Hi there. i am making a streamlit app in which user will upload an image and yolo object detection algorithm will detect objects in that uploaded image but i am unable to do it.
Can anyone tell me how to do this ?
The code is given below.
import streamlit as st
import cv2
from PIL import Image,ImageEnhance
import numpy as np
import pandas as pd
import glob
import random
import os
@st.cache(persist=True)
def load_image(img):
im = Image.open(img)
return im
@st.cache(persist=True)
def yolo_objectdetection():
# Load Yolo
net = cv2.dnn.readNet("yolov3_training_last.weights", "yolov3_testing.cfg")
# Name custom object
classes = ["Land"]
# Images path
global images_path
images_path = glob.glob(r"...\uploads")
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))
# Insert here the path of your images
random.shuffle(images_path)
# loop through all the images
for img_path in images_path:
# Loading image
img = cv2.imread(img_path)
img = cv2.resize(img, None, fx=3.5, fy=3.5)
height, width, channels = img.shape
# Detecting objects
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
# Showing informations on the screen
class_ids = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.3:
# Object detected
print(class_id)
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
# Rectangle coordinates
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
print(indexes)
font = cv2.FONT_HERSHEY_PLAIN
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
color = colors[class_ids[i]]
cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
cv2.putText(img, label, (x, y + 30), font, 3, color, 2)
cv2.imshow('Image',img)
key = cv2.waitKey(0)
cv2.destroyAllWindows()
def main():
"""
Land Detection App
"""
st.title('Land Detection App')
st.text('Build with Streamlit,Yolov3 and OpenCV')
menu = ['Detection','About']
choice = st.sidebar.selectbox('Menu',menu)
if choice == 'Detection':
st.subheader(' Land Detection')
image_file = st.file_uploader("Upload Image",type=['jpg', 'png', 'jpeg'])
if image_file is not None:
our_image = Image.open(image_file)
st.text('Original Image')
# st.write(type(our_image))
st.image(our_image)
enhance_type = st.sidebar.radio('Enhance Type', ['Original', 'Gray-Scale', 'Contrast', 'Brightness', 'Blurring'])
if enhance_type == 'Gray-Scale':
new_img = np.array(our_image.convert('RGB'))
img = cv2.cvtColor(new_img, 1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# st.write(new_img)
st.image(gray)
if enhance_type == 'Contrast':
c_rate = st.sidebar.slider('Contrast', 0.5, 3.5)
enhancer = ImageEnhance.Contrast(our_image)
img_output = enhancer.enhance(c_rate)
st.image(img_output)
if enhance_type == 'Brightness':
c_rate = st.sidebar.slider('Brightness', 0.5, 3.5)
enhancer = ImageEnhance.Brightness(our_image)
img_output = enhancer.enhance(c_rate)
st.image(img_output)
if enhance_type == 'Blurring':
new_img = np.array(our_image.convert('RGB'))
blur_rate = st.sidebar.slider('Blurring', 0.5, 3.5)
img = cv2.cvtColor(new_img, 1)
blur_img = cv2.GaussianBlur(img, (11, 11), blur_rate)
st.image(blur_img)
else:
pass
# Land Detection
task = ['Land']
feature_choice = st.sidebar.selectbox("Find Features", task)
if st.button("Process"):
if feature_choice == 'Land':
result_img = yolo_objectdetection()
result_img = []
st.image(result_img)
st.success("Found {} Land".format(len(result_img)))
else:
st.markdown('## Land not Detected')
st.markdown('## Kindly upload correct image ')
elif choice == 'About':
st.subheader('About')
st.info('We are an AI company which provides great blueprints and provide value to our users.')
if __name__ == '__main__':
main()
