def main():
# read input args
if len(sys.argv) != 3:
print "usage: " + sys.argv[0] + " outer_size fps"
return -1
outer_size = int(sys.argv[1])
fps = int(sys.argv[2])
# delete old postit image
old_postit_image = os.listdir('./datas/postit_saved/')
for old_postit in old_postit_image:
os.remove('./datas/postit_saved/' + old_postit)
# memory save
gc.enable()
# set parameter
analyse_config = yaml.load(open("./config/analyse_config.yaml", "r"))
dist_thre = analyse_config["dist_thre"]
angle_thre = analyse_config["angle_thre"]
time_thre = analyse_config["time_thre"]
video_save = False
skip_mode = True
# read movie file list
movie_id = 0
movie_files = os.listdir('./datas/movie/')
if len(movie_files) == 0:
print "no movie"
return -1
else:
print "movie files:"
for movie_file_each in movie_files:
print movie_file_each
# video reader and writer
cap = cv2.VideoCapture('./datas/movie/' + movie_files[0])
if video_save:
writer = cv2.VideoWriter("./datas/movie/OUT.avi",
cv2.cv.CV_FOURCC('M', 'J', 'P', 'G'), fps,
(3840, 2160))
# csv_file
csv_every_second = open("./datas/csv/every_second.csv", "w")
csv_final = open("./datas/csv/final.csv", "w")
# reed solomon
rs = RSCodec(14)
# first select the desk area
ret, frame = cap.read()
window_name = "SELECT DESK"
frame_for_select = cv2.resize(
frame, (int(frame.shape[1] * 0.2), int(frame.shape[0] * 0.2)))
print "Please select left-top and right-down"
cv2.imshow(window_name, frame_for_select)
desk_area = []
cv2.setMouseCallback(window_name, mouse_callback, desk_area)
cv2.waitKey(0)
cv2.destroyWindow(window_name)
desk_area = np.array(desk_area) * 5
# select the mask area
window_name = "SELECT left-top, right-top, right-down"
print "Please select left-top and right-down"
cv2.imshow(
window_name, frame[desk_area[0][1]:desk_area[1][1],
desk_area[0][0]:desk_area[1][0]])
postit_area = []
cv2.setMouseCallback(window_name, mouse_callback, postit_area)
cv2.waitKey(0)
cv2.destroyWindow(window_name)
postit_area_width = np.linalg.norm(
np.array(postit_area[0]) - np.array(postit_area[1]))
postit_area_height = np.linalg.norm(
np.array(postit_area[1]) - np.array(postit_area[2]))
# result file
experiment_result = open("./datas/csv/resolution_result.csv", "w")
experiment_result.write(str(desk_area) + "\n")
# set desk by myself
# desk_area = np.array([[1615, 890], [2110, 1285]])
for movie_id in range(len(movie_files)):
for resolution in range(0, 100):
resolution = float(resolution) * 0.01
print "pattern:" + str(resolution)
time = 0
success_count = 0
postit_saved = {}
cap = cv2.VideoCapture('./datas/movie/' + movie_files[movie_id])
while (1):
# read frame
# print "progress: " + str(time)
ret, frame = cap.read()
if ret == False:
break
# movie_id += 1
# if movie_id < len(movie_files):
# cap = cv2.VideoCapture('./movie/' + movie_files[movie_id])
# ret, frame = cap.read()
# else:
# break
# save frame for final
frame_for_final = np.copy(frame)
# extract only desk area
frame = frame[desk_area[0][1]:desk_area[1][1],
desk_area[0][0]:desk_area[1][0]]
# change resolution
frame = cv2.resize(frame,
(int(frame.shape[1] * (1 - resolution)),
int(frame.shape[0] * (1 - resolution))))
frame_for_save = np.copy(frame)
outer_size_resized = outer_size * ((1 - resolution)**2)
# get postit
getPostits = post_util.getPostits(frame, outer_size_resized)
# postit_image_analyzing = getPostits.postit_image_analyzing
postit_points = getPostits.postit_points
recognized_location_rectangle = getPostits.recognized_location_rectangle
# add buffer
for i in range(0, len(postit_points)):
for j in range(0, len(postit_points[i])):
postit_points[i][j] += desk_area[0]
# read postit's id and save
postit_ids = []
bit_array_list = []
for i in range(0, len(postit_points)):
# postit_image_analyzing = cv2.imread("./postit_tmp_analyzing/" + str(i) + ".jpg")
postit_image_analyzing = np.load(
"./tmp_datas/postit_tmp_analyzing/" + str(i) + ".npy")
bit_array = post_util.readDots(
postit_image_analyzing).bit_array
bit_array_answer = []
for num in rs.encode([62]):
bit_array_answer.append(num)
bit_array_list.append(bit_array)
result_num = -1
try:
result_num = rs.decode(bit_array)[0]
except:
result_num = -1
# success check
# if (recognized_location_rectangle == 8) and bit_array_answer == bit_array:
# success_count += 1
recognized_id_rectangle = 0
for each_bit in zip(bit_array_answer, bit_array):
if each_bit[0] == each_bit[1]:
recognized_id_rectangle += 1
success_count += recognized_id_rectangle + recognized_location_rectangle
postit_ids.append(result_num)
# save postit image
if result_num != -1:
# already exist
if postit_saved.has_key(result_num):
# judge move and rotate
# calc dist
dist = np.linalg.norm(
np.mean(postit_saved[result_num]
["points_saved"],
axis=0) -
np.mean(postit_points[i], axis=0))
# calc angle(degree)
angle_vec_before = np.array(
postit_saved[result_num]["points_saved"][0]
) - np.array(
postit_saved[result_num]["points_saved"][1])
angle_vec_after = np.array(
postit_points[i][0]) - np.array(
postit_points[i][1])
vec_cos = np.dot(
angle_vec_before, angle_vec_after) / (
np.linalg.norm(angle_vec_before) *
np.linalg.norm(angle_vec_after))
angle = np.arccos(vec_cos) * 180 / np.pi
# add information
if dist > dist_thre:
if len(postit_saved[result_num]["move"]) == 0:
postit_saved[result_num]["move"].append(
time / fps)
elif (time / fps -
postit_saved[result_num]["move"][-1]
) > 5:
postit_saved[result_num]["move"].append(
time / fps)
elif angle > angle_thre:
if len(postit_saved[result_num]
["rotate"]) == 0:
postit_saved[result_num]["rotate"].append(
time / fps)
elif (time / fps -
postit_saved[result_num]["rotate"][-1]
) > 5:
postit_saved[result_num]["rotate"].append(
time / fps)
# renew
postit_saved[result_num]["points"] = postit_points[
i]
postit_saved[result_num][
"points_saved"] = postit_points[i]
postit_saved[result_num]["last_time"] = time / fps
# first appear
else:
postit_saved[result_num] = {
"points": postit_points[i],
"points_saved": postit_points[i],
"first_time": time / fps,
"last_time": time / fps,
"move": [],
"rotate": []
}
postit_image_for_save = cv2.imread(
"./tmp_datas/postit_tmp/" + str(i) + ".jpg")
cv2.imwrite(
"./datas/postit_saved/" + str(result_num) +
".jpg", postit_image_for_save)
# delete old postit(long time no see)
for id, val in postit_saved.items():
if (time / fps - val["last_time"]) > time_thre:
postit_saved[id]["points"] = [[-5, 0], [0, 0], [0, -5],
[-5, -5]]
# write csv
csv_util.write_every_second(postit_saved, csv_every_second)
# memory save
del getPostits
gc.collect()
# key waiting
key = cv2.waitKey(1)
if key == 27:
break
# when drawing dict info
for key in postit_saved:
cv2.drawContours(
frame_for_final,
[np.array(postit_saved[key]["points"]).astype(np.int)],
0, (0, 0, 220), 2)
cv2.putText(frame_for_final, str(key),
(np.mean(postit_saved[key]["points"],
axis=0).astype(np.int)[0] - 40,
np.mean(postit_saved[key]["points"],
axis=0).astype(np.int)[1]),
cv2.FONT_HERSHEY_PLAIN, 5.0, (0, 140, 0), 5)
show_img_final = cv2.resize(
frame_for_final, (int(frame_for_final.shape[1] * 0.3),
int(frame_for_final.shape[0] * 0.3)))
cv2.imshow("show2", show_img_final)
# add time
time += 1
if time > 23:
break
# final save
csv_util.write_final(postit_saved, csv_final)
# print success rate
experiment_result.write(
str(int(postit_area_height * (1 - resolution))) + "," +
str(int(postit_area_width * (1 - resolution))) + "," +
str(time) + "," + str(success_count) + "," +
str(float(success_count) / time) + "\n")
cv2.imwrite("./datas/postit_saved/frame.jpg", frame_for_save)
print "-----success result------"
print "resolution:" + str(
int(postit_area_height * (1 - resolution))) + "," + str(
int(postit_area_width * (1 - resolution)))
print "success count: " + str(success_count)
print "time:" + str(time)
print "success rate: " + str(float(success_count) / time)
def main():
# read input args
if len(sys.argv) != 3:
print "usage: " + sys.argv[0] + " outer_size fps"
return -1
outer_size = int(sys.argv[1])
fps = int(sys.argv[2])
# delete old postit image
old_postit_image = os.listdir('./datas/postit_saved/')
for old_postit in old_postit_image:
os.remove('./datas/postit_saved/' + old_postit)
# memory save
gc.enable()
# set parameter
analyse_config = yaml.load(open("./config/analyse_config.yaml", "r"))
dist_thre = analyse_config["dist_thre"]
angle_thre = analyse_config["angle_thre"]
time_thre = analyse_config["time_thre"]
# video_save = False
skip_mode = True
# video reader and writer
cap = cv2.VideoCapture(0)
cap.set(3, 2592)
cap.set(4, 1944)
# csv_file
csv_every_second = open("./datas/csv/every_second.csv", "w")
csv_final = open("./datas/csv/final.csv", "w")
# reed solomon
rs = RSCodec(14)
#first select the desk area
#while 1:
# ret, frame = cap.read()
# if ret:
# break
cols = 860
rows = 400
projector_image = np.zeros((rows, cols, 3), np.int32)
projector_image[:, :] = np.array([0, 255, 255], np.int32)
cv2.imshow("projector", projector_image)
ret, frame = cap.read()
print frame.shape[0]
print frame.shape[1]
window_name = "SELECT DESK"
frame_for_select = cv2.resize(
frame, (int(frame.shape[1] * 0.2), int(frame.shape[0] * 0.2)))
print "Please select left-top and right-down"
cv2.imshow(window_name, frame_for_select)
desk_area = []
while 1:
ret, frame = cap.read()
frame_for_select = cv2.resize(
frame, (int(frame.shape[1] * 0.2), int(frame.shape[0] * 0.2)))
cv2.imshow(window_name, frame_for_select)
cv2.setMouseCallback(window_name, mouse_callback, desk_area)
cv2.waitKey(1)
if len(desk_area) == 2:
break
cv2.destroyWindow(window_name)
desk_area = np.array(desk_area) * 5
print frame.shape[1]
print frame.shape[0]
# postit dictionary
postit_saved = {}
time = 0
while (1):
# skip or not skip
if skip_mode and time % fps != 0:
mimi = 0
# grab frame
#ret = cap.grab()
#if ret == False:
# break
else:
# read frame
print "progress: " + str(int(time / fps) / 60) + ":" + str(
(time / fps) % 60)
ret, frame = cap.read()
if ret == False:
break
# save frame for final
frame_for_final = np.copy(frame)
if time % fps == 0:
# extract only desk area
frame = frame[desk_area[0][1]:desk_area[1][1],
desk_area[0][0]:desk_area[1][0]]
# get postit
getPostits = post_util.getPostits(frame, outer_size)
# postit_image = getPostits.postit_image
# postit_image_analyzing = getPostits.postit_image_analyzing
postit_points = getPostits.postit_points
#add buffer
for i in range(0, len(postit_points)):
for j in range(0, len(postit_points[i])):
postit_points[i][j] += desk_area[0]
# read postit's id and save
postit_ids = []
bit_array_list = []
for i in range(0, len(postit_points)):
# postit_image_analyzing = cv2.imread("./postit_tmp_analyzing/" + str(i) + ".jpg")
postit_image_analyzing = np.load(
"./tmp_datas/postit_tmp_analyzing/" + str(i) + ".npy")
bit_array = post_util.readDots(
postit_image_analyzing).bit_array
bit_array_list.append(bit_array)
result_num = -1
try:
result_num = rs.decode(bit_array)[0]
except:
result_num = -1
postit_ids.append(result_num)
# save postit image
if result_num != -1:
# already exist
if postit_saved.has_key(result_num):
# judge move and rotate
# calc dist
dist = np.linalg.norm(
np.mean(postit_saved[result_num]["points_saved"],
axis=0) -
np.mean(postit_points[i], axis=0))
# calc angle(degree)
angle_vec_before = np.array(
postit_saved[result_num]["points_saved"]
[0]) - np.array(
postit_saved[result_num]["points_saved"][1])
angle_vec_after = np.array(
postit_points[i][0]) - np.array(
postit_points[i][1])
vec_cos = np.dot(angle_vec_before, angle_vec_after) / (
np.linalg.norm(angle_vec_before) *
np.linalg.norm(angle_vec_after))
angle = np.arccos(vec_cos) * 180 / np.pi
# add information
if dist > dist_thre:
if len(postit_saved[result_num]["move"]) == 0:
postit_saved[result_num]["move"].append(time /
fps)
elif (time / fps -
postit_saved[result_num]["move"][-1]) > 5:
postit_saved[result_num]["move"].append(time /
fps)
elif angle > angle_thre:
if len(postit_saved[result_num]["rotate"]) == 0:
postit_saved[result_num]["rotate"].append(
time / fps)
elif (time / fps -
postit_saved[result_num]["rotate"][-1]) > 5:
postit_saved[result_num]["rotate"].append(
time / fps)
# renew
postit_saved[result_num]["points"] = postit_points[i]
postit_saved[result_num][
"points_saved"] = postit_points[i]
postit_saved[result_num]["last_time"] = time / fps
# first appear
else:
postit_saved[result_num] = {
"points": postit_points[i],
"points_saved": postit_points[i],
"first_time": time / fps,
"last_time": time / fps,
"move": [],
"rotate": []
}
postit_image_for_save = cv2.imread(
"./tmp_datas/postit_tmp/" + str(i) + ".jpg")
cv2.imwrite(
"./datas/postit_saved/" + str(result_num) + ".jpg",
postit_image_for_save)
# delete old postit(long time no see)
for id, val in postit_saved.items():
if (time / fps - val["last_time"]) > time_thre:
postit_saved[id]["points"] = [[-5, 0], [0, 0], [0, -5],
[-5, -5]]
# write csv
csv_util.write_every_second(postit_saved, csv_every_second)
# memory save
del getPostits
gc.collect()
#renew viewing
for key in postit_saved:
cv2.drawContours(
frame_for_final,
[np.array(postit_saved[key]["points"]).astype(np.int)], 0,
(0, 0, 255), 2)
cv2.putText(frame_for_final,
str(key), (np.mean(postit_saved[key]["points"],
axis=0).astype(np.int)[0] - 40,
np.mean(postit_saved[key]["points"],
axis=0).astype(np.int)[1]),
cv2.FONT_HERSHEY_PLAIN, 5.0, (0, 240, 0), 5)
show_img_final = cv2.resize(frame_for_final,
(int(frame_for_final.shape[1] * 0.3),
int(frame_for_final.shape[0] * 0.3)))
cv2.imshow("show2", show_img_final)
#projector draw
projector_image = np.zeros((rows, cols, 3), np.uint8)
desk_width = desk_area[1][0] - desk_area[0][0]
desk_height = desk_area[1][1] - desk_area[0][1]
for key in postit_saved:
postit_relative_points = []
for i in range(0, len(postit_saved[key]["points"])):
scale = 0
relative_point = postit_saved[key]["points"][
i] - desk_area[0]
relative_point[0] = int(cols * scale * relative_point[0] /
desk_width)
relative_point[1] = int(rows * scale * relative_point[1] /
desk_height)
postit_relative_points.append(relative_point)
for i in range(0, len(postit_relative_points)):
postit_relative_points[i][0] += int(
0.05 * (postit_relative_points[i][0] -
np.mean(postit_relative_points, axis=0)[0]))
postit_relative_points[i][1] += int(
0.05 * (postit_relative_points[i][1] -
np.mean(postit_relative_points, axis=0)[1]))
pts = np.array(postit_relative_points, np.int32)
cv2.polylines(projector_image, [pts], True, (0, 255, 255))
cv2.putText(projector_image, str(key), (
np.mean(postit_relative_points, axis=0).astype(np.int)[0] -
30,
np.mean(postit_relative_points, axis=0).astype(np.int)[1] +
30), cv2.FONT_HERSHEY_PLAIN, 3.0, (0, 240, 0), 5)
cv2.imshow("projector", projector_image)
# key waiting
key = cv2.waitKey(1)
if key == 27:
break
# add time
time += 1
# final save
csv_util.write_final(postit_saved, csv_final)
def main():
# read input args
if len(sys.argv) != 3:
print "usage: " + sys.argv[0] + " outer_size fps"
return -1
outer_size = int(sys.argv[1])
fps = int(sys.argv[2])
# delete old postit image
old_postit_image = os.listdir('./datas/postit_saved/')
for old_postit in old_postit_image:
os.remove('./datas/postit_saved/' + old_postit)
# memory save
gc.enable()
# set parameter
analyse_config = yaml.load(open("./config/analyse_config.yaml", "r"))
dist_thre = analyse_config["dist_thre"]
angle_thre = analyse_config["angle_thre"]
time_thre = analyse_config["time_thre"]
video_save = True
skip_mode = True
# read movie file list
movie_id = 0
movie_files = os.listdir('./datas/movie/')
if len(movie_files) == 0:
print "no movie"
return -1
else:
print "movie files:"
for movie_file_each in movie_files:
print movie_file_each
# video reader and writer
cap = cv2.VideoCapture('./datas/movie/' + movie_files[0])
# csv_file
csv_every_second = open("./datas/csv/every_second.csv", "w")
csv_final = open("./datas/csv/final.csv", "w")
# reed solomon
rs = RSCodec(14)
# first select the desk area
ret, frame = cap.read()
window_name = "SELECT DESK"
frame_for_select = cv2.resize(
frame, (int(frame.shape[1] * 0.2), int(frame.shape[0] * 0.2)))
print "Please select left-top and right-down"
cv2.imshow(window_name, frame_for_select)
desk_area = []
cv2.setMouseCallback(window_name, mouse_callback, desk_area)
cv2.waitKey(0)
cv2.destroyWindow(window_name)
desk_area = np.array(desk_area) * 5
# video writer
if video_save:
writer = cv2.VideoWriter("./datas/movie/OUT.avi",
cv2.cv.CV_FOURCC('M', 'J', 'P', 'G'), fps,
(frame.shape[1], frame.shape[0]))
# postit dictionary
postit_saved = {}
time = 0
while (1):
# skip or not skip
if skip_mode and time % fps != 0:
# grab frame
ret = cap.grab()
if ret == False:
movie_id += 1
if movie_id < len(movie_files):
cap = cv2.VideoCapture('./datas/movie/' +
movie_files[movie_id])
ret = cap.grab()
else:
break
else:
# read frame
print "progress: " + str(int(time / fps) / 60) + ":" + str(
(time / fps) % 60)
ret, frame = cap.read()
if ret == False:
movie_id += 1
if movie_id < len(movie_files):
cap = cv2.VideoCapture('./datas/movie/' +
movie_files[movie_id])
ret, frame = cap.read()
else:
break
# save frame for final
frame_for_final = np.copy(frame)
if time % fps == 0:
# extract only desk area
frame = frame[desk_area[0][1]:desk_area[1][1],
desk_area[0][0]:desk_area[1][0]]
# get postit
getPostits = post_util.getPostits(frame, outer_size)
# postit_image = getPostits.postit_image
# postit_image_analyzing = getPostits.postit_image_analyzing
postit_points = getPostits.postit_points
# add buffer
for i in range(0, len(postit_points)):
for j in range(0, len(postit_points[i])):
postit_points[i][j] += desk_area[0]
# read postit's id and save
postit_ids = []
bit_array_list = []
for i in range(0, len(postit_points)):
# postit_image_analyzing = cv2.imread("./postit_tmp_analyzing/" + str(i) + ".jpg")
postit_image_analyzing = np.load(
"./tmp_datas/postit_tmp_analyzing/" + str(i) + ".npy")
bit_array = post_util.readDots(
postit_image_analyzing).bit_array
bit_array_list.append(bit_array)
result_num = -1
try:
result_num = rs.decode(bit_array)[0]
except:
result_num = -1
postit_ids.append(result_num)
# save postit image
if result_num != -1:
# already exist
if postit_saved.has_key(result_num):
# judge move and rotate
# calc dist
dist = np.linalg.norm(
np.mean(postit_saved[result_num]["points_saved"],
axis=0) -
np.mean(postit_points[i], axis=0))
# calc angle(degree)
angle_vec_before = np.array(
postit_saved[result_num]["points_saved"]
[0]) - np.array(
postit_saved[result_num]["points_saved"][1])
angle_vec_after = np.array(
postit_points[i][0]) - np.array(
postit_points[i][1])
vec_cos = np.dot(angle_vec_before, angle_vec_after) / (
np.linalg.norm(angle_vec_before) *
np.linalg.norm(angle_vec_after))
angle = np.arccos(vec_cos) * 180 / np.pi
# add information
if dist > dist_thre:
if len(postit_saved[result_num]["move"]) == 0:
postit_saved[result_num]["move"].append(time /
fps)
elif (time / fps -
postit_saved[result_num]["move"][-1]) > 5:
postit_saved[result_num]["move"].append(time /
fps)
elif angle > angle_thre:
if len(postit_saved[result_num]["rotate"]) == 0:
postit_saved[result_num]["rotate"].append(
time / fps)
elif (time / fps -
postit_saved[result_num]["rotate"][-1]) > 5:
postit_saved[result_num]["rotate"].append(
time / fps)
# renew
postit_saved[result_num]["points"] = postit_points[i]
postit_saved[result_num][
"points_saved"] = postit_points[i]
postit_saved[result_num]["last_time"] = time / fps
# first appear
else:
postit_saved[result_num] = {
"points": postit_points[i],
"points_saved": postit_points[i],
"first_time": time / fps,
"last_time": time / fps,
"move": [],
"rotate": []
}
postit_image_for_save = cv2.imread(
"./tmp_datas/postit_tmp/" + str(i) + ".jpg")
cv2.imwrite(
"./datas/postit_saved/" + str(result_num) + ".jpg",
postit_image_for_save)
# delete old postit(long time no see)
for id, val in postit_saved.items():
if (time / fps - val["last_time"]) > time_thre:
postit_saved[id]["points"] = [[-5, 0], [0, 0], [0, -5],
[-5, -5]]
# write csv
csv_util.write_every_second(postit_saved, csv_every_second)
# memory save
del getPostits
gc.collect()
# key waiting
key = cv2.waitKey(1)
if key == 27:
break
if skip_mode == False or time % fps == 0:
# when drawing dict info
for key in postit_saved:
cv2.drawContours(
frame_for_final,
[np.array(postit_saved[key]["points"]).astype(np.int)], 0,
(0, 0, 200), 2)
cv2.putText(frame_for_final,
str(key), (np.mean(postit_saved[key]["points"],
axis=0).astype(np.int)[0] - 40,
np.mean(postit_saved[key]["points"],
axis=0).astype(np.int)[1]),
cv2.FONT_HERSHEY_PLAIN, 5.0, (0, 100, 0), 5)
show_img_final = cv2.resize(frame_for_final,
(int(frame_for_final.shape[1] * 0.3),
int(frame_for_final.shape[0] * 0.3)))
cv2.imshow("show2", show_img_final)
if video_save:
writer.write(frame_for_final)
# add time
time += 1
# final save
csv_util.write_final(postit_saved, csv_final)
def main():
# read input args
if len(sys.argv) != 3:
print("usage: " + sys.argv[0] + " outer_size fps")
return -1
outer_size = int(sys.argv[1])
#outer_size = 1000
fps = int(sys.argv[2])
# delete old postit image
old_postit_image = os.listdir('./datas/postit_saved/')
for old_postit in old_postit_image:
os.remove('./datas/postit_saved/' + old_postit)
# memory save
gc.enable()
# set parameter
analyse_config = yaml.load(open("./config/analyse_config.yaml", "r"))
dist_thre = analyse_config["dist_thre"]
angle_thre = analyse_config["angle_thre"]
time_thre = analyse_config["time_thre"]
# video_save = False
skip_mode = True
# video reader and writer
cap = cv2.VideoCapture(0)
cap.set(3, 2592)
cap.set(4, 1944)
# csv_file
csv_every_second = open("./datas/csv/every_second.csv", "w")
csv_final = open("./datas/csv/final.csv", "w")
# reed solomon
rs = RSCodec(14)
# db connection
conn = sqlite3.connect('husen_kansou.db')
# DBを開く。適合関数・変換関数を有効にする。
conn = sqlite3.connect('husen_kansou.db',
detect_types=sqlite3.PARSE_DECLTYPES
| sqlite3.PARSE_COLNAMES)
# "TIMESTAMP"コンバータ関数 をそのまま ”DATETIME” にも使う
sqlite3.dbapi2.converters['DATETIME'] = sqlite3.dbapi2.converters[
'TIMESTAMP']
c = conn.cursor()
#first select the desk area
while 1:
ret, frame = cap.read()
if ret:
break
print(frame.shape[0])
print(frame.shape[1])
window_name = "SELECT DESK"
frame_for_select = cv2.resize(
frame, (int(frame.shape[1] * 0.2), int(frame.shape[0] * 0.2)))
print("Please select left-top and right-down")
cv2.imshow(window_name, frame_for_select)
desk_area = [[0, 0], [2592, 1944]]
#cv2.setMouseCallback(window_name,mouse_callback, desk_area)
#cv2.waitKey(0)
cv2.destroyWindow(window_name)
desk_area = np.array(desk_area) * 5
print(frame.shape[1])
print(frame.shape[0])
# postit dictionary
postit_saved = {}
time = 0
while (1):
# skip or not skip
if skip_mode and time % fps != 0:
# grab frame
ret, frame = cap.read()
if ret == False:
break
else:
# read frame
print("progress: " + str(int(time / fps) / 60) + ":" + str(
(time / fps) % 60))
ret, frame = cap.read()
if ret == False:
break
# save frame for final
frame_for_final = np.copy(frame)
if time % fps == 0:
# extract only desk area
frame = frame[desk_area[0][1]:desk_area[1][1],
desk_area[0][0]:desk_area[1][0]]
# get postit
getPostits = post_util.getPostits(frame, outer_size)
# postit_image = getPostits.postit_image
# postit_image_analyzing = getPostits.postit_image_analyzing
postit_points = getPostits.postit_points
#add buffer
for i in range(0, len(postit_points)):
for j in range(0, len(postit_points[i])):
postit_points[i][j] += desk_area[0]
# read postit's id and save
postit_ids = []
bit_array_list = []
#print len(postit_points)
for i in range(0, len(postit_points)):
# postit_image_analyzing = cv2.imread("./postit_tmp_analyzing/" + str(i) + ".jpg")
postit_image_analyzing = np.load(
"./tmp_datas/postit_tmp_analyzing/" + str(i) + ".npy")
#cv2.imshow("analyzing image", postit_image_analyzing)
bit_array = post_util.readDots(
postit_image_analyzing).bit_array
bit_array_list.append(bit_array)
print bit_array
result_num = -1
try:
result_num = rs.decode(bit_array)[0]
except:
result_num = -1
postit_ids.append(result_num)
print result_num
# save postit image
if result_num != -1:
#rint(postit_points[i])
#postit_center = postit_points[i][0]
#for j in range(0 ,4):
# postit_center += postit_points[i][j]
#postit_center /= 4
postit_center = np.mean(postit_points[i], axis=0)
print postit_center
print sakuhin_id(postit_center)
# already exist
if postit_saved.has_key(result_num):
sid = sakuhin_id(postit_center)
if sid >= 0 and postit_saved[result_num][
"ranged"] == False:
postit_image_for_save = cv2.imread(
"./tmp_datas/postit_tmp/" + str(i) + ".jpg")
c.execute('SELECT * FROM kansou WHERE husen_id=?',
(result_num, ))
rows = c.fetchall()
row_len = 0
tdate = datetime.datetime.now()
tstr = tdate.strftime('%Y_%m_%d_%H_%M_%S_')
cv2.imwrite(
"./datas/kansou_husen/" + str(sid) + "/" +
tstr + str(result_num) + "_" + str(len(rows)) +
".jpg", postit_image_for_save)
c.execute(
"INSERT INTO kansou VALUES (?, ?, ?)",
(result_num, sid, datetime.datetime.now()))
print "dbinsert"
postit_saved[result_num]["ranged"] = True
# judge move and rotate
# calc dist
dist = np.linalg.norm(
np.mean(postit_saved[result_num]["points_saved"],
axis=0) -
np.mean(postit_points[i], axis=0))
# calc angle(degree)
angle_vec_before = np.array(
postit_saved[result_num]["points_saved"]
[0]) - np.array(
postit_saved[result_num]["points_saved"][1])
angle_vec_after = np.array(
postit_points[i][0]) - np.array(
postit_points[i][1])
vec_cos = np.dot(angle_vec_before, angle_vec_after) / (
np.linalg.norm(angle_vec_before) *
np.linalg.norm(angle_vec_after))
angle = np.arccos(vec_cos) * 180 / np.pi
# add information
if dist > dist_thre:
if len(postit_saved[result_num]["move"]) == 0:
postit_saved[result_num]["move"].append(time /
fps)
elif (time / fps -
postit_saved[result_num]["move"][-1]) > 5:
postit_saved[result_num]["move"].append(time /
fps)
elif angle > angle_thre:
if len(postit_saved[result_num]["rotate"]) == 0:
postit_saved[result_num]["rotate"].append(
time / fps)
elif (time / fps -
postit_saved[result_num]["rotate"][-1]) > 5:
postit_saved[result_num]["rotate"].append(
time / fps)
if time / fps - postit_saved[result_num][
"last_time"] > 600:
#if False:
print "mouikkai appear"
postit_saved[result_num] = {
"points": postit_points[i],
"points_saved": postit_points[i],
"first_time": time / fps,
"last_time": time / fps,
"move": [],
"rotate": [],
"ranged": False
}
# Insert a row of data
sid = sakuhin_id(postit_center)
if (sid >= 0 and postit_saved[result_num]["ranged"]
== False):
postit_image_for_save = cv2.imread(
"./tmp_datas/postit_tmp/" + str(i) +
".jpg")
c.execute(
'SELECT * FROM kansou WHERE husen_id=?',
(result_num, ))
rows = c.fetchall()
row_len = 0
tdate = datetime.datetime.now()
tstr = tdate.strftime('%Y_%m_%d_%H_%M_%S_')
cv2.imwrite(
"./datas/kansou_husen/" + str(sid) + "/" +
tstr + str(result_num) + "_" +
str(len(rows)) + ".jpg",
postit_image_for_save)
c.execute(
"INSERT INTO kansou VALUES (?, ?, ?)",
(result_num, sid, datetime.datetime.now()))
print "dbinsert"
postit_saved[result_num]["ranged"] = True
# Save (commit) the changes
conn.commit()
# renew
postit_saved[result_num]["points"] = postit_points[i]
postit_saved[result_num][
"points_saved"] = postit_points[i]
postit_saved[result_num]["last_time"] = time / fps
# first appear
else:
print "first appear"
postit_saved[result_num] = {
"points": postit_points[i],
"points_saved": postit_points[i],
"first_time": time / fps,
"last_time": time / fps,
"move": [],
"rotate": [],
"ranged": False
}
postit_image_for_save = cv2.imread(
"./tmp_datas/postit_tmp/" + str(i) + ".jpg")
#for row in c.execute('SELECT * FROM kansou WHERE husen_id=?',(result_num)):
# print(row)
# row_len + 1
sid = sakuhin_id(postit_center)
if sid >= 0 and postit_saved[result_num][
"ranged"] == False:
c.execute('SELECT * FROM kansou WHERE husen_id=?',
(result_num, ))
rows = c.fetchall()
row_len = 0
tdate = datetime.datetime.now()
tstr = tdate.strftime('%Y_%m_%d_%H_%M_%S_')
cv2.imwrite(
"./datas/kansou_husen/" + str(sid) + "/" +
tstr + str(result_num) + "_" + str(len(rows)) +
".jpg", postit_image_for_save)
print("./datas/kansou_husen/" + str(sid) + "/" +
tstr + str(result_num) + "_" +
str(len(rows)) + ".jpg")
# Insert a row of data
c.execute(
"INSERT INTO kansou VALUES (?, ?, ?)",
(result_num, sid, datetime.datetime.now()))
print "dbinsert:" + str(sid)
postit_saved[result_num]["ranged"] = True
# Save (commit) the changes
conn.commit()
# delete old postit(long time no see)
for id, val in postit_saved.items():
if (time / fps - val["last_time"]) > time_thre:
postit_saved[id]["points"] = [[-5, 0], [0, 0], [0, -5],
[-5, -5]]
# write csv
csv_util.write_every_second(postit_saved, csv_every_second)
# memory save
del getPostits
gc.collect()
#renew viewing
for key in postit_saved:
cv2.drawContours(
frame_for_final,
[np.array(postit_saved[key]["points"]).astype(np.int)], 0,
(0, 0, 255), 2)
cv2.putText(frame_for_final,
str(key), (np.mean(postit_saved[key]["points"],
axis=0).astype(np.int)[0] - 40,
np.mean(postit_saved[key]["points"],
axis=0).astype(np.int)[1]),
cv2.FONT_HERSHEY_PLAIN, 5.0, (0, 240, 0), 5)
show_img_final = cv2.resize(frame_for_final,
(int(frame_for_final.shape[1] * 0.3),
int(frame_for_final.shape[0] * 0.3)))
cv2.imshow("show2", show_img_final)
cv2.imwrite("last.jpg", frame_for_final)
#projector draw
cols = 1366
rows = 768
projector_image = np.zeros((rows, cols, 3), np.uint8)
desk_width = desk_area[1][0] - desk_area[0][0]
desk_height = desk_area[1][1] - desk_area[0][1]
postit_relative_points = []
#print postit_saved
iii = 0
for key in postit_saved:
postit_relative_points.append([])
'''
for i in range(0, len(postit_saved[key]["points"])):
postit_relative_points[iii].append(postit_saved[key]["points"] - desk_area[0])
postit_relative_points[iii][i][0] = int(cols * 1.0 / desk_width * postit_relative_points[i][j][0])
postit_relative_points[iii][i][1] = int(rows * 1.0 / desk_height * postit_relative_points[i][j][1])
#cv2.drawContours(projector_image,(postit_relative_points[i]),0,(0,0,255),2)
cv2.drawContours(projector_image, [np.array(postit_saved[key]["points"]).astype(np.int)], 0,
(0, 0, 255), 2)
cv2.putText(projector_image, str(key), (
np.mean(postit_saved[key]["points"], axis=0).astype(np.int)[0] - 40,
np.mean(postit_saved[key]["points"], axis=0).astype(np.int)[1]), cv2.FONT_HERSHEY_PLAIN, 5.0,
(0, 240, 0), 5)
#pts = np.array(postit_relative_points[i], np.int32)
#cv2.polylines(projector_image,[pts],True,(0,255,255))
#cv2.putText(projector_image,str(postit_ids[i]),(int(postit_relative_points[i][0][0]),int(postit_relative_points[i][0][1])), cv2.FONT_HERSHEY_SIMPLEX , 1,(255,255,255),2)
iii+=1
'''
cv2.imshow("projector", projector_image)
# key waiting
key = cv2.waitKey(1)
if key == 27:
break
# add time
time += 1
# final save
csv_util.write_final(postit_saved, csv_final)