognized_frame = recognize_face(frame, face_cascade, recognizer)
    cv2.imshow('Face Recognition', recognized_frame)
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break