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from ultralytics import YOLOv10 as YOLO
import streamlit as st
import cv2
import numpy as np
import settings
import matplotlib.pyplot as plt
def load_model(model_path):
model = YOLO(model_path)
return model
def _display_detected_frames(conf, model, st_frame, image):
if isinstance(image, dict):
st.error("Invalid image format: 'dict' object received.")
return
# Convert image to RGB format for processing with OpenCV
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = model(image_rgb, conf=conf)
# Get bounding boxes, labels, and confidences
boxes = results[0].boxes.xyxy.cpu().numpy()
labels = results[0].boxes.cls.cpu().numpy()
confidences = results[0].boxes.conf.cpu().numpy()
# Category dictionary
category_dict = {
0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus',
6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant',
11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat',
16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear',
22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag',
27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard',
32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove',
36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle',
40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl',
46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli',
51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake',
56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table',
61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard',
67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink',
72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors',
77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'
}
# Initialize colors
num_classes = len(category_dict)
colors = plt.cm.get_cmap('hsv', num_classes)
# Prepare annotations
for box, label, confidence in zip(boxes, labels, confidences):
x1, y1, x2, y2 = box.astype(int)
label_name = category_dict[int(label)]
confidence_text = f"{label_name} {confidence:.2f}"
class_color = colors(int(label) / num_classes)[:3]
class_color = [int(c * 255) for c in class_color]
# Draw bounding boxes and labels on the image
cv2.rectangle(image, (x1, y1), (x2, y2), class_color, 2)
font_scale = 1.0
thickness = 2
(text_width, text_height), baseline = cv2.getTextSize(confidence_text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, thickness)
cv2.rectangle(image, (x1, y1 - text_height - 10), (x1 + text_width, y1), class_color, -1)
cv2.putText(image, confidence_text, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255), thickness)
# Display the annotated image in Streamlit
st_frame.image(image, caption='Detected Image', channels="RGB", use_column_width=True)
def play_stored_video(conf, model):
source_vid = st.sidebar.selectbox("Choose a video...", settings.VIDEOS_DICT.keys())
if st.sidebar.button('Detect Video Objects'):
try:
vid_cap = cv2.VideoCapture(str(settings.VIDEOS_DICT.get(source_vid)))
st_frame = st.empty()
while vid_cap.isOpened():
success, image = vid_cap.read()
if success:
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Convert BGR to RGB
_display_detected_frames(conf, model, st_frame, image_rgb)
else:
vid_cap.release()
break
except Exception as e:
st.sidebar.error("Error loading video: " + str(e))
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