def getTaskOutputFile(taskId):
task = db.tasks.find_one({"_id": ObjectId(taskId)})
fileobj = fs.get(task["output_file"])
data = wrap_file(request.environ, fileobj, buffer_size=1024 * 255)
response = app.response_class(
data,
mimetype=fileobj.content_type,
direct_passthrough=True,
)
response.content_length = fileobj.length
response.last_modified = fileobj.upload_date
response.set_etag(fileobj.md5)
response.cache_control.max_age = 31536000
response.cache_control.public = True
response.make_conditional(request)
return response
def task2(taskId):
try:
task = db.tasks.find_one_and_update({"_id": taskId},
{"$set": {
"state": "PROCESSING"
}})
inpzip = fs.get(task["input_file"])
with zipfile.ZipFile(inpzip) as zip_ref:
tdir = "/tmp/" + str(taskId) + "/"
zip_ref.extractall(tdir)
os.chdir(tdir)
dirlist = os.listdir()
imlist = []
for i in dirlist:
imlist.append(tdir + i)
img = fun2(imlist)[0][0]
success, encoded_image = cv2.imencode('.png', img)
imgdata = encoded_image.tobytes()
content_type, _ = guess_type("test.png")
fsfileid = fs.put(imgdata, content_type=content_type)
task = db.tasks.find_one_and_update(
{"_id": ObjectId(taskId)}, {"$set": {
"output_file": fsfileid
}})
task = db.tasks.find_one_and_update({"_id": ObjectId(taskId)},
{"$set": {
"state": "COMPLETED"
}})
except Exception:
traceback.print_exc()
task = db.tasks.find_one_and_update({"_id": ObjectId(taskId)},
{"$set": {
"state": "ERROR"
}})
def task3(taskId):
print("POINT 1")
try:
print("POINT 2")
task = db.tasks.find_one_and_update({"_id": taskId},
{"$set": {
"state": "PROCESSING"
}})
print("POINT 3")
inpzip = fs.get(task["input_file"])
print("POINT 4")
img = Merge2.run(
Merge2(taskId, inpzip, task['etc']['alpha'], task['etc']['beta'],
task['etc']['zoom']))
print("POINT 5")
success, encoded_image = cv2.imencode('.png', img)
imgdata = encoded_image.tobytes()
content_type, _ = guess_type("test.png")
fsfileid = fs.put(imgdata, content_type=content_type)
print("POINT 6")
task = db.tasks.find_one_and_update(
{"_id": ObjectId(taskId)}, {"$set": {
"output_file": fsfileid
}})
task = db.tasks.find_one_and_update({"_id": ObjectId(taskId)},
{"$set": {
"state": "COMPLETED"
}})
except Exception:
traceback.print_exc()
task = db.tasks.find_one_and_update({"_id": ObjectId(taskId)},
{"$set": {
"state": "ERROR"
}})
def task6(taskId):
try:
task = db.tasks.find_one_and_update({"_id": ObjectId(taskId)},
{"$set": {
"state": "PROCESSING"
}})
# Reading
img_str = fs.get(task["input_file"]).read()
nparr = np.fromstring(img_str, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
print('slicing colors...')
# convert to hsv
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
row, column, channels = img.shape
new_val = 210
#####################BLUR########################################
beps = 35
hsv = cv2.GaussianBlur(hsv, (beps, beps), 0)
#####################BLUR########################################
print(hsv.shape[0], 'x', hsv.shape[1])
used = []
for i in range(0, hsv.shape[0]):
used.append([])
for j in range(0, hsv.shape[1]):
used[i].append(False)
def bfs(y, x, val, eps):
q = [(y, x)]
while len(q) > 0:
x = q[0][1]
y = q[0][0]
used[y][x] = True
hsv[y][x][0] = val
if x + 1 < hsv.shape[1] and used[y][x + 1] == False and abs(
float(hsv[y][x + 1][0]) - val) <= eps:
q.append((y, x + 1))
used[y][x + 1] = True
if x - 1 >= 0 and used[y][x - 1] == False and abs(
float(hsv[y][x - 1][0]) - val) <= eps:
q.append((y, x - 1))
used[y][x - 1] = True
if y + 1 < hsv.shape[0] and used[y + 1][x] == False and abs(
float(hsv[y + 1][x][0]) - val) <= eps:
q.append((y + 1, x))
used[y + 1][x] = True
if y - 1 >= 0 and used[y - 1][x] == False and abs(
float(hsv[y - 1][x][0]) - val) <= eps:
q.append((y - 1, x))
used[y - 1][x] = True
del q[0]
print('\nworking with hsv...')
for i in range(0, hsv.shape[0]):
for j in range(0, hsv.shape[1]):
for g in range(1, 11):
if i == hsv.shape[0] // 100 * g * 10 and j == 0:
print(g * 10, f'%')
if used[i][j] == False:
bfs(i, j, hsv[i][j][0], 2.25)
mask = cv2.inRange(hsv, (0, 75, 0), (33, 255, 255))
# slice the red
imask = mask > 0
red = np.zeros_like(img, np.uint8)
red[imask] = img[imask]
# slice the yellow
mask = cv2.inRange(hsv, (30.00001, 75, 0), (35, 255, 255))
imask = mask > 0
yel = np.zeros_like(img, np.uint8)
yel[imask] = img[imask]
for i in range(0, red.shape[0]):
for j in range(0, red.shape[1]):
for g in range(1, 11):
if i == hsv.shape[0] // 100 * g * 10 and j == 0:
print(g * 10, f'%')
if (i - 1 >= 0) and (yel[i][j].all() != 0 or red[i][j].all() !=
0) and (yel[i - 1][j].all() == 0
and red[i - 1][j].all() == 0):
img.itemset((i, j, 0), 255)
img.itemset((i, j, 1), 255)
img.itemset((i, j, 2), 255)
if (i + 1 < red.shape[0]) and (yel[i][j].all() != 0
or red[i][j].all() != 0) and (
yel[i - 1][j].all() == 0 and
red[i - 1][j].all() == 0):
img.itemset((i, j, 0), 255)
img.itemset((i, j, 1), 255)
img.itemset((i, j, 2), 255)
if (j - 1 >= 0) and (yel[i][j].all() != 0 or red[i][j].all() !=
0) and (yel[i][j - 1].all() == 0
and red[i][j - 1].all() == 0):
img.itemset((i, j, 0), 255)
img.itemset((i, j, 1), 255)
img.itemset((i, j, 2), 255)
if (j + 1 < red.shape[1]) and (yel[i][j].all() != 0
or red[i][j].all() != 0) and (
yel[i][j + 1].all() == 0 and
red[i][j + 1].all() == 0):
img.itemset((i, j, 0), 255)
img.itemset((i, j, 1), 255)
img.itemset((i, j, 2), 255)
if yel[i][j].all() != 0:
img.itemset((i, j, 1), new_val)
if red[i][j].all() != 0:
img.itemset((i, j, 2), new_val)
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
print("algo ended")
# write output image into fs
success, encoded_image = cv2.imencode('.png', img)
imgdata = encoded_image.tobytes()
content_type, _ = guess_type("test.png")
fsfileid = fs.put(imgdata, content_type=content_type)
# link file id with task document
task = db.tasks.find_one_and_update(
{"_id": ObjectId(taskId)}, {"$set": {
"output_file": fsfileid
}})
task = db.tasks.find_one_and_update({"_id": ObjectId(taskId)},
{"$set": {
"state": "COMPLETED"
}})
except Exception:
traceback.print_exc()
task = db.tasks.find_one_and_update({"_id": ObjectId(taskId)},
{"$set": {
"state": "ERROR"
}})