fromPILimportImagepic=Image.open('D:/搜狗高速下载/jpg.png')w,h=pic.size#pic.thumbnail((int(w/2), int(h/2)))#生成缩略图pic=pic.resize((w*10,h*10))#放大图片pic.save('D:/搜狗高速下载/jpg.png')
处理images:对PIL.Image list调用process_images进行转为np类型, 然后进行resize to (image_size, image_size)、rescale to [0, 1]、normalize to [-1, 1],然后转换为tensor:[batch_size, channel, height, width],得到最终的pixle_values。 处理texts:对于每个instruction text,在其前面加上image_seq_len个<...
输入一张手写数字图片,输出识别值: import tensorflow as tf import os import numpy as np from PIL import Image from matplotlib import pyplot as plt model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation=tf.keras.activations.relu), tf.keras.layers...
import torch from PIL import Image import numpy as np import cv2 # Load the YOLOv5 model model = torch.hub.load('ultralytics/yolov5', 'custom', path=r'C:\Users\user\PycharmProjects\Segmentation\hieroglyphics_recognition\models\segmentbest.pt') def letterbox_image(image, new...
from PIL import Image def resize_image(image_path, output_path, width, height): image = Image.open(image_path) resized_image = image.resize((width, height)) resized_image.save(output_path) if __name__ == "__main__": image_path = "input.jpg" output_path = "output.jpg" width =...
如图所示的车牌,经过如下Python程序处理后,“cp.txt”文件得到的效果是( )from PIL import Imagef=open(“cp.txt”,“w”) #以写入模式打卡“cp.txt”文件im=Image.open(“cp.jpg”)im=im.convert(“L”)im=im.resize((90,21)) #将图片尺寸大小修改为90*21pix=im.load() #读取所有像素点的颜色值fo...
from itertools import permutations import warnings from PIL import Image from utils.tools import create_size, ratio_resize, seed_torch, get_model_input_info, get_model_path warnings.filterwarnings("ignore") model_path = get_model_path() DEVICE_ID = 0 BASENAME = list(model_path.keys()) CONT...
fromPILimportImageimportpytesseract#开源OCR识别工具importcv2importos preprocess ='blur'#threshimage = cv2.imread('scan.jpg') gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)ifpreprocess =="thresh": gray = cv2.threshold(gray,0,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]ifpreprocess =="blur"...
from PIL import Image, ImageDraw, ImageFont import os import base64 import iofrom browser_use.agent.prompts import SystemPrompt from browser_use.agent.service import Agent@@ -130,11 +134,11 @@ def _log_response(self, response: CustomAgentOutput) -> None: ...
import torch import os import pandas as pd from PIL import Image class VOCDataset(torch.utils.data.Dataset): def __init__(self,csv_file,img_dir,label_dir,S=7,B=2,C=20,transform=None): self.annotations=pd.read_csv(csv_file)