def process(imgInput):
width = len(imgInput[0])
height = len(imgInput)
# Threshold
print("[OMR Image Preprocessing] Thresholding...")
imgBinary = omr_threshold_image.threshold(imgInput)
# Find largest connected component
print("[OMR Image Preprocessing] Finding largest connected component... (may take a few minutes)")
imgLCC = largestConnectedComponent(imgBinary)
# Fill page with white pixels
print("[OMR Image Preprocessing] Filling page with white pixels...")
imgPageFilled = fillPage(imgLCC)
# Edge detection
print("[OMR Image Preprocessing] Performing edge detection...")
imgEdges = cv2.Canny(imgPageFilled,50,150)
# Dilate edges
print("[OMR Image Preprocessing] Dilating edges...")
dilationKernel = np.ones((8,8),np.uint8)
imgEdgesDilated = cv2.dilate(imgEdges,dilationKernel,iterations=1)
# Hough line transform
print("[OMR Image Preprocessing] Performing Hough line transform...")
houghLines = cv2.HoughLines(imgEdgesDilated, 1, np.pi/180, min(width/2,height/2))
# Sort lines by rho value
sortedHoughLines = sorted(houghLines[0],key=lambda x : x[0])
"""
for i in range(0,len(sortedHoughLines)):
if (sortedHoughLines[i][0] < 0):
sortedHoughLines[i][0] *= (-1)
sortedHoughLines[i][1] = (sortedHoughLines[i][1] + np.pi)%(2*np.pi)
"""
distanceThreshold = 1
while (len(sortedHoughLines) > 4):
nextHoughLines = []
length = len(sortedHoughLines)
skip = False
for i in range(0,length):
rho1 = sortedHoughLines[i][0]
rho2 = sortedHoughLines[(i+1)%length][0]
theta1 = sortedHoughLines[i][1]
theta2 = sortedHoughLines[(i+1)%length][1]
distance = math.sqrt(rho1*rho1 + rho2*rho2 - 2*rho1*rho2*np.cos(theta2 - theta1))
if (not(skip)):
if (distance < distanceThreshold):
#nextHoughLines.append(sortedHoughLines[i])
nextHoughLines.append(np.array([(rho1 + rho2)/2,(theta1 + theta2)/2]))
skip = True
else:
nextHoughLines.append(sortedHoughLines[i])
else:
skip = False
distanceThreshold += 1
sortedHoughLines = copy.deepcopy(nextHoughLines)
print('New iteration:')
for rho,theta in sortedHoughLines:
print('rho: ' + str(rho) + ' theta: ' + str(theta))
# Highlight lines on original image
for rho,theta in sortedHoughLines:
print('rho: ' + str(rho) + ' theta: ' + str(theta))
cosTheta = np.cos(theta)
sinTheta = np.sin(theta)
x0 = cosTheta*rho
y0 = sinTheta*rho
length = max(width,height)
x1 = int(x0 + length * (-sinTheta))
y1 = int(y0 + length * (cosTheta))
x2 = int(x0 - length * (-sinTheta))
y2 = int(y0 - length * (cosTheta))
cv2.line(imgInput,(x1,y1),(x2,y2),(0,0,255),2)
# Perform perspective transform
intersectionPoints = []
for i in range(0,len(sortedHoughLines)-1):
for j in range(i+1,len(sortedHoughLines)):
intersectionPoints.append(intersectPolarLines(sortedHoughLines[i][0],sortedHoughLines[i][1],sortedHoughLines[j][0],sortedHoughLines[j][1]))
print(intersectionPoints)
finalIntersectionPoints = []
for point in intersectionPoints:
if (not(point == None)):
x = point[0]
y = point[1]
if (not(x < 0 or x > width-1) and not(y < 0 or y > height-1)):
finalIntersectionPoints.append((int(x),int(y)))
finalIntersectionPoints = np.array(finalIntersectionPoints,dtype='float32')
print(finalIntersectionPoints)
transformMatrix = cv2.getPerspectiveTransform(finalIntersectionPoints,np.array([(0,0),(0,height),(width,0),(width,height)],dtype='float32'))
outputImage = cv2.warpPerspective(imgBinary,transformMatrix,(width,height))
return outputImage
# Output the thresholded image, the page outline and the hough transform output on the original image to two separate jpg files
"""
def process(imgInput):
width = len(imgInput[0])
height = len(imgInput)
# Threshold
print("[OMR Image Preprocessing] Thresholding...")
imgBinary = omr_threshold_image.threshold(imgInput)
# Find largest connected component
print(
"[OMR Image Preprocessing] Finding largest connected component... (may take a few minutes)"
)
imgLCC = largestConnectedComponent(imgBinary)
# Fill page with white pixels
print("[OMR Image Preprocessing] Filling page with white pixels...")
imgPageFilled = fillPage(imgLCC)
# Edge detection
print("[OMR Image Preprocessing] Performing edge detection...")
imgEdges = cv2.Canny(imgPageFilled, 50, 150)
# Dilate edges
print("[OMR Image Preprocessing] Dilating edges...")
dilationKernel = np.ones((8, 8), np.uint8)
imgEdgesDilated = cv2.dilate(imgEdges, dilationKernel, iterations=1)
# Hough line transform
print("[OMR Image Preprocessing] Performing Hough line transform...")
houghLines = cv2.HoughLines(imgEdgesDilated, 1, np.pi / 180,
min(width / 2, height / 2))
# Sort lines by rho value
sortedHoughLines = sorted(houghLines[0], key=lambda x: x[0])
"""
for i in range(0,len(sortedHoughLines)):
if (sortedHoughLines[i][0] < 0):
sortedHoughLines[i][0] *= (-1)
sortedHoughLines[i][1] = (sortedHoughLines[i][1] + np.pi)%(2*np.pi)
"""
distanceThreshold = 1
while (len(sortedHoughLines) > 4):
nextHoughLines = []
length = len(sortedHoughLines)
skip = False
for i in range(0, length):
rho1 = sortedHoughLines[i][0]
rho2 = sortedHoughLines[(i + 1) % length][0]
theta1 = sortedHoughLines[i][1]
theta2 = sortedHoughLines[(i + 1) % length][1]
distance = math.sqrt(rho1 * rho1 + rho2 * rho2 -
2 * rho1 * rho2 * np.cos(theta2 - theta1))
if (not (skip)):
if (distance < distanceThreshold):
#nextHoughLines.append(sortedHoughLines[i])
nextHoughLines.append(
np.array([(rho1 + rho2) / 2, (theta1 + theta2) / 2]))
skip = True
else:
nextHoughLines.append(sortedHoughLines[i])
else:
skip = False
distanceThreshold += 1
sortedHoughLines = copy.deepcopy(nextHoughLines)
print('New iteration:')
for rho, theta in sortedHoughLines:
print('rho: ' + str(rho) + ' theta: ' + str(theta))
# Highlight lines on original image
for rho, theta in sortedHoughLines:
print('rho: ' + str(rho) + ' theta: ' + str(theta))
cosTheta = np.cos(theta)
sinTheta = np.sin(theta)
x0 = cosTheta * rho
y0 = sinTheta * rho
length = max(width, height)
x1 = int(x0 + length * (-sinTheta))
y1 = int(y0 + length * (cosTheta))
x2 = int(x0 - length * (-sinTheta))
y2 = int(y0 - length * (cosTheta))
cv2.line(imgInput, (x1, y1), (x2, y2), (0, 0, 255), 2)
# Perform perspective transform
intersectionPoints = []
for i in range(0, len(sortedHoughLines) - 1):
for j in range(i + 1, len(sortedHoughLines)):
intersectionPoints.append(
intersectPolarLines(sortedHoughLines[i][0],
sortedHoughLines[i][1],
sortedHoughLines[j][0],
sortedHoughLines[j][1]))
print(intersectionPoints)
finalIntersectionPoints = []
for point in intersectionPoints:
if (not (point == None)):
x = point[0]
y = point[1]
if (not (x < 0 or x > width - 1)
and not (y < 0 or y > height - 1)):
finalIntersectionPoints.append((int(x), int(y)))
finalIntersectionPoints = np.array(finalIntersectionPoints,
dtype='float32')
print(finalIntersectionPoints)
transformMatrix = cv2.getPerspectiveTransform(
finalIntersectionPoints,
np.array([(0, 0), (0, height), (width, 0), (width, height)],
dtype='float32'))
outputImage = cv2.warpPerspective(imgBinary, transformMatrix,
(width, height))
return outputImage
# Output the thresholded image, the page outline and the hough transform output on the original image to two separate jpg files
"""
import sys
import cv2
import omr_threshold_image
import omr_classes
import omr_staff_line_detection
import omr_staff_line_removal
import omr_recognition
import omr_reconstruction
import omr_bar_line_detection
img = cv2.imread(sys.argv[1],0)
# Threshold
img = omr_threshold_image.threshold(img)
# Copy of thresholded image for recognition output
imgRecognitionOutput = img.copy()
imgRecognitionOutput = cv2.cvtColor(imgRecognitionOutput,cv2.COLOR_GRAY2RGB)
# Get staff line detection data
staffData = omr_staff_line_detection.getStaffData(img)
# Remove staff lines
#img = omr_staff_line_removal.removeStaffLines(img,staffData)
# Test removeStaffLinesSP
img = omr_staff_line_removal.removeStaffLinesSP(img,staffData)
raw_input("Press Enter to continue...")
# Perform recognition
musicalObjects = omr_recognition.performRecognition(img,staffData)
