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- # USAGE
- # python recognize_digits.py
- # import the necessary packages
- from imutils.perspective import four_point_transform
- from imutils import contours
- import imutils
- import cv2
- import matplotlib
- #matplotlib.use('GTKAgg')
- import matplotlib.pyplot as plt
- # define the dictionary of digit segments so we can identify
- # each digit on the thermostat
- DIGITS_LOOKUP = {
- (1, 1, 1, 0, 1, 1, 1): 0,
- (0, 0, 1, 0, 0, 1, 0): 1,
- (1, 0, 1, 1, 1, 1, 0): 2,
- (1, 0, 1, 1, 0, 1, 1): 3,
- (0, 1, 1, 1, 0, 1, 0): 4,
- (1, 1, 0, 1, 0, 1, 1): 5,
- (1, 1, 0, 1, 1, 1, 1): 6,
- (1, 0, 1, 0, 0, 1, 0): 7,
- (1, 1, 1, 1, 1, 1, 1): 8,
- (1, 1, 1, 1, 0, 1, 1): 9
- }
- # load the example image
- image = cv2.imread("example.jpg")
- # pre-process the image by resizing it, converting it to
- # graycale, blurring it, and computing an edge map
- image = imutils.resize(image, height=500)
- gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
- blurred = cv2.GaussianBlur(gray, (5, 5), 0)
- edged = cv2.Canny(blurred, 50, 200, 255)
- # find contours in the edge map, then sort them by their
- # size in descending order
- cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
- cv2.CHAIN_APPROX_SIMPLE)
- cnts = cnts[0] if imutils.is_cv2() else cnts[1]
- cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
- displayCnt = None
- # loop over the contours
- for c in cnts:
- # approximate the contour
- peri = cv2.arcLength(c, True)
- approx = cv2.approxPolyDP(c, 0.02 * peri, True)
- # if the contour has four vertices, then we have found
- # the thermostat display
- if len(approx) == 4:
- displayCnt = approx
- break
- # extract the thermostat display, apply a perspective transform
- # to it
- warped = four_point_transform(gray, displayCnt.reshape(4, 2))
- output = four_point_transform(image, displayCnt.reshape(4, 2))
- # threshold the warped image, then apply a series of morphological
- # operations to cleanup the thresholded image
- thresh = cv2.threshold(warped, 0, 255,
- cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
- kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1, 5))
- thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
- # find contours in the thresholded image, then initialize the
- # digit contours lists
- cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
- cv2.CHAIN_APPROX_SIMPLE)
- cnts = cnts[0] if imutils.is_cv2() else cnts[1]
- digitCnts = []
- # loop over the digit area candidates
- for c in cnts:
- # compute the bounding box of the contour
- (x, y, w, h) = cv2.boundingRect(c)
- # if the contour is sufficiently large, it must be a digit
- if w >= 15 and (h >= 30 and h <= 40):
- digitCnts.append(c)
- # sort the contours from left-to-right, then initialize the
- # actual digits themselves
- digitCnts = contours.sort_contours(digitCnts,
- method="left-to-right")[0]
- digits = []
- # loop over each of the digits
- for c in digitCnts:
- # extract the digit ROI
- (x, y, w, h) = cv2.boundingRect(c)
- roi = thresh[y:y + h, x:x + w]
- # compute the width and height of each of the 7 segments
- # we are going to examine
- (roiH, roiW) = roi.shape
- (dW, dH) = (int(roiW * 0.25), int(roiH * 0.15))
- dHC = int(roiH * 0.05)
- # define the set of 7 segments
- segments = [
- ((0, 0), (w, dH)), # top
- ((0, 0), (dW, h // 2)), # top-left
- ((w - dW, 0), (w, h // 2)), # top-right
- ((0, (h // 2) - dHC) , (w, (h // 2) + dHC)), # center
- ((0, h // 2), (dW, h)), # bottom-left
- ((w - dW, h // 2), (w, h)), # bottom-right
- ((0, h - dH), (w, h)) # bottom
- ]
- on = [0] * len(segments)
- # loop over the segments
- for (i, ((xA, yA), (xB, yB))) in enumerate(segments):
- # extract the segment ROI, count the total number of
- # thresholded pixels in the segment, and then compute
- # the area of the segment
- segROI = roi[yA:yB, xA:xB]
- total = cv2.countNonZero(segROI)
- area = (xB - xA) * (yB - yA)
- # if the total number of non-zero pixels is greater than
- # 50% of the area, mark the segment as "on"
- if total / float(area) > 0.5:
- on[i]= 1
- # lookup the digit and draw it on the image
- digit = DIGITS_LOOKUP[tuple(on)]
- digits.append(digit)
- cv2.rectangle(output, (x, y), (x + w, y + h), (0, 255, 0), 1)
- cv2.putText(output, str(digit), (x - 10, y - 10),
- cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 255, 0), 2)
- # display the digits
- print(u"{}{}.{} \u00b0C".format(*digits))
- import numpy as np
- import matplotlib.pyplot as plt
- from PIL import Image
- fname = 'example.jpg'
- image = Image.open(fname).convert("L")
- arr = np.asarray(image)
- plt.imshow(arr, cmap='gray')
- plt.show()
- plt.imshow(output)
- plt.show()
- #cv2.imshow("Input", image)
- #cv2.imshow("Output", output)
- #cv2.waitKey(0)
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