在本文中，将探讨如何使用CodeProject.AI Server和Python的OpenCV库来处理视频文件。将通过一个简单的示例，展示如何加载视频文件，运行对象检测，并生成包含对象和时间戳的文件，同时在视频上叠加检测框。
将使用YOLOv5 6.2对象检测模块在CodeProject.AI Server中进行操作。这个模块在性能上表现不错，并且它运行在共享的Python虚拟环境中，这使得可以直接使用它而不需要进行繁琐的设置步骤。
以下是使用CodeProject.AI Server处理视频文件的代码示例。将打开一个视频文件，将其发送到CodeProject.AI Server进行对象检测，并在视频上显示检测框。
import cv2
import imutils
from imutils.video import FileVideoStream
from PIL import Image, ImageDraw, ImageFont
import io
import requests
import numpy as np
def do_detection(image, log_file):
buf = io.BytesIO()
image.save(buf, format='PNG')
buf.seek(0)
with requests.Session() as session:
response = session.post(server_url + "vision/detection", files={"image": ('image.png', buf, 'image/png')}, data={"min_confidence": 0.5}).json()
predictions = None
if response is not None and "predictions" in response:
predictions = response["predictions"]
if predictions is not None:
font = ImageFont.truetype("Arial.ttf", font_size)
draw = ImageDraw.Draw(image)
for object in predictions:
label = object["label"]
conf = object["confidence"]
y_max = int(object["y_max"])
y_min = int(object["y_min"])
x_max = int(object["x_max"])
x_min = int(object["x_min"])
if y_max < y_min:
temp = y_max
y_max = y_min
y_min = temp
if x_max < x_min:
temp = x_max
x_max = x_min
x_min = temp
draw.rectangle([(x_min, y_min), (x_max, y_max)], outline="red", width=line_width)
draw.text((x_min + padding, y_min - padding - font_size), f"{label} {round(conf*100.0,0)}%", font=font)
log_file.write(f"{object_info}: ({x_min}, {y_min}), ({x_max}, {y_max})\n")
return image
vs = FileVideoStream(file_path).start()
with open("results.txt", 'w') as log_file:
while True:
if not vs.more():
break
frame = vs.read()
if frame is None:
break
image = Image.fromarray(frame)
image = do_detection(image, log_file)
frame = np.asarray(image)
if frame is not None:
frame = imutils.resize(frame, width=640)
cv2.imshow("Movie File", frame)
vs.stop()
cv2.destroyAllWindows()