def init_api(app):
"""
Helper for initialization of api.
:param app: Flask
:return: None
"""
locations.create(app)
records.create(app)
users.create(app)
image.create(app)
ratings.create(app)
def mult(img, s):
w, h = len(img[0]), len(img)
imgRed = red(img,s)
wr, hr = len(imgRed[0]), len(imgRed)
im.create(w, h,(0,0,0))
x, y = 0, 0
for xx in range(s):
y = 0
for yy in range(s):
img = rectCopy(img, imgRed, x, y)
y = y + hr
x = x + wr
return img
def red(img, s):
w, h = len(img[0]), len(img)
wr = round(w/s)
hr = round(h/s)
imgR = im.create(wr, hr, (0,0,0))
newColor = [0, 0, 0]
# PER OGNI PIXEL RIDOTTO DELLA RIGA
for x in range(wr):
#PER OGNI PIXEL RIDOTTO DELLA COLONNA
for y in range(hr):
#CREO UN COLORE CHE E' IL COLORE MEDIO DI S^2 PIXELS
newColor = [0, 0, 0]
#PER LE RIGHE DI X DELL'IMMAGINE ORIGINALE
for px in range(s):
#PER LE RIGHE DI Y DELL'IMMAGINE ORIGINALE
for py in range(s):
#SOMMO TUTTI I VALORI DEI TRE CANALI RGB
for k in range(3):
newColor[k] += img[y*s+py][x*s+px][k]
#CALCOLO IL VALORE MEDIO
for k in range(3):
newColor[k] = min(max(round(newColor[k]/(s*s)), 0), 255)
#print(newColor)
#COMPRIMO S^2 PIXELS IN UN SOLO PIXEL!
imgR[y][x] = tuple(newColor)
return imgR
def edge(img, t, bg):
w, h = len(img[0]), len(img)
imgR = im.create(w, h, (0,0,0))
def isContour(x, y):
#GET THE PIXEL SUM OF COLORS
pixelVal = (sum(img[y][x]) / 765)
for dy in range(-1, 2):
for dx in range(-1, 2):
xx = x + dx
yy = y + dy
#same pixel check skip, could also be left
if xx == x and yy == y:
continue
#if index is negative skip this instruction
if not (0 <= xx < w) or not (0 <= yy < h):
continue
pixelVal2 = (sum(img[yy][xx]) / 765)
if pixelVal2 < pixelVal:
if abs(pixelVal - pixelVal2) >= t:
return True
return False
for x in range(0, w, 1):
for y in range(0, h, 1):
if isContour(x, y):
imgR[y][x] = (0,0,0)
elif bg is not None:
imgR[y][x] = bg
else:
imgR[y][x] = img[y][x]
return imgR
def rot90(img):
w, h = len(img[0]), len(img)
imgR = im.create(h, w, (0,0,0))
for y in range(h):
for x in range(w):
imgR[-1*x][y] = img[y][x]
return imgR
def copyPiece(img, s, dx, dy):
w, h = len(img[0]), len(img)
wr = round(w/s)
hr = round(h/s)
imgR = im.create(wr, hr, (0,0,0))
# PER OGNI PIXEL RIDOTTO DELLA RIGA
for x in range(wr):
#PER OGNI PIXEL RIDOTTO DELLA COLONNA
for y in range(hr):
imgR[y][x] = img[dy+y][dx+x]
return imgR
def contrast(img, c):
'''Ritorna una nuova immagine che e' l'immagine img con contrasto modificato in base al parametro c:
se c = 1.0, non c'e' modifica, se c < 1.0 il contrasto e' diminuito tanto piu' c e' vicino a 0,
se c > 1.0 il contrasto e' aumentato tanto piu' c e' grande'''
w, h = len(img[0]), len(img)
ret = im.create(w, h, (0,0,0))
for y in range(h):
for x in range(w):
r, g, b = img[y][x]
ret[y][x] = (_c(r,c), _c(g,c), _c(b,c))
return ret
def patchwork(img, s):
w, h = len(img[0]), len(img)
random.seed(0)
wr, hr = round(w/s), round(h/s)
im.create(w, h,(0,0,0))
x, y = 0, 0
for xx in range(s):
y = 0
for yy in range(s):
imgRed = copyPiece(img, s, x, y)
sample = random.sample([1,2,3,4,5], 1)
if sample[0] == 2:
imgRed = contrast(imgRed, 0.5)
if sample[0] == 3:
imgRed = contrast(imgRed, 2.0)
if sample[0] == 4:
imgRed = invert(imgRed)
if sample[0] == 5:
imgRed = gray(imgRed)
img = rectCopy(img, imgRed, x, y)
y = y + hr
x = x + wr
return img
def mosaic_average(fname_in, fname_out, s):
'''Ritorna una nuova immagine ottenuta dividendo
l'immagine img in quadrati di lato s e riempendo
ogni quadratino con la media dei suoi colori.'''
img = image.load(fname_in)
w, h = len(img[0]), len(img)
ret = image.create(w, h, (0,0,0))
# itera sui possibili quadrati
for jj in range(h//s):
for ii in range(w//s):
# colore medio dell'immagine
c = average(img,ii*s,jj*s,s,s)
draw_quad(ret, ii*s, jj*s, s, s, c)
image.save(fname_out, ret)
def mosaic_nearest(fname_in, fname_out, s):
"""Ritorna una nuova immagine ottenuta dividendo
l'immagine img in quadrati di lato s e riempendo
ogni quadrato con il colore del suo angolo in
alto a sinistra"""
img = image.load(fname_in)
w, h = len(img[0]), len(img)
ret = image.create(w, h, (0,0,0))
# itera sui possibili quadrati
for jj in range(h//s):
for ii in range(w//s):
# colore dell'angolo in alto-sinistra
c = img[jj*s][ii*s]
draw_quad(ret, ii*s, jj*s, s, s, c)
image.save(fname_out, ret)
def upload():
galleries = sqlite_interface.getCurrentGalleries()
if request.method == "GET":
return render_template("upload.html",
galleryNames = galleries,
galleries = galleries)
else:
print "PROCESSING IMAGE"
processedProperly = image.create(request.form,
galleries,
request.files['file'],
date.today().year)
print "FINISHED PROCESSING"
if processedProperly != True:
render_template("error.html",
error = processedProperly,
galleryNames = galleries)
return redirect(url_for("currentGallery", galleryName = request.form['Gallery']))
def backfill(year):
galleries = sqlite_interface.getVisibleByYear(year)
print "YEAR: " + year
if request.method == "GET":
return render_template("upload.html",
gallerynames = galleries,
galleries = galleries,
yr = year)
else:
processedProperly = image.create(request.form,
galleries,
request.files['file'],
year)
if processedProperly != True:
render_template("error.html",
error = processedProperly,
galleryNames = galleries)
return redirect(url_for("previousGallery",
galleryName = request.form['Gallery'],
year = year))
def mosaic_size(fname_in, fname_out, s):
'''Ritorna una nuova immagine ottenuta dividendo
l'immagine img in quadratini di lato s e
disegnando all'interno di ognuno di essi,
su sfondo nero, un quadratino centrale bianco di
lato proporzionale alla luminosità media del
corrispondente quadratino'''
img = image.load(fname_in)
w, h = len(img[0]), len(img)
ret = image.create(w, h, (0,0,0))
# itera sui possibili quadrati
for jj in range(h//s):
for ii in range(w//s):
# colore medio dell'immagine
c = average(img,ii*s,jj*s,s,s)
# lato del quadratino bianco
r = round(s*(c[0]+c[1]+c[2])/(3*255))
draw_quad(ret, ii*s+(s-r)//2,
jj*s+(s-r)//2, r, r, (255,255,255))
image.save(fname_out, ret)
def edging(fname_in, fname_out, n_iter):
img = image.load(fname_in)
width, height = len(img[0]), len(img)
img_out = image.create(width, height, (0, 0, 0))
pix_hood = ((-1, -1), (0, -1), (1, -1), (1, 0), (1, 1), (0, 1), (-1, 1), (-1, 0))
for _ in range(n_iter):
for row in range(height):
for col in range(width):
color = img[row][col]
max_distance = 0
max_color = color
for x, y in pix_hood:
row_hood = y + row
col_hood = x + col
if not (0 <= col_hood < width and 0 <= row_hood < height):
continue
color_hood = img[row_hood][col_hood]
distance = calc_dist(color, color_hood)
if distance > max_distance:
max_distance = distance
max_color = color_hood
img_out[row][col] = max_color
img, img_out = img_out, img
image.save(fname_out, img)
import cv2
import image
import paint
import seismic
from data import *
if __name__ == '__main__':
_seismic = image.load("SeismicScaled.jpg")
_transformation = seismic.parse_transformation(_seismic, 15 * 1000, int(2.5 * 1000), 10)
_geo = Geo(Well.generate_left(), Well.generate_right(), _transformation.width, _transformation.left_well_intent, _transformation.right_well_intent)
_match = Match.generate()
_image = image.create(_geo.height, _geo.width)
# image.show(_seismic)
paint.wells(_image, _geo)
paint.lines(_image, _geo, _match)
paint.fill(_image, _geo, _match)
# _image1 = _image.copy()
# paint.fill(_image1, _geo, _match)
# paint.wells(_image1, _geo)
#
# _, _image2 = cv2.threshold(_image1, 127, 255, cv2.THRESH_BINARY)
#
# print("Showing ...")
# image.show(_image, _image1, _image2)
_result = image.create(_transformation.height, _transformation.width)
