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from requests.models import MissingSchema
import streamlit as st
import cv2
import numpy as np
from PIL import Image, UnidentifiedImageError
import requests
from io import BytesIO
# Create application title and file uploader widget.
st.title("OpenCV Deep Learning based Image Classification")
# @st.cache(allow_output_mutation=True)
@st.cache_resource
def load_model():
"""Loads the DNN model."""
# Read the ImageNet class names.
with open('classification_classes_ILSVRC2012.txt', 'r') as f:
image_net_names = f.read().split('\n')
# Final class names, picking just the first name if multiple in the class.
class_names = [name.split(',')[0] for name in image_net_names]
# Load the neural network model.
model = cv2.dnn.readNet(
model='DenseNet_121.caffemodel',
config='DenseNet_121.prototxt',
framework='Caffe')
return model, class_names
def classify(model, image, class_names):
"""Performs inference and returns class name with highest confidence."""
# Remove alpha channel if found.
if image.shape[2] == 4:
image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
# Create blob from image using values specified by the model:
# https://github.com/shicai/DenseNet-Caffe
blob = cv2.dnn.blobFromImage(
image=image, scalefactor=0.017, size=(224, 224), mean=(104, 117, 123))
# Set the input blob for the neural network and pass through network.
model.setInput(blob)
outputs = model.forward()
final_outputs = outputs[0]
# Make all the outputs 1D.
final_outputs = final_outputs.reshape(1000, 1)
# get the class label
label_id = np.argmax(final_outputs)
# Convert the output scores to softmax probabilities.
probs = np.exp(final_outputs) / np.sum(np.exp(final_outputs))
# Get the final highest probability.
final_prob = np.max(probs) * 100.
# Map the max confidence to the class label names.
out_name = class_names[label_id]
out_text = f"Class: {out_name}, Confidence: {final_prob:.1f}%"
return out_text
def header(text):
st.markdown(
'<p style="background-color:#0066cc;color:#33ff33;font-size:24px;'
f'border-radius:2%;" align="center">{text}</p>',
unsafe_allow_html=True)
net, class_names = load_model()
img_file_buffer = st.file_uploader("Choose a file or Camera", type=['jpg', 'jpeg', 'png'])
st.text('OR')
url = st.text_input('Enter URL')
if img_file_buffer is not None:
# Read the file and convert it to opencv Image.
image = np.array(Image.open(img_file_buffer))
st.image(image)
# Call the classification model to detect faces in the image.
detections = classify(net, image, class_names)
header(detections)
elif url != '':
try:
response = requests.get(url)
image = np.array(Image.open(BytesIO(response.content)))
st.image(image)
# Call the classification model to detect faces in the image.
detections = classify(net, image, class_names)
header(detections)
except MissingSchema as err:
st.header('Invalid URL, Try Again!')
print(err)
except UnidentifiedImageError as err:
st.header('URL has no Image, Try Again!')
print(err)
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