f = h5py.File(gcPath(basePath, snapNum, i), 'r')
时间: 2024-06-04 22:11:58 浏览: 12
This line of code opens an HDF5 file using the h5py library in Python. The file path is determined by calling the function gcPath with the base path, snapshot number, and an index i. The 'r' argument indicates that the file is being opened in read mode. Once the file is opened, its contents can be accessed using h5py functions.
相关问题
@app.route('/upload', methods=['POST', 'GET']) # 添加路由 def upload(): if request.method == 'POST': f = request.files['file'] basepath = os.path.dirname(__file__) # 当前文件所在路径 upload_path = os.path.join(basepath, 'static/images', secure_filename(f.filename)) f.save(upload_path) # 使用Opencv转换一下图片格式和名称 img = cv2.imread(upload_path) width=img.shape[1] height=(400/width)*img.shape[0] width=400 img= cv2.resize(img,(int(width), int(height))) filenamesave=str(time.time())+".jpg" cv2.imwrite(os.path.join(basepath, 'static/images', filenamesave), img) dict1={"state":1,"path":filenamesave} return jsonify(dict1) return "Please use Post"
这是一个 Flask 的路由函数,用于处理客户端上传的图片文件。当请求方法为 POST 时,函数会从请求中获取图片文件,将其保存在服务器上,然后使用 OpenCV 对图片进行格式转换和尺寸调整,最后返回一个 JSON 格式的响应,包含上传图片的状态和保存路径。如果请求方法不是 POST,则返回一个字符串 "Please use Post"。
coding=UTF-8 from flask import Flask, render_template, request, send_from_directory from werkzeug.utils import secure_filename from iconflow.model.colorizer import ReferenceBasedColorizer from skimage.feature import canny as get_canny_feature from torchvision import transforms from PIL import Image import os import datetime import torchvision import cv2 import numpy as np import torch import einops transform_Normalize = torchvision.transforms.Compose([ transforms.Normalize(0.5, 1.0)]) ALLOWED_EXTENSIONS = set([‘png’, ‘jpg’, ‘jpeg’]) app = Flask(name) train_model = ReferenceBasedColorizer() basepath = os.path.join( os.path.dirname(file), ‘images’) # 当前文件所在路径 def allowed_file(filename): return ‘.’ in filename and filename.rsplit(‘.’, 1)[1] in ALLOWED_EXTENSIONS def load_model(log_path=‘/mnt/4T/lzq/IconFlowPaper/checkpoints/normal_model.pt’): global train_model state = torch.load(log_path) train_model.load_state_dict(state[‘net’]) @app.route(“/”, methods=[“GET”, “POST”]) def hello(): if request.method == ‘GET’: return render_template(‘upload.html’) @app.route(‘/upload’, methods=[“GET”, “POST”]) def upload_lnk(): if request.method == ‘GET’: return render_template(‘upload.html’) if request.method == ‘POST’: try: file = request.files['uploadimg'] except Exception: return None if file and allowed_file(file.filename): format = "%Y-%m-%dT%H:%M:%S" now = datetime.datetime.utcnow().strftime(format) filename = now + '_' + file.filename filename = secure_filename(filename) basepath = os.path.join( os.path.dirname(file), ‘images’) # 当前文件所在路径 # upload_path = os.path.join(basepath,secure_filename(f.filename)) file.save(os.path.join(basepath, filename)) else: filename = None return filename @app.route(‘/download/string:filename’, methods=[‘GET’]) def download(filename): if request.method == “GET”: if os.path.isfile(os.path.join(basepath, filename)): return send_from_directory(basepath, filename, as_attachment=True) pass def get_contour(img): x = np.array(img) canny = 0 for layer in np.rollaxis(x, -1): canny |= get_canny_feature(layer, 0) canny = canny.astype(np.uint8) * 255 kernel = np.array([ [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], ], dtype=np.uint8) canny = cv2.dilate(canny, kernel) # canny = Image.fromarray(canny) return canny @app.route(‘/embedding//’, methods=[“GET”, “POST”]) def icontran(img, reference): global train_model if request.method == ‘POST’: imgPath = os.path.join(basepath, img) referencePath = os.path.join(basepath, reference) img = cv2.imread(imgPath) if img is None or img.size <= 0: return None contour = get_contour(img).astype(np.float32).copy() contour = 255 - contour reference = cv2.imread(referencePath).astype(np.float32) reference = cv2.cvtColor(reference, cv2.COLOR_BGR2RGB) reference = transform_Normalize(torch.from_numpy(reference).permute(2, 0, 1).unsqueeze(0).float()/ 255.0) contour = transform_Normalize(torch.from_numpy(contour).unsqueeze(0).unsqueeze(0).float()/ 255.0) train_model.eval() transfer = train_model(contour, reference) transfer = transfer.squeeze(0) transfer = (transfer + 0.5).clamp(0, 1).mul_(255).permute(1, 2, 0).type(torch.uint8).numpy() transfer = transfer.numpy() cv2.imwrite(imgPath, transfer) return basepath # success if name == “main”: load_model() app.run(host=‘10.21.16.144’, port=9999, debug=True) 用puthon写一个调用这个服务器的gui
这是一段 Python 代码,它使用 Flask 框架来创建一个网站应用。它还导入了一些需要使用的库,如 werkzeug、numpy、torch 等。该代码可以对图片进行一些处理,如调整亮度、色彩等。其中包括一个可以将黑白图片变成彩色图片的算法 train_model。同时,代码中使用了一些图像处理的函数和变换,如 canny 算法和 einops。最后,它可以将处理后的图片保存在指定的文件夹中,并在网页中展示出来。