-
Notifications
You must be signed in to change notification settings - Fork 1
/
app.py
57 lines (43 loc) · 2.39 KB
/
app.py
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
import os
import streamlit as st
import numpy as np
import cv2
from PIL import Image
st.set_page_config(layout="wide", page_title="YOLO Demo", initial_sidebar_state="expanded")
st.sidebar.markdown(
f"This [demo](https://github.com/wnbaum/yolo-demo) uses the YOLO v3 network architecture for object detection. Choose an image, edit some parameters, and watch your model update and detect objects! Images sourced from Google Maps."
)
st.sidebar.markdown(
"Made with 💜 by [Will Baumgartner](https://github.com/wnbaum)"
)
st.title("YOLO Demo")
st.header("This app demonstrates the usage of YOLO for self driving cars and object detection.")
if not os.path.exists("models/yolo_weights.h5"):
with st.spinner("Loading model..."):
os.system("wget --no-check-certificate -O models/yolo_weights.h5 \"https://storage.googleapis.com/inspirit-ai-data-bucket-1/Data/AI%20Scholars/Sessions%206%20-%2010%20(Projects)/Project%20-%20%20Object%20Detection%20(Autonomous%20Vehicles)/yolo.h5\"")
from processing import detect_image
else:
from processing import detect_image
f = st.file_uploader("Upload an Image")
images = { "None":"None", "San Francisco": "images/google1.png", "Paris": "images/google2.png", "London": "images/google3.png", "Tokyo": "images/google4.png" }
image_options = list(images.keys())
image_choice = st.selectbox("or Select an Already Uploaded Image", image_options)
image_location = images[image_choice]
file_bytes = None
if image_choice == "None":
if f is not None:
file_bytes = np.asarray(bytearray(f.read()), dtype=np.uint8)
else:
with open(image_location, 'rb') as image_file:
if image_file is not None:
file_bytes = np.asarray(bytearray(image_file.read()), dtype=np.uint8)
if file_bytes is not None:
st.header("Now edit some parameters to change the object detection algorithm.")
obj_thresh = st.slider("Object Threshold", min_value=0.01, max_value=1.0, value=0.4, step=0.01)
nms_thresh = st.slider("Non-Maximum Suppression Threshold", min_value=0.0, max_value=1.0, value=0.45, step=0.01)
image = cv2.imdecode(file_bytes, 1)
image_pil = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
stimage_location = st.empty()
stimage_location.image(image, channels="BGR")
processed_image = detect_image(image_pil, obj_thresh=obj_thresh, nms_thresh=nms_thresh)
stimage_location.image(processed_image, channels="RGB")