您好,登錄后才能下訂單哦!
這篇文章主要為大家展示了“如何使用OpenCV-Python實(shí)現(xiàn)識(shí)別答題卡判卷功能”,內(nèi)容簡而易懂,條理清晰,希望能夠幫助大家解決疑惑,下面讓小編帶領(lǐng)大家一起研究并學(xué)習(xí)一下“如何使用OpenCV-Python實(shí)現(xiàn)識(shí)別答題卡判卷功能”這篇文章吧。
識(shí)別用相機(jī)拍下來的答題卡,并判斷最終得分(假設(shè)正確答案是B, E, A, D, B)
輪廓識(shí)別——答題卡邊緣識(shí)別
透視變換——提取答題卡主體
輪廓識(shí)別——識(shí)別出所有圓形選項(xiàng),剔除無關(guān)輪廓
檢測每一行選擇的是哪一項(xiàng),并將結(jié)果儲(chǔ)存起來,記錄正確的個(gè)數(shù)
計(jì)算最終得分并在圖中標(biāo)注
輪廓識(shí)別——答題卡邊緣識(shí)別
輸入圖像
import cv2 as cv import numpy as np # 正確答案 right_key = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1} # 輸入圖像 img = cv.imread('./images/test_01.jpg') img_copy = img.copy() img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) cvshow('img-gray', img_gray)
# 圖像預(yù)處理 # 高斯降噪 img_gaussian = cv.GaussianBlur(img_gray, (5, 5), 1) cvshow('gaussianblur', img_gaussian) # canny邊緣檢測 img_canny = cv.Canny(img_gaussian, 80, 150) cvshow('canny', img_canny)
輪廓識(shí)別——答題卡邊緣識(shí)別
# 輪廓識(shí)別——答題卡邊緣識(shí)別 cnts, hierarchy = cv.findContours(img_canny, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) cv.drawContours(img_copy, cnts, -1, (0, 0, 255), 3) cvshow('contours-show', img_copy)
透視變換——提取答題卡主體
對每個(gè)輪廓進(jìn)行擬合,將多邊形輪廓變?yōu)樗倪呅?/p>
docCnt = None # 確保檢測到了 if len(cnts) > 0: # 根據(jù)輪廓大小進(jìn)行排序 cnts = sorted(cnts, key=cv.contourArea, reverse=True) # 遍歷每一個(gè)輪廓 for c in cnts: # 近似 peri = cv.arcLength(c, True) # arclength 計(jì)算一段曲線的長度或者閉合曲線的周長; # 第一個(gè)參數(shù)輸入一個(gè)二維向量,第二個(gè)參數(shù)表示計(jì)算曲線是否閉合 approx = cv.approxPolyDP(c, 0.02 * peri, True) # 用一條頂點(diǎn)較少的曲線/多邊形來近似曲線/多邊形,以使它們之間的距離<=指定的精度; # c是需要近似的曲線,0.02*peri是精度的最大值,True表示曲線是閉合的 # 準(zhǔn)備做透視變換 if len(approx) == 4: docCnt = approx break
透視變換——提取答題卡主體
# 透視變換——提取答題卡主體 docCnt = docCnt.reshape(4, 2) warped = four_point_transform(img_gray, docCnt) cvshow('warped', warped)
def four_point_transform(img, four_points): rect = order_points(four_points) (tl, tr, br, bl) = rect # 計(jì)算輸入的w和h的值 widthA = np.sqrt((tr[0] - tl[0]) ** 2 + (tr[1] - tl[1]) ** 2) widthB = np.sqrt((br[0] - bl[0]) ** 2 + (br[1] - bl[1]) ** 2) maxWidth = max(int(widthA), int(widthB)) heightA = np.sqrt((tl[0] - bl[0]) ** 2 + (tl[1] - bl[1]) ** 2) heightB = np.sqrt((tr[0] - br[0]) ** 2 + (tr[1] - br[1]) ** 2) maxHeight = max(int(heightA), int(heightB)) # 變換后對應(yīng)的坐標(biāo)位置 dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype='float32') # 最主要的函數(shù)就是 cv2.getPerspectiveTransform(rect, dst) 和 cv2.warpPerspective(image, M, (maxWidth, maxHeight)) M = cv.getPerspectiveTransform(rect, dst) warped = cv.warpPerspective(img, M, (maxWidth, maxHeight)) return warped def order_points(points): res = np.zeros((4, 2), dtype='float32') # 按照從前往后0,1,2,3分別表示左上、右上、右下、左下的順序?qū)oints中的數(shù)填入res中 # 將四個(gè)坐標(biāo)x與y相加,和最大的那個(gè)是右下角的坐標(biāo),最小的那個(gè)是左上角的坐標(biāo) sum_hang = points.sum(axis=1) res[0] = points[np.argmin(sum_hang)] res[2] = points[np.argmax(sum_hang)] # 計(jì)算坐標(biāo)x與y的離散插值np.diff() diff = np.diff(points, axis=1) res[1] = points[np.argmin(diff)] res[3] = points[np.argmax(diff)] # 返回result return res
輪廓識(shí)別——識(shí)別出選項(xiàng)
# 輪廓識(shí)別——識(shí)別出選項(xiàng) thresh = cv.threshold(warped, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)[1] cvshow('thresh', thresh) thresh_cnts, _ = cv.findContours(thresh, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) w_copy = warped.copy() cv.drawContours(w_copy, thresh_cnts, -1, (0, 0, 255), 2) cvshow('warped_contours', w_copy) questionCnts = [] # 遍歷,挑出選項(xiàng)的cnts for c in thresh_cnts: (x, y, w, h) = cv.boundingRect(c) ar = w / float(h) # 根據(jù)實(shí)際情況指定標(biāo)準(zhǔn) if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1: questionCnts.append(c) # 檢查是否挑出了選項(xiàng) w_copy2 = warped.copy() cv.drawContours(w_copy2, questionCnts, -1, (0, 0, 255), 2) cvshow('questionCnts', w_copy2)
成功將無關(guān)輪廓剔除
檢測每一行選擇的是哪一項(xiàng),并將結(jié)果儲(chǔ)存起來,記錄正確的個(gè)數(shù)
# 檢測每一行選擇的是哪一項(xiàng),并將結(jié)果儲(chǔ)存在元組bubble中,記錄正確的個(gè)數(shù)correct # 按照從上到下t2b對輪廓進(jìn)行排序 questionCnts = sort_contours(questionCnts, method="t2b")[0] correct = 0 # 每行有5個(gè)選項(xiàng) for (i, q) in enumerate(np.arange(0, len(questionCnts), 5)): # 排序 cnts = sort_contours(questionCnts[q:q+5])[0] bubble = None # 得到每一個(gè)選項(xiàng)的mask并填充,與正確答案進(jìn)行按位與操作獲得重合點(diǎn)數(shù) for (j, c) in enumerate(cnts): mask = np.zeros(thresh.shape, dtype='uint8') cv.drawContours(mask, [c], -1, 255, -1) # cvshow('mask', mask) # 通過按位與操作得到thresh與mask重合部分的像素?cái)?shù)量 bitand = cv.bitwise_and(thresh, thresh, mask=mask) totalPixel = cv.countNonZero(bitand) if bubble is None or bubble[0] < totalPixel: bubble = (totalPixel, j) k = bubble[1] color = (0, 0, 255) if k == right_key[i]: correct += 1 color = (0, 255, 0) # 繪圖 cv.drawContours(warped, [cnts[right_key[i]]], -1, color, 3) cvshow('final', warped)
def sort_contours(contours, method="l2r"): # 用于給輪廓排序,l2r, r2l, t2b, b2t reverse = False i = 0 if method == "r2l" or method == "b2t": reverse = True if method == "t2b" or method == "b2t": i = 1 boundingBoxes = [cv.boundingRect(c) for c in contours] (contours, boundingBoxes) = zip(*sorted(zip(contours, boundingBoxes), key=lambda a: a[1][i], reverse=reverse)) return contours, boundingBoxes
用透過mask的像素的個(gè)數(shù)來判斷考生選擇的是哪個(gè)選項(xiàng)
計(jì)算最終得分并在圖中標(biāo)注
# 計(jì)算最終得分并在圖中標(biāo)注 score = (correct / 5.0) * 100 print(f"Score: {score}%") cv.putText(warped, f"Score: {score}%", (10, 30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) cv.imshow("Original", img) cv.imshow("Exam", warped) cv.waitKey(0)
完整代碼
import cv2 as cv import numpy as np def cvshow(name, img): cv.imshow(name, img) cv.waitKey(0) cv.destroyAllWindows() def four_point_transform(img, four_points): rect = order_points(four_points) (tl, tr, br, bl) = rect # 計(jì)算輸入的w和h的值 widthA = np.sqrt((tr[0] - tl[0]) ** 2 + (tr[1] - tl[1]) ** 2) widthB = np.sqrt((br[0] - bl[0]) ** 2 + (br[1] - bl[1]) ** 2) maxWidth = max(int(widthA), int(widthB)) heightA = np.sqrt((tl[0] - bl[0]) ** 2 + (tl[1] - bl[1]) ** 2) heightB = np.sqrt((tr[0] - br[0]) ** 2 + (tr[1] - br[1]) ** 2) maxHeight = max(int(heightA), int(heightB)) # 變換后對應(yīng)的坐標(biāo)位置 dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype='float32') # 最主要的函數(shù)就是 cv2.getPerspectiveTransform(rect, dst) 和 cv2.warpPerspective(image, M, (maxWidth, maxHeight)) M = cv.getPerspectiveTransform(rect, dst) warped = cv.warpPerspective(img, M, (maxWidth, maxHeight)) return warped def order_points(points): res = np.zeros((4, 2), dtype='float32') # 按照從前往后0,1,2,3分別表示左上、右上、右下、左下的順序?qū)oints中的數(shù)填入res中 # 將四個(gè)坐標(biāo)x與y相加,和最大的那個(gè)是右下角的坐標(biāo),最小的那個(gè)是左上角的坐標(biāo) sum_hang = points.sum(axis=1) res[0] = points[np.argmin(sum_hang)] res[2] = points[np.argmax(sum_hang)] # 計(jì)算坐標(biāo)x與y的離散插值np.diff() diff = np.diff(points, axis=1) res[1] = points[np.argmin(diff)] res[3] = points[np.argmax(diff)] # 返回result return res def sort_contours(contours, method="l2r"): # 用于給輪廓排序,l2r, r2l, t2b, b2t reverse = False i = 0 if method == "r2l" or method == "b2t": reverse = True if method == "t2b" or method == "b2t": i = 1 boundingBoxes = [cv.boundingRect(c) for c in contours] (contours, boundingBoxes) = zip(*sorted(zip(contours, boundingBoxes), key=lambda a: a[1][i], reverse=reverse)) return contours, boundingBoxes # 正確答案 right_key = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1} # 輸入圖像 img = cv.imread('./images/test_01.jpg') img_copy = img.copy() img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) cvshow('img-gray', img_gray) # 圖像預(yù)處理 # 高斯降噪 img_gaussian = cv.GaussianBlur(img_gray, (5, 5), 1) cvshow('gaussianblur', img_gaussian) # canny邊緣檢測 img_canny = cv.Canny(img_gaussian, 80, 150) cvshow('canny', img_canny) # 輪廓識(shí)別——答題卡邊緣識(shí)別 cnts, hierarchy = cv.findContours(img_canny, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) cv.drawContours(img_copy, cnts, -1, (0, 0, 255), 3) cvshow('contours-show', img_copy) docCnt = None # 確保檢測到了 if len(cnts) > 0: # 根據(jù)輪廓大小進(jìn)行排序 cnts = sorted(cnts, key=cv.contourArea, reverse=True) # 遍歷每一個(gè)輪廓 for c in cnts: # 近似 peri = cv.arcLength(c, True) # arclength 計(jì)算一段曲線的長度或者閉合曲線的周長; # 第一個(gè)參數(shù)輸入一個(gè)二維向量,第二個(gè)參數(shù)表示計(jì)算曲線是否閉合 approx = cv.approxPolyDP(c, 0.02 * peri, True) # 用一條頂點(diǎn)較少的曲線/多邊形來近似曲線/多邊形,以使它們之間的距離<=指定的精度; # c是需要近似的曲線,0.02*peri是精度的最大值,True表示曲線是閉合的 # 準(zhǔn)備做透視變換 if len(approx) == 4: docCnt = approx break # 透視變換——提取答題卡主體 docCnt = docCnt.reshape(4, 2) warped = four_point_transform(img_gray, docCnt) cvshow('warped', warped) # 輪廓識(shí)別——識(shí)別出選項(xiàng) thresh = cv.threshold(warped, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)[1] cvshow('thresh', thresh) thresh_cnts, _ = cv.findContours(thresh, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) w_copy = warped.copy() cv.drawContours(w_copy, thresh_cnts, -1, (0, 0, 255), 2) cvshow('warped_contours', w_copy) questionCnts = [] # 遍歷,挑出選項(xiàng)的cnts for c in thresh_cnts: (x, y, w, h) = cv.boundingRect(c) ar = w / float(h) # 根據(jù)實(shí)際情況指定標(biāo)準(zhǔn) if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1: questionCnts.append(c) # 檢查是否挑出了選項(xiàng) w_copy2 = warped.copy() cv.drawContours(w_copy2, questionCnts, -1, (0, 0, 255), 2) cvshow('questionCnts', w_copy2) # 檢測每一行選擇的是哪一項(xiàng),并將結(jié)果儲(chǔ)存在元組bubble中,記錄正確的個(gè)數(shù)correct # 按照從上到下t2b對輪廓進(jìn)行排序 questionCnts = sort_contours(questionCnts, method="t2b")[0] correct = 0 # 每行有5個(gè)選項(xiàng) for (i, q) in enumerate(np.arange(0, len(questionCnts), 5)): # 排序 cnts = sort_contours(questionCnts[q:q+5])[0] bubble = None # 得到每一個(gè)選項(xiàng)的mask并填充,與正確答案進(jìn)行按位與操作獲得重合點(diǎn)數(shù) for (j, c) in enumerate(cnts): mask = np.zeros(thresh.shape, dtype='uint8') cv.drawContours(mask, [c], -1, 255, -1) cvshow('mask', mask) # 通過按位與操作得到thresh與mask重合部分的像素?cái)?shù)量 bitand = cv.bitwise_and(thresh, thresh, mask=mask) totalPixel = cv.countNonZero(bitand) if bubble is None or bubble[0] < totalPixel: bubble = (totalPixel, j) k = bubble[1] color = (0, 0, 255) if k == right_key[i]: correct += 1 color = (0, 255, 0) # 繪圖 cv.drawContours(warped, [cnts[right_key[i]]], -1, color, 3) cvshow('final', warped) # 計(jì)算最終得分并在圖中標(biāo)注 score = (correct / 5.0) * 100 print(f"Score: {score}%") cv.putText(warped, f"Score: {score}%", (10, 30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) cv.imshow("Original", img) cv.imshow("Exam", warped) cv.waitKey(0)
以上是“如何使用OpenCV-Python實(shí)現(xiàn)識(shí)別答題卡判卷功能”這篇文章的所有內(nèi)容,感謝各位的閱讀!相信大家都有了一定的了解,希望分享的內(nèi)容對大家有所幫助,如果還想學(xué)習(xí)更多知識(shí),歡迎關(guān)注億速云行業(yè)資訊頻道!
免責(zé)聲明:本站發(fā)布的內(nèi)容(圖片、視頻和文字)以原創(chuàng)、轉(zhuǎn)載和分享為主,文章觀點(diǎn)不代表本網(wǎng)站立場,如果涉及侵權(quán)請聯(lián)系站長郵箱:is@yisu.com進(jìn)行舉報(bào),并提供相關(guān)證據(jù),一經(jīng)查實(shí),將立刻刪除涉嫌侵權(quán)內(nèi)容。