import numpy as np
import cv2
face_cascade = cv2.CascadeClassifier('E:/DATA
ANALYTICS/IMARTICUS/PGA06/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('E:/DATA ANALYTICS/IMARTICUS/PGA06/haarcascade_eye.xml')
img = cv2.imread("E:\\DATA ANALYTICS\\IMARTIC`US\\PGA06\\images\\chiru(1).jpeg")
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in faces:
img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (ex,ey,ew,eh) in eyes:
cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
getting the following error while running the code
error: OpenCV(4.1.0) C:\projects\opencv-python\opencv\modules\imgproc\src\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'
How i get resolve the issue
Related
i have created a simple chatbot model in python from a video tutorial.
Now i have read that i can use this model in react with tendorflow.js lib but i cant get it to run. I searched around a while but i cant find a real working example.
1.st the code for creating the model (train)
training.py
import random
import json
import numpy as np
import nltk
nltk.download("punkt")
nltk.download("wordnet")
nltk.download('omw-1.4')
from nltk.stem import WordNetLemmatizer
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.optimizers import SGD
import tensorflowjs as tfjs
lemmatizer = WordNetLemmatizer()
intents = json.loads(open("./model/Chatbot/intents.json").read())
words = []
classes = []
documents = []
ignore_letters = ["?", "!", ".", ","]
for intent in intents["intents"]:
for pattern in intent["patterns"]:
word_list = nltk.word_tokenize(pattern)
words.extend(word_list)
documents.append((word_list, intent["tag"]))
if intent["tag"] not in classes:
classes.append(intent["tag"])
words = [lemmatizer.lemmatize(word) for word in words if word not in ignore_letters]
words = sorted(set(words))
classes = sorted(set(classes))
pickle.dump(words, open("./model/Chatbot/words.pkl", "wb"))
pickle.dump(classes, open("./model/Chatbot/classes.pkl", "wb"))
training = []
output_empty = [0] * len(classes)
for document in documents:
bag = []
word_patterns = document[0]
word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns]
for word in words:
bag.append(1) if word in word_patterns else bag.append(0)
output_row = list(output_empty)
output_row[classes.index(document[1])] = 1
training.append([bag, output_row])
random.shuffle(training)
training = np.array(training)
train_x = list(training[:, 0])
train_y = list(training[:, 1])
model = Sequential()
model.add(Dense(128, input_shape=(len(train_x[0]),), activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(64, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(len(train_y[0]), activation="softmax"))
sgd = SGD(learning_rate=0.01, momentum=0.9, nesterov=True)
model.compile(loss="categorical_crossentropy", optimizer=sgd, metrics=["accuracy"])
hist = model.fit(np.array(train_x), np.array(train_y), epochs=1000, batch_size=5, verbose=1)
model.save("./model/Chatbot/chatbotmodel.h5", hist)
tfjs.converters.save_keras_model(model, "./model/Chatbot/")
print("Done")
in the pre-last line the model was exported to model.json and 1 group1-shard1of1.bin file
intents.json (example)
{
"intents": [
{
"tag": "greeting",
"patterns": [
"Hey",
"Hola",
"Hello",
"Hi",
"Ist da jemand?",
"Hallo",
"Guten Tag",
"Hey",
"Moin"
],
"responses": [
"Hallo, schön das du hier bist",
"Schoen dich wiederzusehen",
"Hallo, wie kann ich helfen?"
],
"context_set": "greeting"
}
]
}
in python i can start now chatbot.py which works
import random
import json
import pickle
import numpy as np
import nltk
from nltk.stem import WordNetLemmatizer
from tensorflow import keras
from keras.models import load_model
lemmatizer = WordNetLemmatizer()
intents = json.loads(open("./model/Chatbot/intents.json").read())
words = pickle.load(open("./model/Chatbot/words.pkl", "rb"))
classes = pickle.load(open("./model/Chatbot/classes.pkl", "rb"), fix_imports=True, encoding="ASCII")
model = load_model("./model/Chatbot/chatbotmodel.h5")
context = ""
def clean_up_sentence(sentence):
sentence_words = nltk.word_tokenize(sentence)
sentence_words = [lemmatizer.lemmatize(word) for word in sentence_words]
return sentence_words
def bag_of_words(sentence):
sentence_words = clean_up_sentence(sentence)
bag = [0] * len(words)
for w in sentence_words:
for i, word in enumerate(words):
if word == w:
bag[i] = 1
return np.array(bag)
def predict_class(sentence):
bow = bag_of_words(sentence) # [0 0 0 0 0 0 0 0 0]?
print(np.array([bow]))
res = model.predict(np.array([bow]))[0] # [8.58373418e-02 3.18233818e-02 9.12701711e-02 3.93254980e-02...
print(res)
ERROR_TRESHOLD = 0.25
results = [[i, r] for i, r in enumerate(res) if r > ERROR_TRESHOLD] # Hallo => [[21, 0.35744026]]
results.sort(key=lambda x: x[1], reverse=True) # moin => [[21, 0.35744026]]
return_list = []
for r in results:
return_list.append({"intent": classes[r[0]], "probability": str(r[1])})
return return_list # hallo [{'intent': 'greeting', 'probability': '0.35744026'}]
def get_response(intents_list, intents_json):
tag = intents_list[0]["intent"] # hallo [{'intent': 'greeting', 'probability': '0.35744026'}] ===> 'greeting'
list_of_intents = intents_json["intents"] # ==> alle intents aus datei
print(intents_list)
for i in list_of_intents:
if "context_set" in i:
context = i["context_set"]
print(context)
if i["tag"] == tag:
result = random.choice(i["responses"])
break
return result
print("Go! Bot is running")
while True:
message = input("")
ints = predict_class(message) # # hallo [{'intent': 'greeting', 'probability': '0.35744026'}]
res = get_response(ints, intents)
print(res)
2. Try to get it run in react.
import { useEffect, useState } from 'react';
import * as tf from '#tensorflow/tfjs';
const url = {
model: 'https://example.com/model.json',
};
function App() {
async function loadModel(url) {
try {
let message = "Hallo";
//const inputTensor = tf.tensor([parseInt(message)]);
const model = await tf.loadLayersModel(url.model);
setModel(model);
let result = model.predict(message); // make prediction like in Python
//let bow = bag_of_words(message) // [0 0 0 0 0 0 0 0 0]?
}
catch (err) {
console.log(err);
}
}
useEffect(() => {
tf.ready().then(() => {
loadModel(url)
});
}, [])
}
At this point, the model.json and group1-shard1of1.bin are both imported correct, but when i try to model.predict('hallo') i get the following error:
Error when checking model : the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see 1 Tensor(s), but instead got 5 Tensors(s).
Maybe u have an idea to solve it? Thanks.
I'm implementing 3D demo application using Babylonjs library for 3D Demo.I'm importing 3D model from S3 and adding texture image on top of material in Reactjs.
But when i add texture image on top of material, rest of area on 3D model gets black color and i want get rid of it. Code works fine in Babylon playground but fails in React app.
Here is the source code
var mat = new BABYLON.CustomMaterial("mat", scene);
mat.diffuseTexture = new BABYLON.Texture(textureImage, scene, false, false);
materialedMeshes.forEach(mesh => mesh.material = mat);
mat.emissiveColor = new BABYLON.Color3(1, 1, 1);
// mat.diffuseColor = new BABYLON.Color3(1, 0, 1);
// mat.specularColor = new BABYLON.Color3(0.5, 0.6, 0.87);
// mat.emissiveColor = new BABYLON.Color3(1, 1, 1);
// mat.ambientColor = new BABYLON.Color3(0.23, 0.98, 0.53);
mat.diffuseTexture.uOffset = -0.1000;
mat.diffuseTexture.vOffset = -1.1800;
mat.diffuseTexture.uScale = 1.2200;
mat.diffuseTexture.vScale = 2.2200;
mat.diffuseTexture.uAng = Math.PI;
mat.diffuseTexture.wrapU = BABYLON.Constants.TEXTURE_CLAMP_ADDRESSMODE;
mat.diffuseTexture.wrapV = BABYLON.Constants.TEXTURE_CLAMP_ADDRESSMODE;
mat.Fragment_Custom_Alpha(`
if (baseColor.r == 0. && baseColor.g == 0. && baseColor.b == 0.) {
baseColor.rgb = vec3(0.85, 0.85, 0.85);
}
baseColor.rgb = mix(vec3(0.85, 0.85, 0.85), baseColor.rgb, baseColor.a);
`)
please help me find the error as i didn’t understand for correctly :
from bs4 import BeautifulSoup
import requests
import pandas as pd
url = 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
response = requests.get(url)
with open("imdb_top_250_movies.html", mode='wb') as file:
file.write(response.content)
soup = BeautifulSoup(response.content, 'lxml')
df_list = []
for movie in soup:
title = movie.find('td' , class_="titleColumn").find('a').contents[0]
year = movie.find('td' , class_="titleColumn").find('span').contents[0][1:-1]
user_rating = movie.find('td' , class_="ratingColumn imdbRating").find('strong').contents[0]
df_list.append({'title': title,
'year': int(year),
'user_ratings': float(user_rating)})
df = pd.DataFrame(df_list, columns = ['title', 'year', 'user_ratings'])
df
This is the error I got
TypeError Traceback (most recent call
last) Input In [125], in <cell line: 8>()
9 soup = BeautifulSoup(response.content, 'lxml')
10 df_list = []
---> 11 title = movie.find('td' , class_="titleColumn").find('a').contents[0]
12 year = soup.find('td' , class_="titleColumn").find('span').contents[0][1:-1]
13 user_rating = soup.find('td' , class_="ratingColumn imdbRating").find('strong').contents[0]
TypeError: find() takes no keyword arguments
Someone helped me with this answer as I wrote For incorrectly :
from bs4 import BeautifulSoup
import requests
import pandas as pd
url = 'https://www.imdb.com/chart/top'
response = requests.get(url)
with open("imdb_top_250_movies.html", mode='wb') as file:
file.write(response.content)
soup = BeautifulSoup(response.content, 'lxml')
df_list = []
for movie in soup.find('tbody' , class_="lister-list").find_all('tr'):
Place = movie.find('td' , class_="titleColumn").contents[0][1:-len('.\n ')]
title = movie.find('td' , class_="titleColumn").find('a').contents[0]
year = movie.find('td' , class_="titleColumn").find('span').contents[0][1:-1]
user_rating = movie.find('td' , class_="ratingColumn imdbRating").find('strong').contents[0]
df_list.append({'place': Place,
'title': title,
'year': int(year),
'user_ratings': float(user_rating)})
df = pd.DataFrame(df_list, columns = ['place','title', 'year', 'user_ratings'])
df.style.hide(axis='index')
This is my code.
import cv2
face_cascade = cv2.CascadeClassifier("Resources/haarcascade_frontalface_default.xml")
img = cv2.imread("Resources/skirt.PNG")
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray_img,scaleFactor = 1.1,minNeighbors=4)
for (x,y,w,h) in faces:
img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
cv2.imshow("Result",img)
cv2.waitKey(0)
Getting this error.
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.error: OpenCV(4.5.1) C:\Users\appveyor\AppData\Local\Temp\1\pip-req-build-oduouqig\opencv\modules\imgproc\src\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'
I get a syntax error in the following command of LogisticRegression. Can someone please advise where I might be going wrong here?
from sklearn import linear_model
lm = linear_model.LogisticRegression (C = 1e3)
lm.fit (m[:lags].T, np.sign(m[lags])
LogisticRegression (penalty ='l2', tol = .0001, C = 1000000.0, class_weight = None, dual = False, fit_intercept = True,
intercept_scaling = 1, random_state = None, solver ='liblinear', max_iter = 100, multi_class ='ovr',verbose = 0,
warm_start = False, n_jobs = 1)
Error:
File "<ipython-input-33-529ff9190586>", line 4
LogisticRegression (penalty ='l2', tol = .0001, C = 1000000.0, class_weight = None, dual = False, fit_intercept = True,
^
SyntaxError: invalid syntax
you have missing closing parenthesis here:
lm.fit (m[:lags].T, np.sign(m[lags]) <--
change to
lm.fit (m[:lags].T, np.sign(m[lags]))