crop_img = img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]

This line of code crops the image (img) based on the coordinates of the rectangle. The rectangle is defined by four values: rectangle[0] is the x-coordinate of the top-left corner, rectangle[1] is the y-coordinate of the top-left corner, rectangle[2] is the x-coordinate of the bottom-right corner, and rectangle[3] is the y-coordinate of the bottom-right corner.

The code uses these values to crop the image, creating a new image that contains only the pixels within the rectangle. The resulting cropped image is stored in the variable crop_img.

The int() function is used to convert the coordinates to integers, since image coordinates are typically represented as floating-point values.

相关问题

crop_img = img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]什么意思

这行代码是用来截取原始图像中指定矩形区域的图像。其中，rectangle是一个包含4个元素的列表或元组，分别表示矩形区域的左上角和右下角坐标，即[左上角 x 坐标，左上角 y 坐标，右下角 x 坐标，右下角 y 坐标]。int()函数是用来将浮点数转换为整数。最终，crop_img变量将包含截取出来的子图像。

import uvicorn from fastapi import FastAPI, UploadFile, File from io import BytesIO from PIL import Image, ImageDraw from utils.operation import YOLO def detect(onnx_path='ReqFile/yolov5n-7-k5.onnx', img=r'ReqFile/bus.jpg', show=True): ''' 检测目标，返回目标所在坐标如： {'crop': [57, 390, 207, 882], 'classes': 'person'},...] :param onnx_path:onnx模型路径 :param img:检测用的图片 :param show:是否展示 :return: ''' yolo = YOLO(onnx_path=onnx_path) # 加载yolo类 det_obj = yolo.decect(img) # 检测 # 打印检测结果 print(det_obj) # 画框框 if show: img = Image.open(img) draw = ImageDraw.Draw(img) for i in range(len(det_obj)): draw.rectangle(det_obj[i]['crop'], width=3) img.show() # 展示 return det_obj app = FastAPI() @app.get("/") def read_root(): return {"Hello": "World"} @app.post("/detect/") async def create_upload_file(file: UploadFile = File(...)): contents = await file.read() # 接收浏览器上传的图片 im1 = BytesIO(contents) # 将数据流转换成二进制文件存在内存中 # 返回结果 return detect(onnx_path='ReqFile/best-0206.onnx', img=im1, show=False) # 启动项目 if __name__ == "__main__": uvicorn.run(app='main:app', port=8000, host='0.0.0.0', reload=True)

这是一个使用 FastAPI 和 YOLO 模型进行目标检测的 Python 代码。其中 `detect()` 函数接收一个 ONNX 模型路径和图片，使用 YOLO 模型进行目标检测，并返回检测结果。`create_upload_file()` 函数用于接收浏览器上传的图片，调用 `detect()` 函数进行目标检测，并返回检测结果。最后使用 Uvicorn 启动项目。