Merge pull request #65 from ks6088ts-labs/feature/issue-64_object-detection

add object detection demo

Author: ks6088ts

.gitignore

@@ -163,3 +163,4 @@ cython_debug/
 *.env
 generated/
 *.mp3
+*.pt

apps/99_streamlit_examples/README.md

@@ -69,6 +69,10 @@ Access to http://localhost:8501 and select the sample you want to run from the s
 
 ![Visualize location](../../docs/images/99_streamlit_examples.map.png)
 
+#### 10. Object detection
+
+![Object detection](../../docs/images/99_streamlit_examples.objectdetection.png)
+
 ## References
 
 - [🎈 Streamlit + LLM Examples App](https://github.com/streamlit/llm-examples)

apps/99_streamlit_examples/pages/10_Object_detection.py

@@ -0,0 +1,68 @@
+import cv2
+import numpy as np
+import streamlit as st
+from dotenv import load_dotenv
+from ultralytics import YOLO
+
+load_dotenv()
+
+with st.sidebar:
+    model_name = st.selectbox(
+        label="Select a model",
+        options=[
+            "yolov8n.pt",
+            "yolov9c.pt",
+            "yolov10n.pt",
+            # https://docs.ultralytics.com/models/yolov8/#supported-tasks-and-modes
+        ],
+        key="model_name",
+        index=0,
+    )
+
+
+st.title("Object detection")
+
+st.info("Upload an image and AI will detect objects in the image.")
+
+uploaded_file = st.file_uploader(
+    "Upload an image",
+    type=(
+        "jpg",
+        "jpeg",
+        "png",
+        "gif",
+        "bmp",
+        "tiff",
+    ),
+)
+if uploaded_file is not None:
+    button = st.button("Detect objects")
+
+row1_left, row1_right = st.columns(2)
+with row1_left:
+    if uploaded_file:
+        st.image(
+            uploaded_file,
+            use_column_width=True,
+            caption="Input image",
+        )
+
+with row1_right:
+    if uploaded_file and button:
+        with st.spinner("Thinking..."):
+            model = YOLO(model_name)
+            bytes_data = uploaded_file.getvalue()
+            cv2_img = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
+            results = model(cv2_img, conf=0.5, classes=[0])
+            output_img = results[0].plot(labels=True, conf=True)
+            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2RGB)
+            categories = results[0].boxes.cls
+            person_num = len(categories)
+
+            # Output
+            st.image(
+                output_img,
+                use_column_width=True,
+                caption="Output image",
+            )
+            st.text(f"Number of people: {person_num}")