def main():
""" Main entry point of the app """
usr_input = input(
'Select the Video\n\t1. Tag0 \n\t2. Tag1 \n\t3. Tag2 \n\t4. multipleTags\n\nYour Choice: '
)
print(getVideoFile(int(usr_input)))
cap = cv2.VideoCapture(getVideoFile(int(usr_input)))
font = cv2.FONT_HERSHEY_SIMPLEX
Xc = np.array([[0, 0], [199, 0], [199, 199], [0, 199]])
while (cap.isOpened()):
ret, frame = cap.read()
if frame is not None:
frame = np.array(frame, dtype=np.uint8)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
corner_points_temp, dst_total, frame = getCornerPoints(frame)
for tag_no in range(0, (len(corner_points_temp))):
corner_points = corner_points_temp[0]
H = homographicTransform(corner_points, Xc)
virtualCube(H, frame,
corner_points[4 * tag_no:4 * tag_no + 4][:])
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
cv2.destroyAllWindows()
def main():
""" Main entry point of the app """
usr_input = input(
'Select the Video\n\t1. Tag0 \n\t2. Tag1 \n\t3. Tag2 \n\t4. multipleTags\n\nYour Choice: '
)
print(getVideoFile(int(usr_input)))
cap = cv2.VideoCapture(getVideoFile(int(usr_input)))
font = cv2.FONT_HERSHEY_SIMPLEX
Xc = np.array([[0, 0], [511, 0], [511, 511], [0, 511]])
while (cap.isOpened()):
ret, frame = cap.read()
if frame is not None:
cv2.imshow('Normal', frame)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
corner_points_temp, dst_total, frame = getCornerPoints(frame)
for tag_no in range(0, (len(corner_points_temp))):
corner_points = corner_points_temp[0]
H = homographicTransform(corner_points, Xc)
h_inv = np.linalg.inv(H)
transformed_image = getTransfomredImage(h_inv, gray, 512)
ID_val, rotations = decode(transformed_image)
temp = frame
frame = superImpose(h_inv, temp, rotations + 1)
cv2.imshow('Superimposed', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
cv2.destroyAllWindows()
def main():
""" Main entry point of the app """
usr_input = input(
'Select the Video\n\t1. Tag0 \n\t2. Tag1 \n\t3. Tag2 \n\t4. multipleTags\n\nYour Choice: ')
print(getVideoFile(int(usr_input)))
cap = cv2.VideoCapture(getVideoFile(int(usr_input)))
font = cv2.FONT_HERSHEY_SIMPLEX
Xc = np.array([[0, 0], [0, 199], [199, 199], [199, 0]])
while(cap.isOpened()):
ret, frame = cap.read()
if frame is not None:
frame = np.array(frame, dtype=np.uint8)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
corner_points_temp, dst_total, frame = getCornerPoints(frame)
for tag_no in range(0, (len(corner_points_temp))):
corner_points = corner_points_temp[0]
H = homographicTransform(corner_points, Xc)
h_inv = np.linalg.inv(H)
transformed_image = getTransfomredImage(h_inv, gray,200)
ID_val,rotation = decode(transformed_image)
cv2.imshow('transformed_image',transformed_image)
cv2.putText(frame,'Tag ' + str(tag_no + 1) + ' value: ' + str(ID_val),(10,100 + 50*tag_no), font, 2, (200,255,155), 2, cv2.LINE_AA)
frame[dst_total>0.01*dst_total.max()]=[0,0,255]
cv2.imshow('Harris corner detector', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
cv2.destroyAllWindows()
def main():
""" Main entry point of the app """
print('in')
usr_input = input(
'Select the Video\n\t1. challenge_video.mp4 \n\t2. project_video.mp4 \n\nYour Choice: '
)
print(getVideoFile(int(usr_input)))
cap = cv2.VideoCapture(getVideoFile(int(usr_input)))
font = cv2.FONT_HERSHEY_SIMPLEX
Xc = np.array([[900, 0], [900, 710], [250, 710], [250, 0]])
Xw = np.array([[685, 450], [1090, 710], [220, 710], [595, 450]])
font = cv2.FONT_HERSHEY_SIMPLEX
L_coef = np.zeros(3)
R_coef = np.zeros(3)
l_coef_arr = list()
r_coef_arr = list()
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
out = cv2.VideoWriter('outpy.avi',
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 25,
(frame_width, frame_height))
while (cap.isOpened()):
ret, frame = cap.read()
if frame is not None:
frame = np.array(frame, dtype=np.uint8)
image_shape = frame.shape
undistorted_img = get_undistort(frame)
segmented_image = colorSegmentation(undistorted_img)
Homography = homographicTransform(Xw, Xc)
transformed_image = getTransfomredImage(
np.linalg.inv(Homography[0]), segmented_image, frame.shape[1],
frame.shape[0])
hist = np.sum(transformed_image, axis=0)
left_lane_hist = np.argmax(hist[0:int(len(hist) / 2)])
right_lane_hist = np.argmax(hist[int(len(hist) / 2):-1]) + int(
len(hist) / 2) - 1
L_coef, R_coef = least_squares(transformed_image, left_lane_hist,
right_lane_hist, L_coef, R_coef)
frame = superImpose(L_coef, R_coef, Homography[0], undistorted_img)
turn, l_coef_arr, r_coef_arr = detect_turn(L_coef, R_coef,
image_shape, l_coef_arr,
r_coef_arr)
cv2.putText(frame, 'Turn: ' + turn, (10, 100), font, 2,
(200, 255, 155), 2, cv2.LINE_AA)
cv2.imshow('Lane Detection', frame)
out.write(frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
cv2.destroyAllWindows()
def main():
""" Main entry point of the app """
cap = cv2.VideoCapture('Tag1.mp4')
while (cap.isOpened()):
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
ret, thresh = cv2.threshold(gray, 240, 255, 0, cv2.THRESH_BINARY)
contours, heirarchy = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
contour_sizes = [(cv2.contourArea(contour), contour)
for contour in contours]
biggest_contour = max(contour_sizes, key=lambda x: x[0])[1]
dst_total = np.zeros(gray.shape, dtype='uint8')
heirarchy = heirarchy[0]
corner_idx_temp = np.zeros((4, 1))
corner_idx = np.zeros((1, 1))
corner_points = np.zeros((1, 2))
for component in zip(contours, heirarchy):
currentHierarchy = component[1]
currentContour = component[0]
size = cv2.minAreaRect(component[0])
isSecond = 0
idx = currentHierarchy[3]
while True:
if idx == -1:
break
isSecond = isSecond + 1
idx = heirarchy[idx][3]
if isSecond == 1:
gray_fl = np.float32(gray)
mask = np.zeros(gray_fl.shape, dtype='uint8')
mask = cv2.GaussianBlur(mask, (3, 3), 0)
cv2.fillPoly(mask, [currentContour], (255, 255, 255))
dst = cv2.cornerHarris(mask, 5, 3, 0.04)
dst = cv2.dilate(dst, None)
ret, dst = cv2.threshold(dst, 0.1 * dst.max(), 255, 0)
dst = np.uint8(dst)
cv2.drawContours(frame, [currentContour], -1, (0, 255, 0), 3)
ret, labels, stats, centroids = cv2.connectedComponentsWithStats(
dst)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER,
100, 0.001)
corners = cv2.cornerSubPix(gray_fl, np.float32(centroids),
(5, 5), (-1, -1), criteria)
dst_total = dst + dst_total
corners = np.delete(corners, (0), axis=0)
print(corners)
if (len(corners) < 4):
break
#corner_idx_temp = sortCorners(corners)
#corners = np.delete(corners, np.setdiff1d(np.arange(len(corners)), corner_idx_temp), axis = 0)
corners = sortCorners(corners)
corner_points = np.concatenate((corner_points, corners),
axis=0)
#corner_idx = np.concatenate((corner_idx, corner_idx_temp), axis = 0)
#corner_idx = np.delete(corner_idx, (0), axis=0)
corner_points = np.delete(corner_points, (0), axis=0)
#corner_idx = np.rint(corner_idx)
#corner_idx = corner_idx.astype(int)
corner_points = (np.rint(corner_points)).astype(int)
#print('corner')
#print(corner_idx)
print('corner_points')
print(corner_points)
total_tags = np.int(len(corner_points) / 4)
print('total_tags')
print(total_tags)
for tag_no in range(0, total_tags):
#print('inside each tag')
#print((corner_points[4*tag_no:4*tag_no+4][:]) )
#print((corner_idx[4*tag_no:4*tag_no+4]))
H = homographicTransform(
corner_points[4 * tag_no:4 * tag_no +
4][:]) #,(corner_idx[4*tag_no:4*tag_no+4]))
transformed_image = np.zeros((200, 200), dtype='uint8')
h_inv = np.linalg.inv(H)
for row in range(0, 200):
for col in range(0, 200):
X_dash = np.array([col, row, 1]).T
X = np.matmul(h_inv, X_dash)
X = (X / X[2])
X = X.astype(int)
transformed_image[col][row] = gray[X[1]][X[0]]
#cv2.imshow('QR_image',transformed_image)
ID_val = decode(transformed_image)
print(ID_val)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
frame_modi = frame
frame_modi[dst_total > 0.01 * dst_total.max()] = [0, 0, 255]
cv2.imshow('Harris corner detector', frame_modi)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def main():
""" Main entry point of the app """
print('in')
usr_input = input(
'Select the Video\n\t1. challenge_video.mp4 \n\t2. project_video.mp4 \n\nYour Choice: '
)
print(getVideoFile(int(usr_input)))
cap = cv2.VideoCapture(getVideoFile(int(usr_input)))
font = cv2.FONT_HERSHEY_SIMPLEX
Xc = np.array([[900, 0], [900, 710], [250, 710], [250, 0]])
#([[200, 150], [1000, 150], [1000,600], [200,600]])
Xw = np.array([[685, 450], [1090, 710], [220, 710],
[595,
450]]) #([[565, 471], [707, 471], [958, 618], [385, 618]])
#Xc = np.array([[149, 0], [249, 0], [249, 399], [149, 399]])
#Xw = np.array([[548, 518], [761, 522], [891, 616], [408, 616]])
# kernel = np.ones((4,4),np.uint8)
font = cv2.FONT_HERSHEY_SIMPLEX
L_coef = np.zeros(3)
R_coef = np.zeros(3)
l_coef_arr = list()
r_coef_arr = list()
# Xw = np.array([[600, 452], [683, 452], [1005, 630], [387, 630]])
while (cap.isOpened()):
ret, frame = cap.read()
if frame is not None:
frame = np.array(frame, dtype=np.uint8)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
image_shape = gray.shape
'''
ret, thresh = cv2.threshold(cropped_image, 150, 255, 0, cv2.THRESH_BINARY)
try:
_,contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
except:
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
'''
undistorted_img = get_undistort(frame)
segmented_image, img_output = colorSegmentation(undistorted_img)
cropped_image = segmented_image.copy()
cropped_image[0:int(image_shape[0] * 1 / 2), :] = 1
# erosion = cv2.erode(segmented_image,kernel,iterations = 1)
# houghTransform(erosion, frame)
Homography = homographicTransform(Xw, Xc)
transformed_image = getTransfomredImage(
np.linalg.inv(Homography[0]), segmented_image, frame.shape[1],
frame.shape[0])
# hist = cv2.calcHist([transformed_image],[0],None,[2],[0,2])
hist = np.sum(transformed_image, axis=0)
edges = cv2.Canny(img_output, 100, 200)
sobelx = cv2.Sobel(undistorted_img, cv2.CV_64F, 1, 0, ksize=5)
#cv2.drawContours(frame, contours, -1, (0, 255, 0), 3)
cv2.imshow('transformed_image', transformed_image)
left_lane_hist = np.argmax(hist[0:int(len(hist) / 2)])
right_lane_hist = np.argmax(hist[int(len(hist) / 2):-1]) + int(
len(hist) / 2) - 1
# image, y_points, x_points = slidingWindowFit(transformed_image, left_lane_hist, right_lane_hist)
lefty, leftx, righty, rightx, L_coef, R_coef = least_squares(
transformed_image, left_lane_hist, right_lane_hist, L_coef,
R_coef)
frame = superImpose(L_coef, R_coef, Homography[0], undistorted_img)
turn, l_coef_arr, r_coef_arr = detect_turn(L_coef, R_coef,
image_shape, l_coef_arr,
r_coef_arr)
cv2.putText(frame, 'Turn: ' + turn, (10, 100), font, 2,
(200, 255, 155), 2, cv2.LINE_AA)
#cv2.imshow('Lane edges', segmented_image)
cv2.imshow('Lane Detection', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
cv2.destroyAllWindows()