import cv2
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import numpy as np
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from skimage import morphology, img_as_ubyte
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import pkg_resources
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templatefile = '/template-8.png' # always use slash
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template8 = pkg_resources.resource_filename(__name__, templatefile)
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def kernel(x, y):
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"""
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Function greates square kernel of size x and y
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"""
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return np.ones((x, y), np.uint8)
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def find_biggest_blob(image, original_image,sid_mask):
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if sid_mask[0] == "1":
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move_left = 35
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elif sid_mask[0] == "x":
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move_left = 40
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else:
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move_left = 0
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# Remove noise
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image2 = cv2.morphologyEx(
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original_image, cv2.MORPH_OPEN, kernel(2, 2), iterations=3
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)
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# find biggest block of pixels
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image1 = cv2.morphologyEx(image2, cv2.MORPH_DILATE, kernel(5, 25), iterations=4)
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image1 = img_as_ubyte(image1 > 50)
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cv2.imwrite("/tmp/sidblock1.png", image1)
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im2, ctrs, hier = cv2.findContours(
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image1.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
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)
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sorted_ctrs = sorted(
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ctrs, key=lambda ctr: cv2.contourArea(ctr)
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) # get bigges contour
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x, y, w, h = cv2.boundingRect(sorted_ctrs[-1])
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image = image[y : y + h, x + 25 - move_left : x + w - 40] # +25,-25
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return image
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def sid_compare(sid_no, sid_mask):
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"""
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Function compares student id number with student id mask if the recognised number is valid according to the mask
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:param sid_no:
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:param sid_mask:
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:return: True if they match, else False
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"""
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for s, es in zip(sid_mask, sid_no):
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if s != "x" and s != es:
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return False
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return True
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def segment_by_contours(image, original_image, classifier,sid_mask):
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"""
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First algorithm. it segments numerals with contours. It works with numbers where individual numerals does not touch.
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:param image:
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:param original_image:
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:param classifier:
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:return: student id as a string
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"""
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sid_no = ""
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image=find_biggest_blob(image,original_image,sid_mask)
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cv2.imwrite("/tmp/sid_contour1.png",image)
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im2, ctrs, hier = cv2.findContours(
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image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
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)
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sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
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for i, ctr in enumerate(sorted_ctrs):
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# Get bounding box
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x, y, w, h = cv2.boundingRect(ctr)
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# Getting ROI
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if w < h / 2:
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sid_no = sid_no + "1"
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continue
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roi = image[y : y + h, x : x + w]
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roi = img_as_ubyte(roi < 128)
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roi = cv2.resize(roi, (32, 32))
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# cv2.rectangle(image,(x,y),( x + w, y + h ),(0,255,0),2)
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cv2.imwrite("/tmp/sid_no_{}.png".format(i), roi)
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sid_no = sid_no + str(classifier.predict(roi.reshape(1, -1) / 255.0)[0])
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return sid_no
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def segment_by_sid_len(image, original_image, sid_mask, classifier):
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"""
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Third algorithm. It trys to get biggest "blob" in the image and then it cuts it into individual numbers by force.
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It has some problems with finding individual numbers, so some tweaking must be done!
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:param image:
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:param original_image:
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:param sid_mask:
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:param classifier:
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:return: student id as a string
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"""
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sid_no = ""
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sid_len = len(sid_mask)
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image=find_biggest_blob(image,original_image,sid_mask)
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cv2.imwrite("/tmp/sidblock2.png", image)
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imgHeight, imgWidth = image.shape[0:2]
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numWidth = int(imgWidth / (sid_len))
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for i in range(0, sid_len):
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num = image[:, i * numWidth : (i + 1) * numWidth]
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num = img_as_ubyte(num < 128)
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num = cv2.resize(num, (32, 32))
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cv2.imwrite("/tmp/sid_no_{}.png".format(i), num)
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sid_no = sid_no + str(classifier.predict(num.reshape(1, -1) / 255.0)[0])
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return sid_no
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def segment_by_7segments(image, original_image, sid_mask, classifier):
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"""
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Second attempt. It dilates the image to get all 7 segments wisible as 8888888 then it does pattern matching of 8 with
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pattern image. It works if the scaned gray level is high enough.
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:param image:
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:param original_image:
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:param sid_mask:
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:param classifier:
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:return: student id number as a string
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"""
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block_image = cv2.morphologyEx(
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original_image, cv2.MORPH_CLOSE, kernel(2, 2), iterations=10
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)
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block_image = img_as_ubyte(block_image < 50)
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cv2.imwrite("/tmp/sid_3rd1.png", block_image)
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template = cv2.imread(template8, 0)
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w, h = template.shape[::-1]
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res = cv2.matchTemplate(block_image, template, cv2.TM_CCOEFF_NORMED)
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loc = np.where(res >= 0.75)
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cimg = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
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loc_filtered_x = []
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loc_filtered_y = []
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for pt in zip(*loc[::-1]):
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pt = (pt[0] - 10, pt[1] - 10)
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loc_filtered_y.append(pt[1])
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loc_filtered_x.append(pt[0])
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# points.append(pt)
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# filter points
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if len(loc_filtered_x) == 0:
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return ""
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loc_filtered_x, loc_filtered_y = zip(*sorted(zip(loc_filtered_x, loc_filtered_y)))
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a = np.diff(loc_filtered_x) > int(w / 2)
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a = np.append(a, True)
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loc_filtered_x = np.array(loc_filtered_x)
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loc_filtered_y = np.array(loc_filtered_y)
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points = [loc_filtered_y[a], loc_filtered_x[a]]
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for pt in zip(*points[::-1]):
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cv2.rectangle(cimg, pt, (pt[0] + w, pt[1] + h), (0, 255, 255), 2)
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cv2.imwrite("/tmp/sid_3rd2.png", cimg)
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sid_no = ""
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for i, pt in enumerate(zip(*points[::-1])):
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num = image[pt[1] : pt[1] + h, pt[0] : pt[0] + w]
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# cv2.imwrite("sid_3no_{}.png".format(i), num)
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num = img_as_ubyte(num < 128)
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try:
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num = cv2.resize(num, (32, 32))
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except:
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return ""
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cv2.imwrite("/tmp/sid_3no_{}.png".format(i), num)
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sid_no = sid_no + str(classifier.predict(num.reshape(1, -1) / 255.0)[0])
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return sid_no
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def getSID(image, classifier, sid_mask):
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"""
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Tries different approaches on image to get student id number. Firstly clears image of noise and then skeletonizes
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numbers and thickens it until it gets normalized image. It sends it to the segmentation and recognition functions.
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Tweak both MORPH_OPEN lines....
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:param image:
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:param classifier:
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:param sid_mask:
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:return: (student_id, error, warning) student id as a string, list of errors and list of warnings during the recognition
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"""
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sid_warn = []
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sid_err = []
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image = 255 - image
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image_original = image.copy()
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image = img_as_ubyte(image > 70)
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cv2.imwrite("/tmp/enSID0.png", image)
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# Remove noise
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#image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel(2, 2), iterations=3)
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# Closing. Connect non connected parts
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image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel(5, 1), iterations=4)
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# Again noise removal after closing
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# image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel(8, 8), iterations=1)
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# don't do too much noise removal.
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image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel(3, 3), iterations=1)
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# Skeletonization
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image = img_as_ubyte(morphology.thin(image > 128))
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cv2.imwrite("/tmp/enSID1.png", image)
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# Stub removal (might not be necessary if thinning instead of skeletonize is used above
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# Making lines stronger
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image = cv2.morphologyEx(image, cv2.MORPH_DILATE, kernel(5, 5), iterations=1)
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image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel(10, 10))
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# Thining again
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image = img_as_ubyte(morphology.skeletonize(image > 0.5))
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image = cv2.morphologyEx(image, cv2.MORPH_DILATE, kernel(10, 10))
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cv2.imwrite("/tmp/enhancedSID.png", image)
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sid_no = segment_by_contours(image, image_original, classifier, sid_mask)
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if len(sid_no) != len(sid_mask) or not sid_compare(sid_no, sid_mask):
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sid_warn.append("Trying second SID algorithm.")
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sid_no = segment_by_7segments(image, image_original, sid_mask, classifier)
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if (len(sid_no)) != len(sid_mask):
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sid_no = segment_by_sid_len(image, image_original, sid_mask, classifier)
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sid_warn.append("Trying third SID algorithm.")
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if not sid_compare(sid_no, sid_mask):
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sid_err = ["Wrong SID!"]
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return sid_no, sid_err, sid_warn
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