3 posts tagged with "streamlit"

Object detection with YOLO3

October 1, 2021 · 2 min read

Principal Developer

Live app

This app can detect COCO 80-classes using three different models - Caffe MobileNet SSD, Yolo3-tiny, and Yolo3. It can also detect faces using two different models - SSD Res10 and OpenCV face detector. Yolo3-tiny can also detect fires.

/img/content-blog-raw-blog-object-detection-with-yolo3-untitled.png

/img/content-blog-raw-blog-object-detection-with-yolo3-untitled-1.png

Code

import streamlit as st
import cv2
from PIL import Image
import numpy as np
import os

from tempfile import NamedTemporaryFile
from tensorflow.keras.preprocessing.image import img_to_array, load_img

temp_file = NamedTemporaryFile(delete=False)

DEFAULT_CONFIDENCE_THRESHOLD = 0.5
DEMO_IMAGE = "test_images/demo.jpg"
MODEL = "model/MobileNetSSD_deploy.caffemodel"
PROTOTXT = "model/MobileNetSSD_deploy.prototxt.txt"

CLASSES = [
    "background",
    "aeroplane",
    "bicycle",
    "bird",
    "boat",
    "bottle",
    "bus",
    "car",
    "cat",
    "chair",
    "cow",
    "diningtable",
    "dog",
    "horse",
    "motorbike",
    "person",
    "pottedplant",
    "sheep",
    "sofa",
    "train",
    "tvmonitor",
]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

@st.cache
def process_image(image):
    blob = cv2.dnn.blobFromImage(
        cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5
    )
    net = cv2.dnn.readNetFromCaffe(PROTOTXT, MODEL)
    net.setInput(blob)
    detections = net.forward()
    return detections

@st.cache
def annotate_image(
    image, detections, confidence_threshold=DEFAULT_CONFIDENCE_THRESHOLD
):
    # loop over the detections
    (h, w) = image.shape[:2]
    labels = []
    for i in np.arange(0, detections.shape[2]):
        confidence = detections[0, 0, i, 2]

        if confidence > confidence_threshold:
            # extract the index of the class label from the `detections`,
            # then compute the (x, y)-coordinates of the bounding box for
            # the object
            idx = int(detections[0, 0, i, 1])
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # display the prediction
            label = f"{CLASSES[idx]}: {round(confidence * 100, 2)}%"
            labels.append(label)
            cv2.rectangle(image, (startX, startY), (endX, endY), COLORS[idx], 2)
            y = startY - 15 if startY - 15 > 15 else startY + 15
            cv2.putText(
                image, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2
            )
    return image, labels

def main():
  selected_box = st.sidebar.selectbox(
    'Choose one of the following',
    ('Welcome', 'Object Detection')
    )
    
  if selected_box == 'Welcome':
      welcome()
  if selected_box == 'Object Detection':
      object_detection() 

def welcome():
  st.title('Object Detection using Streamlit')
  st.subheader('A simple app for object detection')
  st.image('test_images/demo.jpg',use_column_width=True)

def object_detection():
  
  st.title("Object detection with MobileNet SSD")

  confidence_threshold = st.sidebar.slider(
    "Confidence threshold", 0.0, 1.0, DEFAULT_CONFIDENCE_THRESHOLD, 0.05)

  st.sidebar.multiselect("Select object classes to include",
  options=CLASSES,
  default=CLASSES
  )

  img_file_buffer = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])

  if img_file_buffer is not None:
      temp_file.write(img_file_buffer.getvalue())
      image = load_img(temp_file.name)
      image = img_to_array(image)
      image = image/255.0

  else:
      demo_image = DEMO_IMAGE
      image = np.array(Image.open(demo_image))

  detections = process_image(image)
  image, labels = annotate_image(image, detections, confidence_threshold)

  st.image(
      image, caption=f"Processed image", use_column_width=True,
  )

  st.write(labels)

main()

You can play with the live app *here. Source code is available here on Github.*

Object detection with YOLO3

January 23, 2021 · 2 min read

Sparsh Agarwal

Principal Developer

Live app

/img/content-blog-raw-blog-object-detection-with-yolo3-untitled.png

/img/content-blog-raw-blog-object-detection-with-yolo3-untitled-1.png

Code

import streamlit as st
import cv2
from PIL import Image
import numpy as np
import os

from tempfile import NamedTemporaryFile
from tensorflow.keras.preprocessing.image import img_to_array, load_img

temp_file = NamedTemporaryFile(delete=False)

DEFAULT_CONFIDENCE_THRESHOLD = 0.5
DEMO_IMAGE = "test_images/demo.jpg"
MODEL = "model/MobileNetSSD_deploy.caffemodel"
PROTOTXT = "model/MobileNetSSD_deploy.prototxt.txt"

CLASSES = [
    "background",
    "aeroplane",
    "bicycle",
    "bird",
    "boat",
    "bottle",
    "bus",
    "car",
    "cat",
    "chair",
    "cow",
    "diningtable",
    "dog",
    "horse",
    "motorbike",
    "person",
    "pottedplant",
    "sheep",
    "sofa",
    "train",
    "tvmonitor",
]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

@st.cache
def process_image(image):
    blob = cv2.dnn.blobFromImage(
        cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5
    )
    net = cv2.dnn.readNetFromCaffe(PROTOTXT, MODEL)
    net.setInput(blob)
    detections = net.forward()
    return detections

@st.cache
def annotate_image(
    image, detections, confidence_threshold=DEFAULT_CONFIDENCE_THRESHOLD
):
    # loop over the detections
    (h, w) = image.shape[:2]
    labels = []
    for i in np.arange(0, detections.shape[2]):
        confidence = detections[0, 0, i, 2]

        if confidence > confidence_threshold:
            # extract the index of the class label from the `detections`,
            # then compute the (x, y)-coordinates of the bounding box for
            # the object
            idx = int(detections[0, 0, i, 1])
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # display the prediction
            label = f"{CLASSES[idx]}: {round(confidence * 100, 2)}%"
            labels.append(label)
            cv2.rectangle(image, (startX, startY), (endX, endY), COLORS[idx], 2)
            y = startY - 15 if startY - 15 > 15 else startY + 15
            cv2.putText(
                image, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2
            )
    return image, labels

def main():
  selected_box = st.sidebar.selectbox(
    'Choose one of the following',
    ('Welcome', 'Object Detection')
    )
    
  if selected_box == 'Welcome':
      welcome()
  if selected_box == 'Object Detection':
      object_detection() 

def welcome():
  st.title('Object Detection using Streamlit')
  st.subheader('A simple app for object detection')
  st.image('test_images/demo.jpg',use_column_width=True)

def object_detection():
  
  st.title("Object detection with MobileNet SSD")

  confidence_threshold = st.sidebar.slider(
    "Confidence threshold", 0.0, 1.0, DEFAULT_CONFIDENCE_THRESHOLD, 0.05)

  st.sidebar.multiselect("Select object classes to include",
  options=CLASSES,
  default=CLASSES
  )

  img_file_buffer = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])

  if img_file_buffer is not None:
      temp_file.write(img_file_buffer.getvalue())
      image = load_img(temp_file.name)
      image = img_to_array(image)
      image = image/255.0

  else:
      demo_image = DEMO_IMAGE
      image = np.array(Image.open(demo_image))

  detections = process_image(image)
  image, labels = annotate_image(image, detections, confidence_threshold)

  st.image(
      image, caption=f"Processed image", use_column_width=True,
  )

  st.write(labels)

main()

You can play with the live app *here. Source code is available here on Github.*

MobileNet SSD Caffe Pre-trained model

January 19, 2020 · One min read

Sparsh Agarwal

Principal Developer

You can play with the live app here. Souce code is available here on Github.

Live app

/img/content-blog-raw-mobilenet-ssd-caffe-pre-trained-model-untitled.png

Code

#------------------------------------------------------#
# Import libraries
#------------------------------------------------------#

import datetime
import urllib
import time
import cv2 as cv
import streamlit as st

from plugins import Motion_Detection
from utils import GUI, AppManager, DataManager

#------------------------------------------------------#
#------------------------------------------------------#

def imageWebApp(guiParam):
    """
    """
    # Load the image according to the selected option
    conf = DataManager(guiParam)
    image = conf.load_image_or_video()
    
    # GUI
    switchProcessing = st.button('* Start Processing *')

    # Apply the selected plugin on the image
    bboxed_frame, output = AppManager(guiParam).process(image, True)

    # Display results
    st.image(bboxed_frame, channels="BGR",  use_column_width=True)

def main():
    """
    """
    # Get the parameter entered by the user from the GUI
    guiParam = GUI().getGuiParameters()

    # Check if the application if it is Empty
    if guiParam['appType'] == 'Image Applications':
        if guiParam["selectedApp"] is not 'Empty':
            imageWebApp(guiParam)

    else:
        raise st.ScriptRunner.StopException

#------------------------------------------------------#
#------------------------------------------------------#

if __name__ == "__main__":
    main()

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