defsupersample(img_array,scale_factor=2):# 获取原始图像尺寸original_height,original_width,channels=img_array.shape# 创建一个新的以 scale_factor 放大的图像new_height=original_height*scale_factor new_width=original_width*scale_factor supersampled_img=np.zeros((new_height,new_width,channels),dtype=...
The function copies selected elements from an input array to an output array: dst(I) = src(I) if mask(I) = 0.//该函数把输入数组(src数组)中选中的元素(可以认为是做了标记的,不过这些标志是谁来做的呢??对,就是mask,孩子你太聪明了)拷贝到……… ………到哪里?快说!!拷贝到dst数组嘛…… ...
squeeze(image_masks, axis=3) def detect(img_array): # model expects images shape: [1, None, None, 3] img_array_expanded = np.expand_dims(img_array, axis=0) results = {} with detection_graph.as_default(): with tf.Session(config=config) as sess: ops = tf.get_default_graph()....
=len(label_name_to_value)label_name_to_value[label_name]=label_value lbl,_=utils.shapes_to_label(img.shape,data["shapes"],label_name_to_value)label_names=[None]*(max(label_name_to_value.values())+1)forname,valueinlabel_name_to_value.items():label_names[value]=name lbl_viz=imgviz....
import cv2 import matplotlib.pyplot as plt import numpy as np from PIL import Image from torchvision.transforms import transforms def show_mask(mask, ax, random_color=False): if random_color: color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) else: color = np.arra...
思路:基于一维image array数值聚类,总体期望肺部区域为一类,肺部周边为另一类。 #提取肺部大致均值middle=img[100:400,100:400]mean=np.mean(middle)# 将图片最大值和最小值替换为肺部大致均值max=np.max(img)min=np.min(img)print(mean,min,max)img[img==max]=meanimg[img==min]=meanimage_array=imgimp...
[mask>0]=label#转为RGB图像,*(mask_o/255)用于调节每个像素点亮度#(h,w,1)->(h*w*1,1)->(h*w*1,3)->(h,w,3)#(h,w,3)*(h,w,3)mask_image = np.reshape(np.array(colors, np.uint8)[np.reshape(mask, [-1])], [origin.size[1], origin.size[0], -1])*(mask_origin/255...
python3 person_blocker.py -i images/img3.jpg -c '(128, 128, 128)' -o 'bus' 'truck'遮掩特定的目标需要两个步骤：首先执行推断模型并获取所有的目标 ID，然后再根据 ID 选择性地遮掩这些目标。python3 person_blocker.py -i images/img4.jpg -l python3 person_blocker.py -i images/img4.jpg ...
mask_img = np.array([[0, 0, 0], [0, 0, 1], [1, 1, 1]]) mask_img = mask_img / (np.max(mask_img) + 0.5) #输出图像 输出_img = np.array([[15, 15, 30], [25, 25, 30], [25, 15, 25]]) #计算蒙片比例 mask_百分比= np.max(mask_img) + 0.5 百分比_output = (...
img = Image.open(old_mask) img = Image.fromarray(np.uint8(np.array(img))) new_mask = os.path.join(dest_dir, new_name) img.save(new_mask) if _name_ == 'main': #dir = raw_input('please input the operate dir:') src_dir = r'D:\Anaconda3\labelme-master\labelme_json' ...