在Python图像中检测暖色 [英] Detecting warm colors in the Python image
问题描述
我有问题，需要您的帮助.
I have a problem and I need your help.
我有一系列的热成像图像，我需要检测其中进行分析的区域中的热点(显示在图像右侧的栏中).在这些示例图像的情况下，热点位于十字准线的焦点上，但是，目标是假设我不知道该点在哪里，并且算法本身会根据交叉点上的条找到它.正确的.我将其中一些图像作为示例:
I have a series of thermographic images, of which I need to detect the hot spot (shown in the bar to the right of the image) in the area where the analysis is being done. In the case of these example images, the hot spot is in the focus of the crosshair, however, the goal is to imagine that I don't know where this point is and that the algorithm itself finds it, based on the bar on the right. I leave below some of these images as an example:
在此示例中，边栏指示33.2到97.7°C之间的温度范围.我想在图像中标识97.7°C的位置.最初，我创建了一个代码，在其中读取条形图最高点的BGR值，并在图像的其余部分中查找此组合，但这没有返回任何内容.不敢相信，我创建了一个代码，该代码标识整个栏中的RGB代码并查看图像，该图像也未返回任何内容，代码如下:
In this example, the sidebar indicates a temperature range between 33.2 and 97.7 ° C. I would like to identify in the image where the 97.7 ° C point is. Initially I created a code in which I read the BGR value at the highest point of the bar and look for this combination in the rest of the image, this didn't return anything. Not convinced, I created a code that identifies the RGB code in the entire bar and looks in the image, which also did not return anything, the code follows below:
# Find one of temperature bar colors in the image
import cv2
image_path = r"C:\Users\bruno\PycharmProjects\TCC\Imagens\IR_1544.jpg"
img = cv2.imread(image_path)
crop1 = img[69:171, 309:310]
for i in range(70, 172):
crop = img[i-1:i, 309:310]
num1, num2, num3 = cv2.split(crop)
for i in range(0, crop.shape[0]):
for j in range(0, crop.shape[1]):
if img[i][j][0] == num1:
if img[i][j][1] == num2:
if img[i][j][2] == num3:
print("I found")
cv2.imshow("img1", img)
cv2.imshow("img2", crop1)
cv2.waitKey(0)
cv2.destroyAllWindows()
我想知道是否还有其他方法可以识别图像中的这些颜色. 我感谢所有可以提供帮助的人！
I would like to know if there is another way that I can identify these colors in the image. I thank everyone who can help !!
推荐答案
我必须遵循很多教程才能实现自己的目标:
图像Opencv的估计亮度
在OpenCV中将HSV转换为灰度
在图像中找到最亮的地方使用Python和OpenCV
OpenCV-Python教程
OpenCV-Python教程
使用OpenCV和Python识别数字
使用Pytesseract OCR识别表格图像中的特定数字
将数字范围转换为另一个范围，并保持比例
I had to follow a lot of tutorials to achieve my goal:
Estimate Brightness of an image Opencv
Convert HSV to grayscale in OpenCV
Finding the Brightest Spot in an Image using Python and OpenCV
OpenCV-Python Tutorials
OpenCV-Python Tutorials
Recognizing digits with OpenCV and Python
Recognise text and digit from the image with Python, OpenCV and Tesseract OCR
Recognize specific numbers from table image with Pytesseract OCR
Convert a number range to another range, maintaining ratio
import cv2
import numpy as np
import pytesseract # used to read the digits on images
from PIL import Image # transformation of image read with OpenCV to use it with pytesseract
src_path = 'C:/Users/user/Documents/StackOverflow/WarmColorDetection/'
pytesseract.pytesseract.tesseract_cmd = 'C:/Users/user/AppData/Local/Tesseract-OCR/tesseract.exe'
def find_temperature_range(img, y1=0, y2=0, x1=0, x2=0):
'''
Find the number that indicates the temperature range for that image.
:param img: The image where the temperature range is located.
:param y1: Start of the temperature scale label height.
:param y2: End of the temperature scale label height.
:param x1: Start of of the temperature scale label width.
:param x2: End of of the temperature scale label width.
:return: A temperature range value read.
'''
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
roi = gray[y1:y2, x1:x2] # ROI - Region of Interest
thresh = cv2.threshold(roi, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
kernel = np.ones((1, 1), np.uint8)
dilation = cv2.dilate(thresh, kernel, iterations=1)
# Recognize text with tesseract for python
binimagem = Image.fromarray(dilation)
temperature_range = pytesseract.image_to_string(binimagem,
config='--psm 10 -c tessedit_char_whitelist=01234567890.')
return float(temperature_range)
def find_warm_pixel(img, radius=3):
'''
Find warm pixel in the given image
:param img: Image where the warm pixel will be searched
:param radius: kernel
:return: A tuple with the values of (minVal, maxVal, minLoc, maxLoc)
'''
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Apply a Gaussian Blur to the image then find the brightest region
gray = cv2.GaussianBlur(gray, (radius, radius), 0)
return cv2.minMaxLoc(gray)
if __name__ == '__main__':
# Loop over all images and show the warm point of all of them
for i in range(1, 6):
img = cv2.imread(f'img/img{i}.jpg', 1)
y, x, _ = img.shape
img_copy = img.copy()
max_temp_range = find_temperature_range(img_copy, 45, 60, 280, 315)
min_temp_range = find_temperature_range(img_copy, 178, 194, 280, 315)
if i == 1:
max_temp_range = 97.7 # Could not read the correct number only for this case, as it's showing 77
(minVal, maxVal, minLoc, maxLoc) = find_warm_pixel(img_copy)
# Converting a pixel value based on minimum and maximum value range read from the image
# new_value = ( (old_value - old_min) / (old_max - old_min) ) * (new_max - new_min) + new_min
old_value = maxVal
old_min = 0
old_max = 255
temperature = ((old_value - old_min) / (old_max - old_min)) * (max_temp_range - min_temp_range) + min_temp_range
circle_radius = 3
cv2.circle(img, maxLoc, circle_radius, (255, 0, 0), 2) # draw a circle around the britest pixel
cv2.putText(img, f'Coordinate: {maxLoc}', (122, 210), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.putText(img, f'Value: {temperature:.2f}', (122, 225), cv2.FONT_HERSHEY_SIMPLEX, 0.35,
(255, 255, 255), 1,
cv2.LINE_AA)
# Display the result
cv2.namedWindow(f'Image {i}', cv2.WINDOW_GUI_NORMAL)
cv2.resizeWindow(f'Image {i}', x, y)
cv2.imshow(f'Image {i}', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
这篇关于在Python图像中检测暖色的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
