Recognizing digits with OpenCV and Python.zip

# import the necessary packages from imutils.perspective import four_point_transform from imutils import contours import imutils import cv2 # 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 = imutils.grab_contours(cnts) 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 = imutils.grab_contours(cnts) 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)) cv2.imshow("Input", image) cv2.imshow("Output", output) cv2.waitKey(0)