def __init__(self, path, show_steps=False, save=False):
self.image = Helpers.load_image(path)
# build the prepocessing pipleine
pipeline = Pipeline([
Helpers.convert_to_grayscale, lambda image: Helpers.blur(image, 5),
Helpers.thresholdify, Helpers.ellipse_morph
])
processed_image = pipeline.process_pipeline(self.image)
# get the contour, crop it out, find the corners and straighten
contour = Helpers.largest_contour(processed_image)
processed_image_cropped = Helpers.cut_out_rect(processed_image,
contour)
corners = Helpers.get_corners(processed_image_cropped)
# apply the same cropping and warping to the original image
image_cropped = Helpers.cut_out_rect(self.image, contour)
straigtened_image = Helpers.warp_perspective(corners, image_cropped)
if show_steps:
Helpers.show(processed_image, 'Preprocessing')
Helpers.show(processed_image_cropped, 'Processed image cropped')
Helpers.show(image_cropped, 'Original image cropped')
Helpers.show(straigtened_image, 'Final image')
self.final = straigtened_image
if save:
Helpers.save_image(
f'{Helpers.IMAGE_DIRECTORY}/extractor_finish.png', self.final)
return None
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
# print("height", height)
# print("width", width)
# print("d x", dx)
# print("d y", dy)
# print(beliefs)
# print("blu rring", blurring)
new_G = [[0.0 for i in range(width)] for j in range(height)]
for i, row in enumerate(beliefs):
for j, cell in enumerate(row):
# print("i", i)
# print("j", j)
if (i + dy) >= 0 and (i + dy) < height:
new_i = (i + dy) % width
else:
new_i = ((i + dy) % width) - width
if (j + dx) >= 0 and (i + dy) < width:
new_j = (j + dx) % height
else:
new_j = ((j + dx) % height) - height
# print("new_i", new_i)
# print("new_j", new_j)
# pdb.set_trace()
new_G[int(new_i)][int(new_j)] = cell
return blur(new_G, blurring)
def get_student_responses(self):
student_responses = {}
for num, cell in enumerate(self.cells):
# clean the cell
gray = Helpers.convert_to_grayscale(cell)
thresh = self.cell_pipeline.process_pipeline(cell)
corners = self.find_corners_inside_largest_contour(thresh, gray)
warp = Helpers.warp_perspective(corners,
gray,
size="same",
verbose=False)
#Helpers.show(warp, "clean cell")
# find the contour of the text
warp = Helpers.dilate(warp, 1)
blur = Helpers.blur(warp, 3)
thresh = cv2.threshold(blur, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
contours = Helpers.find_contours(255 - thresh,
mode_type="external",
min_area=20)
contours = Helpers.sort_contours(contours, method="left-to_right")
rects = []
for contour in contours:
rect = Helpers.cut_out_rect(warp, contour)
rects.append(rect)
student_responses[num] = rects
return student_responses
def move(self, dy, dx ):
new_beliefs = [[0.0 for i in range(self.width)] for j in range(self.height)]
for i in range(self.height):
for j in range(self.width):
previous_belief = self.beliefs[i][j]
new_i = (i + dy) % self.height
new_j = (j + dx) % self.width
new_beliefs[int(new_i)][int(new_j)] = previous_belief
self.beliefs = blur(new_beliefs)
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
new_G = [[0.0 for i in range(width)] for j in range(height)]
for i, row in enumerate(beliefs):
for j, cell in enumerate(row):
new_i = (i + dy) % height
new_j = (j + dx) % width
new_G[int(new_i)][int(new_j)] = cell
return blur(new_G, blurring)
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
new_G = [[0.0 for i in range(width)] for j in range(height)]
for i in range(height):
for j in range(width):
new_i = (i + dy) % height
new_j = (j + dx) % width
# pdb.set_trace()
new_G[int(new_i)][int(new_j)] = beliefs[i][j]
return blur(new_G, blurring)
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
new_G = [[0.0 for i in range(width)] for j in range(height)]
for i, row in enumerate(beliefs):
for j, cell in enumerate(row):
# debugging info : the "height" and "width" in the modulo part was interchanged.
new_i = (i + dy) % height
new_j = (j + dx) % width
new_G[int(new_i)][int(new_j)] = cell
return blur(new_G, blurring)
def move(dy, dx, beliefs, blurring):
height = len(beliefs) #rows
width = len(beliefs[0]) #cols
new_G = [[0.0 for j in range(width)] for i in range(height)]
for i, row in enumerate(beliefs): #row
for j, cell in enumerate(row): #col
new_i = (i + dx) % height
new_j = (j + dy) % width
#pdb.set_trace()
new_G[int(new_i)][int(new_j)] = cell
return blur(new_G, blurring)
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
new_G = [[0.0 for i in range(width)] for j in range(height)]
for i, row in enumerate(beliefs):
for j, cell in enumerate(row):
# There was error in the height and wisth positions in new_i & new_j; sol : change them interchangably
new_i = (i + dy ) % height
new_j = (j + dx ) % width
# pdb.set_trace()
new_G[int(new_i)][int(new_j)] = cell
return blur(new_G, blurring)
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
new_G = [[0.0 for i in range(width)] for j in range(height)]
for i, row in enumerate(beliefs):
# i corresponds to height
for j, cell in enumerate(row):
# j corresponds to width
new_i = (i + dy ) % height
new_j = (j + dx ) % width #height
# pdb.set_trace()
new_G[int(new_i)][int(new_j)] = cell
return blur(new_G, blurring)
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
new_G = [[0.0 for i in range(width)] for j in range(height)]
# print("new_G",(len(new_G),len(new_G[0])),(dy,dx))
for i, row in enumerate(beliefs):
for j, cell in enumerate(row):
new_i = (i + dy) % (height)
new_j = (j + dx) % (width)
# pdb.set_trace()
# print(new_i,new_j)
new_G[int(new_i)][int(new_j)] = cell
return blur(new_G, blurring)
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
new_G = [[0.0 for i in range(width)]
for j in range(height)] #np.zeros((width, height)) #
for y, row in enumerate(beliefs):
for x, cell in enumerate(row):
new_y = (y + dy) % height
new_x = (x + dx) % width
#print("y:", y, "x:", x, '\n')
#print("new y:", new_y, "new x:", new_x, '\n')
#pdb.set_trace()
new_G[int(new_y)][int(new_x)] = cell
return blur(new_G, blurring)
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
#new_G = [[0.0 for i in range(height)] for j in range(width)]
new_G = [None] * height
for i in range(height):
new_G[i] = [None] * width
for i, row in enumerate(beliefs):
for j, cell in enumerate(row):
new_i = (i + dy) % (height)
new_j = (j + dx) % (width)
# pdb.set_trace()
new_G[int(new_i)][int(new_j)] = cell
return blur(new_G, blurring)
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
new_G = [[0.0 for i in range(width)] for j in range(height)]
for i, row in enumerate(beliefs):
new_i = (i + dy) % height
for j, cell in enumerate(row):
new_j = (j + dx) % width
#debugger, pasue code, and read temp value
#pdb.set_trace()
new_G[int(new_i)][int(new_j)] = cell
return blur(new_G, blurring)
def captureHistogram(source=None):
source = 0 if source is None else source
if source is not None and str(type(source)) != "<class 'int'>":
raise ValueError("source: integer value expected")
cap = cv2.VideoCapture(source)
boxSize = 100
centerX = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH) / 2)
centerY = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) / 3)
padding = boxSize + 10
while True:
_, frame = cap.read()
frame = cv2.flip(frame, 1)
box1 = (centerY + padding * 0, centerX + padding * 0, boxSize, boxSize)
box2 = (centerY + padding * 0, centerX + padding * 1, boxSize, boxSize)
box3 = (centerY + padding * 1, centerX + padding * 0, boxSize, boxSize)
box4 = (centerY + padding * 1, centerX + padding * 1, boxSize, boxSize)
box5 = (centerY + padding * 2, centerX + padding * 0, boxSize, boxSize)
box6 = (centerY + padding * 2, centerX + padding * 1, boxSize, boxSize)
helpers.drawBoxes(frame, helpers.PURPLE, [box1, box2, box3, box4])
boxOne = helpers.crop(frame, box1)
boxTwo = helpers.crop(frame, box2)
boxThree = helpers.crop(frame, box3)
boxFour = helpers.crop(frame, box4)
boxFourBlurred = helpers.blur(boxFour, 15)
helpers.copyTo(frame, box5, boxFour)
helpers.copyTo(frame, box6, boxFourBlurred)
cv2.imshow("Video Feed", frame)
key = cv2.waitKey(10)
if key == 97:
objectColor = finalHistImage
cv2.destroyAllWindows()
break
if key == 27:
cv2.destroyAllWindows()
cap.release()
exit()
hsvObjectColor = cv2.cvtColor(objectColor, cv2.COLOR_BGR2HSV)
objectHist = cv2.calcHist([hsvObjectColor], [0, 1], None, [12, 15],
[0, 180, 0, 256])
cv2.normalize(objectHist, objectHist, 0, 255, cv2.NORM_MINMAX)
return objectHist
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
new_G = [[0.0 for i in range(width)] for j in range(height)]
for i, row in enumerate(beliefs):
for j, cell in enumerate(row):
new_i = (i + dy) % width
new_j = (j + dx) % height
#pdb.set_trace()
if new_i >= height:
new_i = new_i - height
if new_j >= width:
new_j = new_j - width
new_G[int(new_i)][int(new_j)] = cell
return blur(new_G, blurring)
def process_image(self):
'''
Process the image by thresholding and other techniques to make the cells easy to extract.
Return value
----------
returns a black and white version of the image
'''
# build the prepocessing pipleine
pipeline = Pipeline([
Helpers.convert_to_grayscale, lambda image: Helpers.blur(image, 5),
Helpers.thresholdify, Helpers.ellipse_morph
])
return pipeline.process_pipeline(self.image)
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
new_G = [[0.0 for i in range(width)] for j in range(height)]
#print new_G[0][3]
#print new_G
for i, row in enumerate(beliefs):
for j, cell in enumerate(row):
new_i = (
i + dy
) % height #fix - previousltheight and width were interchanged
new_j = (j + dx) % width
#pdb.set_trace()
new_G[int(new_i)][int(new_j)] = cell
return blur(new_G, blurring)
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
new_G = [[0.0 for i in range(width)] for j in range(height)]
for i, row in enumerate(beliefs):
for j, cell in enumerate(row):
new_i = (
i + dy
) % height # the problem was here. It was being modulo'ed with width instead of height
new_j = (
j + dx
) % width # the problem was here. It was being modulo'ed with height instead of width
# pdb.set_trace()
new_G[int(new_i)][int(new_j)] = cell
return blur(new_G, blurring)
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
new_G = [[0.0 for i in range(width)] for j in range(height)]
heighti = len(new_G)
widthi = len(new_G[0])
for i, row in enumerate(beliefs):
for j, cell in enumerate(row):
new_i = (i - dy ) % height
new_j = (j - dx ) % width
#pdb.set_trace()
new_G[int(new_i)][int(new_j)] = cell
return blur(new_G, blurring)
def move(dy, dx, beliefs, blurring):
height = len(beliefs)
width = len(beliefs[0])
#print('height', height)
#print('width', width)
new_G = [[0.0 for i in range(width)] for j in range(height)]
#print('new_G', new_G)
for i, row in enumerate(beliefs):
new_i = 0
for j, cell in enumerate(row):
new_i = (i + dy) % height
new_j = (j + dx) % width
#pdb.set_trace()
#print('new_i', new_i, 'new_j', new_j, 'dy', dy, 'dx', dx)
new_G[int(new_i)][int(new_j)] = beliefs[i][j]
return blur(new_G, blurring)
def move(dy, dx, beliefs, blurring):
#get the world size
height = len(beliefs)
width = len(beliefs[0])
#creat a new belife grid
new_G = [[0.0 for i in range(width)] for j in range(height)]
#go through the original belief
for i, row in enumerate(beliefs):
for j, cell in enumerate(row):
#calculate new position
new_i = (i + dy) % height
new_j = (j + dx) % width
#pdb.set_trace()
#update beliefs
new_G[int(new_i)][int(new_j)] = cell
#movement uncertainty
return blur(new_G, blurring)
from helpers import blur new_G = [[0 for i in range(3)] for j in range(3)] new_G[1][1] = 1 print(blur(new_G, 0.12))
