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import streamlit as st
import cv2
import numpy as np
import tempfile
from collections import Counter
import pandas as pd
import pyttsx3
# import streamlit.components.v1 as components
# # embed streamlit docs in a streamlit app
# components.iframe("https://nafisrayan.github.io/ThreeJS-Hand-Control-Panel/", height=500, width=500)
p_time = 0
st.sidebar.title('Settings')
model_type = st.sidebar.selectbox(
'Choose YOLO Model', ('YOLOv8', 'YOLOv9', 'YOLOv10')
)
st.title(f'{model_type} Predictions')
sample_img = cv2.imread('logo2.jpg')
FRAME_WINDOW = st.image(sample_img, channels='BGR')
cap = None
def speak(audio):
engine = pyttsx3.init('sapi5')
voices = engine.getProperty('voices')
engine.setProperty('voice', voices[1].id)
engine.say(audio)
engine.runAndWait()
# Inference Mode
options = st.sidebar.radio(
'Options:', ('Webcam', 'Image', 'Video'), index=1) # removed RTSP for now
# YOLOv8 Model
if model_type == 'YOLOv8':
path_model_file = 'yolov8m.pt'
from ultralytics import YOLO
model = YOLO(path_model_file)
if model_type == 'YOLOv9':
path_model_file = 'yolov9c.pt'
from ultralytics import YOLO
model = YOLO(path_model_file)
if model_type == 'YOLOv10':
st.caption("Work in Progress... >_<")
# path_model_file = 'yolov10n.pt'
# from ultralytics import YOLO
# model = YOLO(path_model_file)
# Load Class names
class_labels = model.names
# Confidence
confidence = st.sidebar.slider(
'Detection Confidence', min_value=0.0, max_value=1.0, value=0.25)
# Draw thickness
draw_thick = st.sidebar.slider(
'Draw Thickness:', min_value=1,
max_value=20, value=3
)
color_pick_list = [None]*len(class_labels)
# Image
if options == 'Image':
upload_img_file = st.sidebar.file_uploader(
'Upload Image', type=['jpg', 'jpeg', 'png'])
if upload_img_file is not None:
pred = st.checkbox(f'Predict Using {model_type}')
file_bytes = np.asarray(
bytearray(upload_img_file.read()), dtype=np.uint8)
img = cv2.imdecode(file_bytes, 1)
FRAME_WINDOW.image(img, channels='BGR')
# st.caption(model(img)[0][0])
if pred:
def predict(model, imag, classes=[], conf=confidence):
if classes:
results = model.predict(imag, classes=classes, conf=confidence)
else:
results = model.predict(imag, conf=conf)
return results
def predict_and_detect(model, img, classes=[], conf=confidence, rectangle_thickness=draw_thick, text_scale=draw_thick, text_thickness=draw_thick):
results = predict(model, img, classes, conf=conf)
# Initialize a Counter to keep track of class occurrences
class_counts = Counter()
for result in results:
for box in result.boxes:
# Update the counter with the class name
class_name = result.names[int(box.cls[0])]
class_counts[class_name] += 1
# Draw the bounding box and label with a random color
color = tuple(np.random.randint(0, 255, size=3).tolist())
cv2.rectangle(img, (int(box.xyxy[0][0]), int(box.xyxy[0][1])),
(int(box.xyxy[0][2]), int(box.xyxy[0][3])), color, rectangle_thickness)
cv2.putText(img, f"{class_name}",
(int(box.xyxy[0][0]), int(box.xyxy[0][1]) - 10),
cv2.FONT_HERSHEY_PLAIN, text_scale, color, text_thickness)
# Convert the Counter to a DataFrame for easy viewing
df_fq = pd.DataFrame.from_dict(class_counts, orient='index', columns=['Number'])
df_fq.index.name = 'Class'
return img, df_fq
img, df_fq = predict_and_detect(model, img, classes=[], conf=confidence)
FRAME_WINDOW.image(img, channels='BGR')
# Updating Inference results
with st.container():
st.markdown("<h2>Inference Statistics</h2>", unsafe_allow_html=True)
st.markdown("<h3>Detected objects in curret Frame</h3>", unsafe_allow_html=True)
st.dataframe(df_fq)
# print("🚀 ~ df_fq:", df_fq)
list_of_tuples = [(row.Number, row.Index) for row in df_fq.itertuples()]
print("🚀 ~ list_of_tuples:", list_of_tuples)
speak(f'This is what I have found {list_of_tuples}')
# Video
if options == 'Video':
upload_video_file = st.sidebar.file_uploader(
'Upload Video', type=['mp4', 'avi', 'mkv'])
if upload_video_file is not None:
pred = st.checkbox(f'Predict Using {model_type}')
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(upload_video_file.read())
cap = cv2.VideoCapture(tfile.name)
while True:
success, img = cap.read()
if not success:
st.error(f"Video NOT working\nCheck Video settings!", icon="🚨")
break
if pred:
def predict(model, img, classes=[], conf=confidence):
if classes:
results = model.predict(img, classes=classes, conf=confidence)
else:
results = model.predict(img, conf=conf)
return results
def predict_and_detect(model, img, classes=[], conf=confidence, rectangle_thickness=draw_thick, text_scale=draw_thick, text_thickness=draw_thick):
results = predict(model, img, classes, conf=conf)
# Initialize a Counter to keep track of class occurrences
class_counts = Counter()
for result in results:
for box in result.boxes:
# Update the counter with the class name
class_name = result.names[int(box.cls[0])]
class_counts[class_name] += 1
# Draw the bounding box and label with a random color
color = tuple(np.random.randint(0, 255, size=3).tolist())
cv2.rectangle(img, (int(box.xyxy[0][0]), int(box.xyxy[0][1])),
(int(box.xyxy[0][2]), int(box.xyxy[0][3])), color, rectangle_thickness)
cv2.putText(img, f"{class_name}",
(int(box.xyxy[0][0]), int(box.xyxy[0][1]) - 10),
cv2.FONT_HERSHEY_PLAIN, text_scale, color, text_thickness)
# Convert the Counter to a DataFrame for easy viewing
df_fq = pd.DataFrame.from_dict(class_counts, orient='index', columns=['Number'])
df_fq.index.name = 'Class'
return img, df_fq
img, df_fq = predict_and_detect(model, img, classes=[], conf=confidence)
FRAME_WINDOW.image(img, channels='BGR')
# Updating Inference results
with st.container():
st.markdown("<h2>Inference Statistics</h2>", unsafe_allow_html=True)
st.markdown("<h3>Detected objects in current Frame</h3>", unsafe_allow_html=True)
st.dataframe(df_fq)
# print("🚀 ~ df_fq:", df_fq)
list_of_tuples = [(row.Number, row.Index) for row in df_fq.itertuples()]
print("🚀 ~ list_of_tuples:", list_of_tuples)
# speak(f'This is what I have found {list_of_tuples}')
# Webcam
if options == 'Webcam':
cam_options = st.sidebar.selectbox('Select Webcam Channel', ('0', '1', '2', '3'))
if not cam_options == 'Select Channel':
pred = st.checkbox(f'Predict Using {model_type}')
cap = cv2.VideoCapture(int(cam_options))
while True:
success, img = cap.read()
if not success:
st.error(f"Webcam NOT working\nCheck Webcam settings!", icon="🚨")
break
if pred:
def predict(model, img, classes=[], conf=confidence):
if classes:
results = model.predict(img, classes=classes, conf=confidence)
else:
results = model.predict(img, conf=conf)
return results
def predict_and_detect(model, img, classes=[], conf=confidence, rectangle_thickness=draw_thick, text_scale=draw_thick, text_thickness=draw_thick):
results = predict(model, img, classes, conf=conf)
# Initialize a Counter to keep track of class occurrences
class_counts = Counter()
for result in results:
for box in result.boxes:
# Update the counter with the class name
class_name = result.names[int(box.cls[0])]
class_counts[class_name] += 1
# Draw the bounding box and label with a random color
color = tuple(np.random.randint(0, 255, size=3).tolist())
cv2.rectangle(img, (int(box.xyxy[0][0]), int(box.xyxy[0][1])),
(int(box.xyxy[0][2]), int(box.xyxy[0][3])), color, rectangle_thickness)
cv2.putText(img, f"{class_name}",
(int(box.xyxy[0][0]), int(box.xyxy[0][1]) - 10),
cv2.FONT_HERSHEY_PLAIN, text_scale, color, text_thickness)
# Convert the Counter to a DataFrame for easy viewing
df_fq = pd.DataFrame.from_dict(class_counts, orient='index', columns=['Number'])
df_fq.index.name = 'Class'
return img, df_fq
img, df_fq = predict_and_detect(model, img, classes=[], conf=confidence)
FRAME_WINDOW.image(img, channels='BGR')
# Updating Inference results
with st.container():
st.markdown("<h2>Inference Statistics</h2>", unsafe_allow_html=True)
st.markdown("<h3>Detected objects in current Frame</h3>", unsafe_allow_html=True)
st.dataframe(df_fq)
# print("🚀 ~ df_fq:", df_fq)
list_of_tuples = [(row.Number, row.Index) for row in df_fq.itertuples()]
print("🚀 ~ list_of_tuples:", list_of_tuples)
# speak(f'This is what I have found {list_of_tuples}')
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