13_02_image_classification_streamlit_app.py

from fileinput import filename

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


import os
from zipfile import ZipFile



#combined the source code from the following links to merge a multi-part zip file
#merge zip files together  url:https://princekfrancis.medium.com/concatenate-large-csv-files-using-python-7e155e70f643
#https://stackoverflow.com/questions/26680579/merging-big-binary-files-using-python-3
#https://stackoverflow.com/questions/6591931/getting-file-size-in-python

#csv_files = ['DenseNet_121.zip.001', 'DenseNet_121.zip.002', 'DenseNet_121.zip.003']
#target_file_name = 'DenseNet_121.zip';
#with open(target_file_name, 'wb') as outfile:
#    for source_file in csv_files[0:]:
#        with open(source_file, "rb") as infile:
#            chunk = os.stat(source_file).st_size
#            outfile.write(infile.read(chunk))
#    outfile.close()

#https://www.geeksforgeeks.org/unzipping-files-in-python/
#with ZipFile( "DenseNet_121.zip", 'r') as z:
#    z.extractall(path=None, members=None, pwd=None)
#z.close()

# Create application title and file uploader widget.
st.title("OpenCV Deep Learning based Image Classification")

#JH
#removed argument that was not recognized by my compiler ... perhaps a different build worked
@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) * 100.0
    # 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)