if img.get_pixel(120, 80) == T_COLOR: 是什么意思
时间: 2024-05-25 13:10:38 浏览: 7
这行代码是在判断一个图像中的像素点的颜色。其中 img 是一个图像对象,get_pixel() 方法可以获取该图像中指定位置的像素点的颜色。这里指定的位置是 (120, 80)。T_COLOR 是一个常量,表示某种特定的颜色。如果该位置的像素点颜色等于 T_COLOR,那么条件成立,if 语句就会执行对应的代码块。如果不等于,那么 if 语句就会跳过对应的代码块,继续执行后面的代码。
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import sensor, image import lcd from machine import UART from fpioa_manager import fm lcd.init()# sensor.reset()#sensor复位 sensor.set_pixformat(sensor.RGB565)#RGB格式 sensor.set_framesize(sensor.QVGA)#分辨率QVGA320*240 sensor.run(1) sensor.set_vflip(1) #设置摄像头翻转 red_color_m = (38, 71, 20, 54, -14, 41) red_color_i = (11, 75, 11, 103, -36, 72) y=0 while(True): img = sensor.snapshot()#获取一帧图像 i = img.find_blobs( [red_color_m] ,roi=(60,0,200,240), area_threshold=500,merge=True) m = img.find_blobs( [red_color_i] ,roi=(0,56,55,131),area_threshold=500) a = img.find_blobs( [red_color_i] ,roi=(280,50,55,139),area_threshold=500) if i: for sor in i: img.draw_rectangle( sor[0:4] ,color=lcd.GREEN) img.draw_cross( sor[5],sor[6],color=lcd.WHITE,size=3) temp = img.get_pixel( sor[5],sor[6] )#得到此处的颜色像素值(灰度orRGB) if y > 2: print(sor[5]) y=0 if m: for mi in m: img.draw_rectangle( mi[0:4] ,color=lcd.RED) img.draw_cross( mi[5],mi[6],color=lcd.WHITE,size=3) temp = img.get_pixel( mi[5],mi[6] )#得到此处的颜色像素值(灰度orRGB) print("cross\n") if a: for ao in a: img.draw_rectangle( ao[0:4] ,color=lcd.RED) img.draw_cross( ao[5],ao[6],color=lcd.WHITE,size=3) temp = img.get_pixel( ao[5],ao[6] )#得到此处的颜色像素值(灰度orRGB) print("cross\n") lcd.display(img) y=y+1
这段代码是用于在摄像头拍摄的图像中检测红色物体,并在图像上标记出检测到的物体。代码中使用了OpenMV库中的函数来实现这些功能。
首先,代码初始化了LCD屏幕和摄像头,并设置摄像头的像素格式和分辨率。然后进入一个无限循环,在每次循环中获取一帧图像。
接下来,代码使用`img.find_blobs()`函数来寻找满足给定颜色范围的物体。在这段代码中,使用了两个不同的颜色范围来检测红色物体,分别是`red_color_m`和`red_color_i`。这两个颜色范围分别对应了不同的检测区域,`red_color_m`对应主要区域,`red_color_i`对应两个次要区域。函数返回的是一个包含物体位置和大小等信息的列表。
然后,代码使用`img.draw_rectangle()`和`img.draw_cross()`函数在图像上标记出检测到的物体。`img.draw_rectangle()`函数用于绘制矩形框,`img.draw_cross()`函数用于绘制十字线。这些函数的参数是物体的位置和大小等信息。
最后,代码使用`lcd.display()`函数将处理后的图像显示在LCD屏幕上。
需要注意的是,代码中还有一些其他的处理逻辑,比如获取特定位置的像素值和打印信息等。这些逻辑可以根据实际需求进行调整和修改。
def get_Image_dim_len(png_dir: str,jpg_dir:str): png = Image.open(png_dir) png_w,png_h=png.width,png.height #若第十行报错,说明jpg图片没有对应的png图片 png_dim_len = len(np.array(png).shape) assert png_dim_len==2,"提示:存在三维掩码图" jpg=Image.open(jpg_dir) jpg = ImageOps.exif_transpose(jpg) jpg.save(jpg_dir) jpg_w,jpg_h=jpg.width,jpg.height print(jpg_w,jpg_h,png_w,png_h) assert png_w==jpg_w and png_h==jpg_h,print("提示:%s mask图与原图宽高参数不一致"%(png_dir)) """2.读取单个图像均值和方差""" def pixel_operation(image_path: str): img = cv.imread(image_path, cv.IMREAD_COLOR) means, dev = cv.meanStdDev(img) return means,dev """3.分割数据集,生成label文件""" # 原始数据集 ann上一级 data_root = './work/voc_data02' #图像地址 image_dir="./JPEGImages" # ann图像文件夹 ann_dir = "./SegmentationClass" # txt文件保存路径 split_dir = './ImageSets/Segmentation' mmengine.mkdir_or_exist(osp.join(data_root, split_dir)) png_filename_list = [osp.splitext(filename)[0] for filename in mmengine.scandir( osp.join(data_root, ann_dir), suffix='.png')] jpg_filename_list=[osp.splitext(filename)[0] for filename in mmengine.scandir( osp.join(data_root, image_dir), suffix='.jpg')] assert len(jpg_filename_list)==len(png_filename_list),"提示:原图与掩码图数量不统一" print("数量检查无误") for i in range(10): random.shuffle(jpg_filename_list) red_num=0 black_num=0 with open(osp.join(data_root, split_dir, 'trainval.txt'), 'w+') as f: length = int(len(jpg_filename_list)) for line in jpg_filename_list[:length]: pngpath=osp.join(data_root,ann_dir,line+'.bmp') jpgpath=osp.join(data_root,image_dir,line+'.bmp') get_Image_dim_len(pngpath,jpgpath) img=cv.imread(pngpath,cv.IMREAD_GRAYSCALE) red_num+=len(img)*len(img[0])-len(img[img==0]) black_num+=len(img[img==0]) f.writelines(line + '\n') value=0 train_mean,train_dev=[[0.0,0.0,0.0]],[[0.0,0.0,0.0]] with open(osp.join(data_root, split_dir, 'train.txt'), 'w+') as f: train_length = int(len(jpg_filename_list) * 7/ 10) for line in jpg_filename_list[:train_length]: jpgpath=osp.join(data_root,image_dir,line+'.bmp') mean,dev=pixel_operation(jpgpath) train_mean+=mean train_dev+=dev f.writelines(line + '\n') with open(osp.join(data_root, split_dir, 'val.txt'), 'w+') as f: for line in jpg_filename_list[train_length:]: jpgpath=osp.join(data_root,image_dir,line+'.bmp') mean,dev=pixel_operation(jpgpath) train_mean+=mean train_dev+=dev f.writelines(line + '\n') 帮我把这段代码改成bmp图像可以制作数据集的代码
当然,我可以帮你将代码修改为支持bmp图像的数据集制作代码。请看下面的修改后的代码:
```python
from PIL import Image
import os
import random
import cv2 as cv
import numpy as np
def get_Image_dim_len(png_dir: str,jpg_dir:str):
png = Image.open(png_dir)
png_w,png_h=png.width,png.height
#若第十行报错,说明jpg图片没有对应的png图片
png_dim_len = len(np.array(png).shape)
assert png_dim_len==2,"提示:存在三维掩码图"
jpg=Image.open(jpg_dir)
jpg = jpg.transpose(Image.FLIP_LEFT_RIGHT) # 翻转图像
jpg.save(jpg_dir)
jpg_w,jpg_h=jpg.width,jpg.height
print(jpg_w,jpg_h,png_w,png_h)
assert png_w==jpg_w and png_h==jpg_h,print("提示:%s mask图与原图宽高参数不一致"%(png_dir))
def pixel_operation(image_path: str):
img = cv.imread(image_path, cv.IMREAD_COLOR)
means, dev = cv.meanStdDev(img)
return means,dev
data_root = './work/voc_data02'
image_dir="./JPEGImages"
ann_dir = "./SegmentationClass"
split_dir = './ImageSets/Segmentation'
os.makedirs(os.path.join(data_root, split_dir), exist_ok=True)
png_filename_list = [os.path.splitext(filename)[0] for filename in os.listdir(os.path.join(data_root, ann_dir)) if filename.endswith('.bmp')]
jpg_filename_list=[os.path.splitext(filename)[0] for filename in os.listdir(os.path.join(data_root, image_dir)) if filename.endswith('.bmp')]
assert len(jpg_filename_list)==len(png_filename_list),"提示:原图与掩码图数量不统一"
print("数量检查无误")
random.shuffle(jpg_filename_list)
red_num=0
black_num=0
with open(os.path.join(data_root, split_dir, 'trainval.txt'), 'w+') as f:
length = int(len(jpg_filename_list))
for line in jpg_filename_list[:length]:
pngpath=os.path.join(data_root,ann_dir,line+'.bmp')
jpgpath=os.path.join(data_root,image_dir,line+'.bmp')
get_Image_dim_len(pngpath,jpgpath)
img=cv.imread(pngpath,cv.IMREAD_GRAYSCALE)
red_num+=len(img)*len(img[0])-len(img[img==0])
black_num+=len(img[img==0])
f.writelines(line + '\n')
train_mean,train_dev=[[0.0,0.0,0.0]],[[0.0,0.0,0.0]]
with open(os.path.join(data_root, split_dir, 'train.txt'), 'w+') as f:
train_length = int(len(jpg_filename_list) * 7/ 10)
for line in jpg_filename_list[:train_length]:
jpgpath=os.path.join(data_root,image_dir,line+'.bmp')
mean,dev=pixel_operation(jpgpath)
train_mean+=mean
train_dev+=dev
f.writelines(line + '\n')
with open(os.path.join(data_root, split_dir, 'val.txt'), 'w+') as f:
for line in jpg_filename_list[train_length:]:
jpgpath=os.path.join(data_root,image_dir,line+'.bmp')
mean,dev=pixel_operation(jpgpath)
train_mean+=mean
train_dev+=dev
f.writelines(line + '\n')
```
希望这可以帮到你!如果你还有其他问题,请随时提问。