计算机视觉算法中的视频分割（Video Segmentation）

pythonCopy codeimport cv2
import numpy as np
import tensorflow as tf
from mrcnn import utils
from mrcnn import model as modellib
from mrcnn import visualize
# 加载预训练的Mask R-CNN模型
MODEL_DIR = "path/to/model/directory"
config = utils.Config()
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
model.load_weights("path/to/model/weights.h5", by_name=True)
# 定义类别标签
class_names = ['background', 'object1', 'object2', ...]
# 打开视频文件
video_path = "path/to/video/file"
cap = cv2.VideoCapture(video_path)
# 逐帧进行视频分割
while True:
    ret, frame = cap.read()
    if not ret:
        break
    # 调整图像尺寸
    frame = cv2.resize(frame, (config.IMAGE_SHAPE[1], config.IMAGE_SHAPE[0]))
    # 对图像进行预处理
    molded_images, image_metas, windows = model.mold_inputs([frame])
    # 执行分割
    results = model.detect([molded_images], verbose=0)
    r = results[0]
    # 可视化分割结果
    masked_frame = visualize.display_instances(frame, r['rois'], r['masks'], r['class_ids'], class_names, r['scores'])
    cv2.imshow('Video Segmentation', masked_frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

pythonCopy codeimport cv2
import numpy as np
import time
# 加载预训练的行人检测模型
model_path = "path/to/model/weights.h5"
net = cv2.dnn.readNetFromDarknet(model_path)
# 加载类别标签
class_names = ["person"]
# 打开视频文件
video_path = "path/to/video/file"
cap = cv2.VideoCapture(video_path)
# 设置参数
confidence_threshold = 0.5
nms_threshold = 0.3
while True:
    ret, frame = cap.read()
    if not ret:
        break
    # 执行行人检测
    blob = cv2.dnn.blobFromImage(frame, 1/255, (416, 416), swapRB=True, crop=False)
    net.setInput(blob)
    layer_names = net.getLayerNames()
    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    outs = net.forward(output_layers)
    # 解析检测结果
    boxes = []
    confidences = []
    class_ids = []
    height, width, _ = frame.shape
    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > confidence_threshold and class_names[class_id] == "person":
                center_x = int(detection[0] * width)
                center_y = int(detection[1] * height)
                w = int(detection[2] * width)
                h = int(detection[3] * height)
                x = int(center_x - w / 2)
                y = int(center_y - h / 2)
                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)
    # 应用非极大值抑制
    indexes = cv2.dnn.NMSBoxes(boxes, confidences, confidence_threshold, nms_threshold)
    # 在图像上绘制边界框和标签
    font = cv2.FONT_HERSHEY_SIMPLEX
    for i in range(len(boxes)):
        if i in indexes:
            x, y, w, h = boxes[i]
            label = class_names[class_ids[i]]
            confidence = confidences[i]
            color = (0, 255, 0)
            cv2.rectangle(frame, (x, y), (x+w, y+h), color, 2)
            cv2.putText(frame, f"{label}: {confidence:.2f}", (x, y-10), font, 0.5, color, 1)
    # 显示结果
    cv2.imshow("Smart Surveillance", frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()