def bgExtract(path):
img = cv2.imread(path, 1)
colors, pixel_count = extcolors.extract(path)
# print(path)
normPercentages = []
pieColor = []
colorLabels = []
for i in range(len(colors)):
# keep here the rgb code of the color
colorRGB = str(colors[i][0])
colorLabels.append(colorRGB)
# calculate the percentage of the rgb color in the picture
percentage = colors[i][1]/pixel_count * 100
# format the percentage and put it in the matrix of the original percentages
colorPercentage = '{0:.3f}'.format(percentage)
normPercentages.append(colorPercentage)
# We normalize some small values so that they appear correclty in the pie chart
if percentage <= 0.01:
percentage = percentage*300
# print("IN IF ---- with % ",percentage)
elif percentage <= 0.1:
percentage = percentage*30
# print("elif ---- with % ",percentage)
elif percentage <= 0.5:
percentage = percentage *10
# We enter those corrected percentages in the list of the pie chart sizes(how big is each chunk)
pieColor.append(percentage)
colorPercentage = '{0:.3f}'.format(percentage)
# print(colorRGB + ": " + colorPercentage)
# create the pie chart
plt.figure()
patches,texts = plt.pie(pieColor,labels=normPercentages)
plt.legend(patches, colorLabels, loc="best")
cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
canny = cv2.Canny(img, 100, 200)
cv2.imshow(path, canny)
contours,hierarchy = cv2.findContours(canny,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
if len(contours) > 0:
cnt = contours[0]
area = cv2.contourArea(cnt)
print(area)
print("\n")
return area
def click_image(self):
now = datetime.now()
dt_string = now.strftime("%d%m%Y%H%M%S")
camera = cv2.VideoCapture(0)
return_value, image = camera.read()
cv2.imwrite('./Images/opencv' + dt_string + '.png', image)
cv2.imwrite('./Images/default.jpg', image)
del camera
img = cv2.imread('./Images/opencv' + dt_string + '.png')
img = cv2.resize(img, (0, 0), fx=0.25, fy=0.25)
cv2.imwrite('./Images/opencv' + dt_string + '.png', img)
colors, pixel_count = extcolors.extract('./Images/opencv' + dt_string +
'.png')
return np.array([[(colors[0])[0][0]], [(colors[0])[0][1]],
[(colors[0])[0][2]]])
def canculateColor(self, img_url):
image_name = md5(img_url.lower().encode('utf-8')).hexdigest()
ext = img_url.split(".")
if ext:
ext = ext[-1]
else:
ext = 'jpg'
img_filepath = image_name + "." + ext
logging.info("Downloading image...")
with open(img_filepath, 'wb') as f:
f.write(requests.get(img_url).content)
logging.info("Analyse image color...")
import extcolors
if os.path.isfile(img_filepath):
colors, pixel_count = extcolors.extract(img_filepath)
os.remove(img_filepath)
return ','.join(str(color) for color in colors[:5])
return ''
def ext_colors(img_path):
# Renkler ve piksel sayılarının ataması
colors, pixel_count = extcolors.extract(img_path)
# Renklerin RGB değerleri ve piksel sayıları
for i in range(len(colors)):
print(colors[i])
print('pixel_count:', pixel_count)
print()
print('-----returns as RGB-----')
# Renklerin metin çıktısı
i = 0
while i < len(colors):
print(
str(colors[i][0]) + ': %' +
str(round(((colors[i][1]) / pixel_count) * 100.0, 2)))
i += 1
# Renklerin görsel çıktısı
extcolors.image_result(colors, 100, img_path)
return colors
def coloriser(filename):
colors, pixel_count = extcolors.extract(filename)
rgb=[]
for item in colors:
rgb.append(item[0])
color1 = []
for item in rgb:
color1.append(colorpredictor.get_colour_name(item))
filtered_colors = set(color1)
colors_sorted=filtercolors(list(filtered_colors))
final = set(colors_sorted)
final = list(final)
row = []
filename_sorted = str(filename).split('/')
row.append(filename_sorted[-1])
for i in range(8):
row.append("0")
for items in final:
if items == "red":
row[1] = "1"
if items == "blue":
row[2] = "1"
if items == "green":
row[3] = "1"
if items == "white":
row[4] = "1"
if items == "pink":
row[5] = "1"
if items == "orange":
row[6] = "1"
if items == "yellow":
row[7] = "1"
if items == "purple":
row[8] = "1"
with open('onehotencoded.csv', 'a') as csvFile:
writer = csv.writer(csvFile)
writer.writerow(row)
csvFile.close()
import os
import cv2
from PIL import Image
import extcolors as ji
"""Save a screenshot from spotify.com in current directory"""
chromedriver = "C:/Users/Siddh/Downloads/chromedriver"
os.environ["webdriver.chrome.driver"] = chromedriver
driver = webdriver.Chrome(chromedriver)
driver.get(
'https://www.incois.gov.in/portal/osf/osfCoastal.jsp?region=coastal&area=westbengal¶m=swh&ln=en'
)
screenshot = driver.save_screenshot("my_screenshot1.png")
driver.quit()
im = Image.open('my_screenshot1.png')
outfile = "result.jpg"
region = im.crop((500, 350, 900, 900))
region.save(outfile, "png")
colors, pixel_count = ji.extract(outfile)
for x in colors:
if ((x[0][0] == 246 and x[0][1] == 157 and x[0][2] == 73)
or (x[0][0] == 213 and x[0][1] == 254 and x[0][2] == 160)
or (x[0][0] == 249 and x[0][1] == 219 and x[0][2] == 105)
or (x[0][0] == 247 and x[0][1] == 220 and x[0][2] == 105)):
print('alert there')
else:
print('No Alert!')
break
from functions.EnhanceImage import enhancing
from sklearn.cluster import KMeans
from functions.crop_func import crop
from scipy import ndimage
from functions.func import dist, crop, enhancing, dist1, dist2
import colorgram
################################################
####
name_nat = 'm16.jpg'
name = 'input/'+name_nat
imag = cv2.imread(name)
imag1 = crop(imag)
img = cv2.cvtColor(imag1, cv2.COLOR_BGR2RGB)
plt.imshow(img)
plt.show()
colors, pixel_count = extcolors.extract(name)
color_group, n = np.shape(colors)
color_centre = np.zeros([color_group,3])
for i in range(0,color_group):
color_centre[i,0] = colors[i][0][0]
color_centre[i,1] = colors[i][0][1]
color_centre[i,2] = colors[i][0][2]
######################################
colorsG = colorgram.extract(name, color_group)
color_centre1 = np.zeros([color_group,3])
for i in range(0,color_group):
a = colorsG[i]
color_centre1[i,0] = a.rgb.r
color_centre1[i,1] = a.rgb.g
color_centre1[i,2] = a.rgb.b
img21 = np.zeros([img.shape[0] , img.shape[1]])
def FrameCapture(path):
# Path to video file
vidObj = cv2.VideoCapture(path)
# Used as counter variable
time.sleep(5)
count = 0
# checks whether frames were extracted
success = 1
flg = 0
while success:
# vidObj object calls read
# function extract frames
image: object
success, image = vidObj.read()
# Saves the frames with frame-count
frameoriginal = "originalframe%d.jpg" % count
cv2.imwrite(frameoriginal, image)
# im_resized = cv2.resize(image, (224, 224), interpolation=cv2.INTER_LINEAR)
#cv2.imshow('dst_rt', image)
#image = Image.open("frame%d.jpg" % count, image)
#image.show()
#img = np.zeros((46341, 46341))
#image = cv2.resize(image, (0, 0), fx=1, fy=61, interpolation=cv2.INTER_CUBIC)
"""image_obj = Image.open("image")
cropped_image = image_obj.crop(coords)
cv2.imwrite("frame%d.jpg" % count, image)"""
crop(frameoriginal, (206, 4, 890, 1079),
"frameASHWAMEDHA%d.jpg" % count)
# cropped_image.save(saved_location)
#cropped_image.show()
"""colors, pixel_count = ji.extract("frameASHWAMEDHA%d.jpg" % count)
print(colors)"""
frameoriginalextract = "frameASHWAMEDHA%d.jpg" % count
colors, pixel_count = ji.extract(frameoriginalextract)
# print(colors)
for x in colors:
"""print(x[0][0])
print(x[0][1])
print(x[0][2])"""
if ((x[0][0] == 246 and x[0][1] == 157 and x[0][2] == 73)
or (x[0][0] == 213 and x[0][1] == 254 and x[0][2] == 160)
or (x[0][0] == 249 and x[0][1] == 219 and x[0][2] == 105)
or (x[0][0] == 247 and x[0][1] == 220 and x[0][2] == 105)):
print('alert there')
#sending Email alerts to everyone
content = 'TSUNAMI ALERT!!!!'
mail = smtplib.SMTP('smtp.gmail.com', 587)
mail.ehlo()
mail.starttls()
recipients = [
'*****@*****.**', '*****@*****.**',
'*****@*****.**', '*****@*****.**'
]
mail.login('*****@*****.**', 'sk27$9806')
mail.sendmail('*****@*****.**', recipients, content)
mail.close()
firebase.database().child("RealTime Node").child("Mane").set(
"5")
#Updating the Database
#sending text messages
frameoriginalextract.show()
flg = 1
break
count += 1
if flg == 1:
break
f.close()
print(flist)
print(len(flist))
print(flist1)
# np.save('train_data.npy', training_data)
# return training_data
j = 0
for i in range(0, 90):
d = str(flist[j])
e = "/home/ananth/images/" + d
print(e)
img = cv2.imread(e, 1)
icrop = img[1500:1500 + 500, 2000:2000 + 500]
cv2.imwrite("r.jpg", icrop)
colors, pixel_count = extcolors.extract("r.jpg")
print(colors)
j = j + 1
if colors[2][0] is None:
continue
meanr = (colors[0][0][0] * colors[0][1] + colors[1][0][0] * colors[1][1] +
colors[2][0][0] * colors[2][1]) / (colors[0][1] + colors[1][1] +
colors[2][1])
meang = (colors[0][0][1] * colors[0][1] + colors[1][0][1] * colors[1][1] +
colors[2][0][1] * colors[2][1]) / (colors[0][1] + colors[1][1] +
colors[2][1])
meanb = (colors[0][0][2] * colors[0][1] + colors[1][0][2] * colors[1][1] +
colors[2][0][2] * colors[2][1]) / (colors[0][1] + colors[1][1] +
colors[2][1])
row = [
# # define criteria, number of clusters(K) and apply kmeans()
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
K = 8
ret,label,center=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
# # Now convert back into uint8, and make original image
center = np.uint8(center)
res = center[label.flatten()]
res2 = res.reshape((img.shape))
new_img_dir = join('Kmeans_MOMA',img_dir.split('/')[1])
cv2.imwrite(new_img_dir,res2)
#save the compressed image
colors,pixel_count = extcolors.extract(path = new_img_dir,tolerance=10)
colors = [list(f) for f in colors]
# print(colors)
channel = min(8,len(colors))
dominant_color = colors[:channel]
max_color=colors[0][0]
# print(dominant_color)
sum_pixel = sum([x[1] for x in dominant_color])
for sub in dominant_color:
sub[1] = sub[1]/sum_pixel
item['Domain color'] = dominant_color
item['Max Color']= max_color
newjson.append(item)
count = count+1
if count %100 ==0:
import extcolors as ji
colors, pixel_count = ji.extract("ak.gif")
print(colors)
print(pixel_count)
print((colors[0]))
print((colors[0][0]))
print((colors[0][0][0]))
#5*2 banner
#4*3 standee
#255 124 124 - last Fourth 3.9-4.2
#255 160 69 - last sixth 3.3-3.6
def FrameCapture(path):
# Path to video file
vidObj = cv2.VideoCapture(path)
# Used as counter variable
time.sleep(5)
count = 0
# checks whether frames were extracted
success = 1
flg = 0
while success:
# vidObj object calls read
# function extract frames
image: object
success, image = vidObj.read()
# Saves the frames with frame-count
frameoriginal = "originalframe%d.jpg" % count
cv2.imwrite(frameoriginal, image)
# im_resized = cv2.resize(image, (224, 224), interpolation=cv2.INTER_LINEAR)
#cv2.imshow('dst_rt', image)
#image = Image.open("frame%d.jpg" % count, image)
#image.show()
#img = np.zeros((46341, 46341))
#image = cv2.resize(image, (0, 0), fx=1, fy=61, interpolation=cv2.INTER_CUBIC)
"""image_obj = Image.open("image")
cropped_image = image_obj.crop(coords)
cv2.imwrite("frame%d.jpg" % count, image)"""
crop(frameoriginal, (206, 4, 890, 1079),
"frameASHWAMEDHA%d.jpg" % count)
# cropped_image.save(saved_location)
#cropped_image.show()
"""colors, pixel_count = ji.extract("frameASHWAMEDHA%d.jpg" % count)
print(colors)"""
frameoriginalextract = "frameASHWAMEDHA%d.jpg" % count
colors, pixel_count = ji.extract(frameoriginalextract)
# print(colors)
for x in colors:
"""print(x[0][0])
print(x[0][1])
print(x[0][2])"""
if ((x[0][0] == 246 and x[0][1] == 157 and x[0][2] == 73)
or (x[0][0] == 213 and x[0][1] == 254 and x[0][2] == 160)
or (x[0][0] == 249 and x[0][1] == 219 and x[0][2] == 105)
or (x[0][0] == 247 and x[0][1] == 220 and x[0][2] == 105)):
print('alert there')
#sending Email alerts to everyone
nvar = "1"
firebase.database().child("RealTime Node").child("Mane").set(
nvar) #Updating the Database
dlink = firebase.database().child("Images").child(
nvar).get().val()
#dlink="https://firebasestorage.googleapis.com/v0/b/test-3f09a.appspot.com/o/1.jpg?alt=media&token=9cf380b8-755a-4723-8d41-4cd98c5e8f58"
content = 'TSUNAMI ALERT!!!!' + '\nThe Safe zones are as follows: ' + dlink
mail = smtplib.SMTP('smtp.gmail.com', 587)
mail.ehlo()
mail.starttls()
recipients = [
'*****@*****.**', '*****@*****.**',
'*****@*****.**', '*****@*****.**'
]
mail.login('*****@*****.**', 'sk27$9806')
mail.sendmail('*****@*****.**', recipients, content)
mail.close()
#sending text messages
account_sid = 'ACbb713e5387b4d4d93ecd8c7b80cf6cfb'
auth_token = '72a40562f8b77477f06c52a1f5e19855'
client = Client(account_sid, auth_token)
message = client.messages.create(
body="Tsunami Alert!!" +
'\nFollowing are the safe zones you should head to :' +
dlink,
from_='+19497634689',
to='+919881862818')
print(message.sid)
# Your Account Sid and Auth Token from twilio.com/console
account_sid = 'AC42f06897050e484f5624419e1701f0db'
auth_token = '4e52319b8760b0a2743ecbbfbefccab0'
client = Client(account_sid, auth_token)
message = client.messages.create(
body="Tsunami Alert!!" +
'\nFollowing are the safe zones you should head to :' +
dlink,
from_='+16106869128',
to='+919421986210')
print(message.sid)
account_sid = 'AC0fe52979971e508c946069aaf822499e'
auth_token = '1a5f643116b1d884ab5e0d8fadd80f9b'
client = Client(account_sid, auth_token)
message = client.messages.create(
body="Tsunami Alert!!" +
'\nFollowing are the safe zones you should head to :' +
dlink,
from_='+19284517433',
to='+918007817047')
print(message.sid)
account_sid = 'AC4106d85131931e833b4ae54621c0375b'
auth_token = '89ac03a76fe7e425b2444c3c47333e15'
client = Client(account_sid, auth_token)
message = client.messages.create(
body="Tsunami Alert!!" +
'\nFollowing are the safe zones you should head to :' +
dlink,
from_='+13216168741',
to='+919637336606')
print(message.sid)
account_sid = 'ACbb713e5387b4d4d93ecd8c7b80cf6cfb'
auth_token = '72a40562f8b77477f06c52a1f5e19855'
client = Client(account_sid, auth_token)
message = client.messages.create(
body="Tsunami Alert!!" +
'\nFollowing are the safe zones you should head to :' +
dlink,
from_='+19497634689',
to='+919881862818')
print(message.sid)
# Your Account Sid and Auth Token from twilio.com/console
account_sid = 'AC42f06897050e484f5624419e1701f0db'
auth_token = '4e52319b8760b0a2743ecbbfbefccab0'
client = Client(account_sid, auth_token)
message = client.messages.create(
body="Tsunami Alert!!" +
'\nFollowing are the safe zones you should head to :' +
dlink,
from_='+16106869128',
to='+919421986210')
print(message.sid)
account_sid = 'AC0fe52979971e508c946069aaf822499e'
auth_token = '1a5f643116b1d884ab5e0d8fadd80f9b'
client = Client(account_sid, auth_token)
message = client.messages.create(
body="Tsunami Alert!!" +
'\nFollowing are the safe zones you should head to :' +
dlink,
from_='+19284517433',
to='+918007817047')
print(message.sid)
account_sid = 'AC537a49d120b9fa65e37ccc04b9b37b10'
auth_token = '1706d481924a04924a35eb0b76afb3f8'
client = Client(account_sid, auth_token)
message = client.messages.create(
body="Tsunami Alert!!" +
'\nFollowing are the safe zones you should head to :' +
dlink,
from_='+12015281649',
to='+919518371149')
print(message.sid)
frameoriginalextract.show()
flg = 1
break
count += 1
if flg == 1:
break
import extcolors
#IMAGINI NORMALE
colorsn1, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/n1.jpg")
colorsn2, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/n2.jpg")
colorsn3, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/n3.jpg")
colorsn4, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/n4.jpg")
colorsn5, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/n5.jpg")
colorsn6, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/n6.jpg")
colorsn7, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/n7.jpg")
colorsn8, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/n9.jpg")
colorsn9, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/n10.jpg")
colorss1, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/s1.jpg")
colorss2, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/s2.jpg")
colorss3, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/s3.jpg")
colorss4, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/s4.jpg")
colorss5, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/s5.jpg")
colorss6, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/s6.jpg")
colorss7, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/s7.jpg")
colorss8, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/s8.jpg")
colorss9, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/s9.jpg")
colorss10, pixel_count = extcolors.extract("C:/Users/PycharmProjects/LAB10/Imagini/s10.jpg")
def averageRed(colors): #
x=0
s=0
for i in range(0,min(5,len(colors))):
for j in range(0,3):
s = s + colors[i][0][j]
