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| import streamlit as st | |
| from tensorflow.keras.models import load_model | |
| import numpy as np # linear algebra | |
| import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) | |
| from sklearn.metrics import roc_curve,auc,classification_report,confusion_matrix | |
| import matplotlib.pyplot as plt | |
| import matplotlib.image as mpimg | |
| from PIL import Image | |
| import cv2 | |
| import keras | |
| from keras.utils import np_utils | |
| from tensorflow.keras.models import Sequential | |
| from tensorflow.keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout | |
| from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint,ReduceLROnPlateau | |
| from tensorflow.keras.models import Model | |
| from tensorflow.keras.optimizers import Adam,SGD,RMSprop,Adamax | |
| from tensorflow.keras.models import Model, Sequential | |
| from tensorflow.keras.callbacks import ReduceLROnPlateau | |
| from sklearn.model_selection import StratifiedKFold | |
| from tensorflow.keras.applications import MobileNetV2 | |
| from random import shuffle | |
| from tqdm import tqdm | |
| import scipy | |
| import skimage | |
| from skimage.transform import resize | |
| import random | |
| import os | |
| from io import BytesIO,StringIO | |
| from pathlib import Path | |
| import h5py | |
| page_names = ["Blurred or Not Blurred Prediction","Occluded or Not Occluded Prediction"] | |
| page = st.sidebar.radio('Navigation',page_names) | |
| #st.write("Welcome to the Project") | |
| if page == "Blurred or Not Blurred Prediction": | |
| st.title(""" | |
| Image Blurriness Occluded | |
| """) | |
| st.subheader("Prediction of Blur or NotBlur Image") | |
| images = ["blur1.png","blurimg2.png","blurimg3.png","images_11.jpeg"] | |
| with st.sidebar: | |
| st.write("choose an image") | |
| st.image(images) | |
| model_file_path = "mobile_net_occ.h5" | |
| ##Blurriness Features | |
| plt. figure(figsize=(10,9)) | |
| def variance_of_laplacian(image): | |
| return cv2.Laplacian(image, cv2.CV_64F).var() | |
| def threshold(value, thresh): | |
| if value > thresh: | |
| return "Not Blur" | |
| else: | |
| return "Blur" | |
| def blurr_predict(img_iter): | |
| def make_prediction(img_content): | |
| pil_image = Image.open(img_content) | |
| imgplot = plt.imshow(pil_image) | |
| #st.image(pil_image) | |
| plt.show() | |
| gray_cvimage = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2GRAY) | |
| #print(gray_cvimage) | |
| variance_laplacian = variance_of_laplacian(gray_cvimage) | |
| #print(variance_laplacian) | |
| return variance_laplacian | |
| variance_score = make_prediction(img_iter) | |
| thresh = 2000 | |
| variance_score = variance_score/thresh | |
| predicted_label = threshold(variance_score, 1) | |
| return predicted_label,variance_score | |
| #image_path = "images_11.jpeg" | |
| file = st.file_uploader('Upload an Image',type=(["jpeg","jpg","png"])) | |
| if file is None: | |
| st.write("Please upload an image file") | |
| else: | |
| image= Image.open(file) | |
| st.image(image,use_column_width = True) | |
| predicted_label,variance_score = blurr_predict(file) | |
| #st.header(predicted_label) | |
| #st.header(str(round(variance_score,2))) | |
| string = "The image is," + str(predicted_label) + " with the score value of " + str(round(variance_score,2)) | |
| st.subheader(string) | |
| else: | |
| st.title("Prediction of Occluded or not Occluded ") | |
| #plt. figure(figsize=(10,9)) | |
| def occ_predict(img_content): | |
| im = [] | |
| st.write(img_content) | |
| image=cv2.imread(img_content) | |
| #imgplot = plt.imshow(image) | |
| #plt.show() | |
| img = Image.fromarray(image, 'RGB') | |
| resize_image = img.resize((50, 50)) | |
| im.append(np.array(resize_image)) | |
| fv = np.array(im) | |
| np_array_img = fv.astype('float32') / 255 | |
| model_gcs = h5py.File(model_file_path, 'r') | |
| myModel = load_model(model_gcs) | |
| prediction = myModel.predict(np_array_img) | |
| score = prediction[0][0].item() | |
| thresh = 0.5 | |
| if score > thresh: | |
| return "Not Occluded",score | |
| else: | |
| return "Occluded",score | |
| f = st.file_uploader('Upload an Image',type=(["jpeg","jpg","png"])) | |
| #st.write(f) | |
| #st.subheader("Prediction of Occluded or Not Occluded") | |
| images1 = ["img1.png","img2.png","img3.png","img4.png"] | |
| with st.sidebar: | |
| st.write("choose an image") | |
| st.image(images1) | |
| if f is None: | |
| st.write("Please upload an image file") | |
| else: | |
| st.write(f.name) | |
| image1= Image.open(f) | |
| st.image(image1,use_column_width = True) | |
| image_path = Path(f) | |
| predicted_label,variance_score = occ_predict(image_path) | |
| #st.header(predicted_label) | |
| #st.header(str(round(variance_score,2))) | |
| string1 = "The image is," + predicted_label + " with the score value of " + str(round(variance_score,2)) | |
| st.subheader(string1) | |
| #predicted_label, score = occ_predict("/content/drive/MyDrive/Occulded.jpg") | |
| #print("The image is", '\033[1m' + predicted_label1 + '\033[0m', "with the score value of" ,round(score,2)) |