multiObjTracking/multi_object_tracking_slow.py

import tkinter.filedialog as tkinter
import numpy as np
import argparse
import cv2
from utils import FPS


# 图像增强
def enhance_image(frame):
    # 直方图均衡化
    frame_yuv = cv2.cvtColor(frame, cv2.COLOR_BGR2YUV)
    frame_yuv[:, :, 0] = cv2.equalizeHist(frame_yuv[:, :, 0])
    frame = cv2.cvtColor(frame_yuv, cv2.COLOR_YUV2BGR)

    # 锐化
    sharpen_kernel = np.array([[-1, -1, -1],
                               [-1, 9, -1],
                               [-1, -1, -1]])
    sharpened_frame = cv2.filter2D(frame, -1, sharpen_kernel)

    # 对比度增强
    alpha = 1.5  # 控制对比度（1.0表示不变）
    enhanced_frame = cv2.convertScaleAbs(sharpened_frame, alpha=alpha, beta=0)

    # 亮度调整
    beta = 30  # 控制亮度调整量
    enhanced_frame = cv2.convertScaleAbs(enhanced_frame, alpha=1.0, beta=beta)

    return enhanced_frame




# 参数
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", default="mobilenet_ssd/MobileNetSSD_deploy.prototxt",
                help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", default="mobilenet_ssd/MobileNetSSD_deploy.caffemodel",
                help="path to Caffe pre-trained model")
ap.add_argument("-v", "--video", default=None,
                help="path to input video file")
ap.add_argument("-o", "--output", type=str,
                help="path to optional output video file")
ap.add_argument("-c", "--confidence", type=float, default=0.3,
                help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

# SSD标签
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
           "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
           "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
           "sofa", "train", "tvmonitor"]

# 读取网络模型
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# 初始化
if args["video"] is None:
    video_path = tkinter.askopenfilename(filetypes=[("视频文件", "*.mp4")])
    print("[INFO] starting video stream...")
    vs = cv2.VideoCapture(video_path)
else:
    print("[INFO] starting video stream...")
    vs = cv2.VideoCapture(args["video"])
writer = None

# 初始化目标追踪器
trackers = []
labels = []
fps = FPS().start()

while True:
    # 读取一帧
    (grabbed, frame) = vs.read()

    # 是否是最后了
    if frame is None:
        break

    # 图像增强
    # frame = enhance_image(frame)

    # 预处理操作
    (h, w) = frame.shape[:2]
    width = 600
    r = width / float(w)
    dim = (width, int(h * r))
    frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # 如果要将结果保存的话
    if args["output"] is not None and writer is None:
        fourcc = cv2.VideoWriter_fourcc(*"MJPG")
        writer = cv2.VideoWriter(args["output"], fourcc, 30, (frame.shape[1], frame.shape[0]), True)

    # 先检测 再追踪
    if len(trackers) == 0:
        # 获取blob数据
        (h, w) = frame.shape[:2]
        blob = cv2.dnn.blobFromImage(frame, 0.007843, (w, h), 127.5)

        # 得到检测结果
        net.setInput(blob)
        detections = net.forward()

        # 遍历得到的检测结果
        for i in np.arange(0, detections.shape[2]):
            # 能检测到多个结果，只保留概率高的
            confidence = detections[0, 0, i, 2]

            # 过滤
            if confidence > args["confidence"]:
                # 提取类别索引
                idx = int(detections[0, 0, i, 1])
                label = CLASSES[idx]

                # 只保留人的
                if CLASSES[idx] != "person":
                    continue

                # 得到BBOX
                box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
                (startX, startY, endX, endY) = box.astype("int")

                # 使用CSRT目标追踪器
                tracker = cv2.TrackerCSRT_create()
                tracker.init(frame, (startX, startY, endX - startX, endY - startY))

                # 保存结果
                labels.append(label)
                trackers.append(tracker)

                # 绘图
                cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 255, 0), 2)
                cv2.putText(frame, label, (startX, startY - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)

    # 如果已经有了框，就可以直接追踪了
    else:
        # 每一个追踪器都要进行更新
        for (tracker, label) in zip(trackers, labels):
            success, box = tracker.update(frame)
            if success:
                (startX, startY, w, h) = [int(v) for v in box]
                endX = startX + w
                endY = startY + h

                # 画出来
                cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 255, 0), 2)
                cv2.putText(frame, label, (startX, startY - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)

    # 也可以把结果保存下来
    if writer is not None:
        writer.write(frame)

    # 显示
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF

    # 退出
    if key == 27:
        break

    # 计算FPS
    fps.update()

fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

if writer is not None:
    writer.release()

cv2.destroyAllWindows()
vs.release()