Spaces:
Runtime error
Runtime error
File size: 4,366 Bytes
bc69be5 e1a012f bc69be5 ca63cea 1859f7d 48802c9 1d1f5b4 d4d94d1 7eed78f 7f80112 bd234d5 7f80112 8a68876 1859f7d 48802c9 1859f7d bd234d5 1859f7d 27c45bf 2cd4e0f 3f605a0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
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
import h5py
st.set_page_config(
page_title = "Choose the options"
)
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)
st.header("Prediction of Occluded or not Occluded ")
plt. figure(figsize=(10,9))
def occ_predict(img_content):
im = []
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.subheader("Prediction of Blur or NotBlur Image")
images1 = ["blur1.png","blurimg2.png","blurimg3.png","images_11.jpeg"]
with st.sidebar:
st.write("choose an image")
st.image(images)
if f is None:
st.write("Please upload an image file")
else:
image1= Image.open(f)
st.image(image1,use_column_width = True)
predicted_label1,variance_score1 = occ_predict(f)
#st.header(predicted_label)
#st.header(str(round(variance_score,2)))
string1 = "The image is," + str(predicted_label1) + " with the score value of " + str(round(variance_score1,2))
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)) |