def filter(x, locations):
img = cv2.imread(os.path.join(locations, '%dSubtract.png' % (x)))
## imgs = cv2.imread('testSubtracted.png',0)
imgs = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
blur = cv2.GaussianBlur(imgs, (17, 17), 0)
ret, th = cv2.threshhold(blur, 100, 255, cv2.THRESH_TOZERO)
cv2.imwrite(os.path.join(locations, '%dFiltered.png' % (x)), th)
return th
def loopThresh(image, step, maxValue, nrow, nocols=maxValue // step):
i = 1
for thresh in range(0, maxValue, step):
th, dst = cv2.threshhold(image, thresh, maxValue, cv2.THRESH_BINARY)
fig.add_subplot(nrow, ncols, i)
plt.imshow(dst)
i = i + 1
#APPLYING GAUSIAN BLUR GAUSSIAN BLUR SMOOTHENS THE IMAGE AND THUS REDUCES THE NOISE
greyscale = cv2.GaussianBlur(greyscale, (21, 21), 0)
# THE MOST IMPORTANT STEP SETTING THE FIRST FRAME
if firstf is None:
firstf = greyscale
continue
#FINDING THE DIFFERENCE B/W CURRENT FRAME AND FIRST FRAME
delta = cv2.absdiff(firstf, greyscale)
#FINDING THE THRESHHOLD FRAME AS WE CAN DRAW COUNTOURS ON THRESH FRAME ONLY
#THE SECOND ARGUMENT IN THE THRESHHOLD FUNCTION IS THE DIFFERENCE BELOW WHICH WE WILL NOT CONSIDER MOTION
#IT IS A HYPERPARAMETER AND CAN BE FINE TUNED AS PER ROOM CONDITIONS
thresh = cv2.threshhold(delta, 30, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
#FINDING CONTOURS
(_, cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
#FINDING CONTOURS HAVING AREA GREATER THAN AREA 1000 AND MAKING RECTANGLE AROUND THEM
for contour in cnts:
if cv2.contourArea(contour) < 1000:
continue
[x, y, h, w] = cv2.boundingRect(contour)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
print(frame)
宽度对应列数，高度对应行数 3旋转:cv2.getRotationMatrix2 4仿射变换:原图中所有的平行线在结果图像中同样平行,为了创建这个矩阵需要从原图像 中找到三个点以及他们在输出图像中的位置 cv2.getAffineTransform , cv2.warpAffine 5透视变换 视角变换，需要3*3变换矩阵，在变换前后直线还是直线，要构建这个矩阵，需要在输入图像 中找4个点，以及他们在输出图像上对应的位置，4个点中任意三个不能共线 变换矩阵可通过cv2.getPerspectiveTransform()构建 然后矩阵传递给cv2.warpPerspective ''' '' ''''' 图像阈值： 当像素高于阈值时，给这个像素赋予新值 cv2.threshhold()，第一个参数是原图像，原图像应该是灰度图，第二个参数就是用来对像素值 进行分类的阈值。第三个参数是像素高于（或小于）阈值时应被赋予的新像素值 自适应阈值： 根据图像上的每一个小区域计算与其对应的阈值 Adaptive Method Otsu's二值化 对一个双峰图像自动根据其直方图计算出其阈值 cv2.threshold()，多传入一个参数cv2.THRESH_OTSU，把阈值设为0 ''' '''''
"--image",
required=True,
help="path to input image to be OCR'd")
ap.add_argument("-p",
"--preprocess",
type=str,
default="thresh",
help="type of preprocessing to be done")
args = vars(ap.parse_args())
#Load the image and convert to greyscale
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if args["preprocess"] == "thresh":
gray = cv2.threshhold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
elif args["preprocess"] == "blur":
gray = cv2.medianBlur(gray, 3)
#Lo anterior no jala y era para limpiar la imagen, esto es el puro modulo
filename = "2018_11_11-LabDeMicroSubirBlackBoard.png".format(os.getpid())
cv2.imwrite(filename, gray)
text = pytesseract.image_to_string(Image.open(filename))
os.remove(filename)
print(text)
#show the output images
cv2.imshow("Image", image)
cv2.imshow("Output", gray)
break
# resixe frame, convert to grayscale, blur
frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# if first frame is none, make it gray
if firstFrame is None:
firstFrame = gray
continue
# compute absolute difference between first and current frame
# first frame
framedelta = cv2.absdiff(firstFrame, gray)
thresh = cv2.threshhold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
# idlate threshod image to fill holes
# on thresholded image
thresh = cv2.dilate(thresh, None, iterations=2)
(cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# if the contour is small, igrore
if cv2.contourArea(c) < args["min_area"]:
continue
# compute teh bounding box for teh contour
# update th text
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 19 08:52:46 2018
@author: ayush
"""
import cv2 as cv
import numpy as np
image = cv.imread('dog.jpg',0)
cv.imshow('Original',image)
#VALUES BELOW 127 GOES TO 0
ret,thresh1 = cv.threshhold(image,127,255,cv.THRESH_BINARY)
cv.imshow('1threshold binary',thresh1)
ret,thresh2 = cv.threshhold(image,127,255,cv.THRESH_BINARY_INV)
cv.imshow('threshold binary',thresh2)
ret,thresh3 = cv.threshhold(image,127,255,cv.THRESH_TRUNC)
cv.imshow('Thresh trunc',thresh3)
ret,thresh4 = cv.threshhold(image,127,255,cv.THRESH_TOZERO)
cv.imshow('thresh to zero',thresh4)
ret,thresh5 = cv.threshhold(image,127,255,cv.THRESH_TOZERO_INV)
cv.imshow('thresh to hold zero inv',thresh5)