帮我利用yolov4算法写一个数据集为mot17det的行人检测python文件

好的，以下是一个基于YOLOv4算法的行人检测Python文件，可以用于MOT17Det数据集。

import cv2
import numpy as np

# Load YOLOv4
net = cv2.dnn.readNet("yolov4.weights", "yolov4.cfg")

# Define output layer names
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# Load MOT17Det video
cap = cv2.VideoCapture("path/to/MOT17Det/video.mp4")

# Define classes
classes = ["person"]

# Define colors
colors = np.random.uniform(0, 255, size=(len(classes), 3))

# Loop through video frames
while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Get image dimensions
    height, width, channels = frame.shape

    # Prepare input image for YOLOv4
    blob = cv2.dnn.blobFromImage(frame, 1/255, (416, 416), swapRB=True, crop=False)

    # Set input image for YOLOv4
    net.setInput(blob)

    # Forward pass through YOLOv4
    outputs = net.forward(output_layers)

    # Define lists for bounding boxes, confidences and class IDs
    boxes = []
    confidences = []
    class_ids = []

    # Loop through output layers
    for output in outputs:
        # Loop through detections
        for detection in output:
            # Get class ID and confidence
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]

            # If object is a person and confidence is high enough
            if classes[class_id] == "person" and confidence > 0.5:
                # Get bounding box coordinates
                center_x = int(detection[0] * width)
                center_y = int(detection[1] * height)
                w = int(detection[2] * width)
                h = int(detection[3] * height)
                x = center_x - w // 2
                y = center_y - h // 2

                # Append bounding box, confidence and class ID to lists
                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)

    # Apply non-maximum suppression to remove overlapping bounding boxes
    indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

    # Draw bounding boxes and class labels on image
    for i in indices:
        i = i[0]
        color = colors[class_ids[i]]
        x, y, w, h = boxes[i]
        cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
        cv2.putText(frame, classes[class_ids[i]] + " " + str(round(confidences[i], 2)), (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

    # Display output frame
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1)
    if key == 27:
        break

# Release video capture and close all windows
cap.release()
cv2.destroyAllWindows()

需要注意的是，此代码假定您已经将YOLOv4配置文件和权重文件下载到本地，并根据您的实际情