Initialize edge streamlit page

deployment/edge/ansible/files/docker-compose.template.yml

@@ -39,3 +39,15 @@ services:
       dockerfile: Dockerfile
     ports:
       - 8080:80
+
+  edge_streamlit:
+    container_name: edge_streamlit
+    build:
+      context: edge_streamlit
+      dockerfile: Dockerfile
+    # devices:
+    #   - /dev/video0:/dev/video0
+    #   - /dev/video2:/dev/video2
+    ports:
+      - 8502:8501
+    profiles: [edge]

docs/edge_orchestrator.md

@@ -191,7 +191,7 @@ Here's a template of a config file.
     }
   },
   "item_rule": {
-    "name": "min_threshold_KO_rule", #the item rule name
+    "name": "min_threshold_ko_rule", #the item rule name
     "parameters": {
       "threshold": 1
     }

edge_orchestrator/config/station_configs/yolo_coco_nano_with_1_fake_camera.json

@@ -21,7 +21,7 @@
       }
     },
     "item_rule": {
-      "name": "min_threshold_KO_rule",
+      "name": "min_threshold_ko_rule",
       "parameters": {
         "threshold": 1
       }

edge_orchestrator/config/station_configs/yolo_coco_nano_with_2_fake_camera.json

@@ -0,0 +1,48 @@
+{
+    "name": "yolo_coco",
+    "cameras": {
+      "camera_id3": {
+        "type": "fake",
+        "source": "people_dataset",
+        "position": "back",
+        "exposition": 100,
+        "models_graph": {
+          "model_id4": {
+            "name": "yolo_coco_nano",
+            "depends_on": []
+          }
+        },
+        "camera_rule": {
+          "name": "expected_label_rule",
+          "parameters": {
+            "expected_label": ["person"]
+          }
+        }
+      },
+      "camera_id4": {
+        "type": "fake",
+        "source": "people_dataset",
+        "position": "back",
+        "exposition": 100,
+        "models_graph": {
+          "model_id4": {
+            "name": "yolo_coco_nano",
+            "depends_on": []
+          }
+        },
+        "camera_rule": {
+          "name": "expected_label_rule",
+          "parameters": {
+            "expected_label": ["person"]
+          }
+        }
+      }
+    },
+    "item_rule": {
+      "name": "min_threshold_ko_rule",
+      "parameters": {
+        "threshold": 1
+      }
+    }
+  }
+

edge_streamlit/Dockerfile

@@ -0,0 +1,29 @@
+FROM python:3.9-slim
+
+# let print debug messages log on the console
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONIOENCODING=UTF-8
+
+ENV ORCHESTRATOR_URL=http://edge_orchestrator:8000/api/v1/
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    software-properties-common \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+
+RUN pip install -r requirements.txt
+
+COPY app.py .
+COPY prediction_boxes.py .
+
+EXPOSE 8501
+
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+
+ENTRYPOINT ["streamlit", "run", "app.py"]

edge_streamlit/Makefile

@@ -0,0 +1,27 @@
+SHELL := /bin/bash
+.SHELLFLAGS = -ec
+.ONESHELL:
+.SILENT:
+
+.PHONY: help
+help:
+	echo "❓ Use \`make <target>\`"
+	grep -E '^\.PHONY: [a-zA-Z0-9_-]+ .*?## .*$$' $(MAKEFILE_LIST) | \
+		awk 'BEGIN {FS = "(: |##)"}; {printf "\033[36m%-30s\033[0m %s\n", $$2, $$3}'
+
+.PHONY: env ## Create a virtual environment
+env:
+	python3 -m venv env
+	. env/bin/activate
+	pip install --upgrade pip
+	pip install -r requirements.txt
+
+.PHONY: run ## Run the streamlit app
+run:
+	streamlit run app.py
+
+.PHONY: linting  ## 🐍 Lint Python files to conform to the PEP 8 style guide
+linting:
+	black .
+	isort . --gitignore
+	autoflake -i --remove-all-unused-imports -r --ignore-init-module-imports . --exclude .venv

edge_streamlit/app.py

@@ -0,0 +1,110 @@
+import json
+import os
+import time
+import requests
+import streamlit as st
+from prediction_boxes import filtering_items_that_have_predictions, plot_predictions
+from PIL import Image
+from io import BytesIO
+
+# Page configuration
+st.set_page_config(page_title="VIO-edge", page_icon="🔦", layout="wide")
+
+URL_ORCH = os.getenv("ORCHESTRATOR_URL", "http://localhost:8000/api/v1/")
+
+url_config = URL_ORCH + "configs"
+url_active_config = URL_ORCH + "configs/active"
+url_trigger = URL_ORCH + "trigger"
+
+
+def main():
+    active_config = json.loads(requests.get(url_active_config).text)
+    if active_config:
+        st.session_state.active_config = active_config
+
+    if "active_config" not in st.session_state:
+        st.session_state.active_config = None
+    if "trigger" not in st.session_state:
+        st.session_state.trigger = False
+    if "item_id" not in st.session_state:
+        st.session_state.item_id = None
+
+    col1, col2, col3 = st.columns(3)
+
+    configs = json.loads(requests.get(url_config).text)
+
+    active_config_index = 0
+    if st.session_state.active_config:
+        active_config_name = st.session_state.active_config.get("name")
+        active_config_index = next(
+            (
+                index
+                for (index, config) in enumerate(configs.values())
+                if config["name"] == active_config_name
+            ),
+            0,
+        )
+    option = col1.selectbox(
+        "Select an option",
+        tuple(configs),
+        index=active_config_index,
+        label_visibility="collapsed",
+    )
+
+    if col2.button("Active", use_container_width=True):
+        st.session_state.item_id = None
+        body = {"config_name": option}
+        requests.post(url=url_active_config, json=body)
+        st.session_state.active_config = json.loads(
+            requests.get(url_active_config).text
+        )
+
+    if st.session_state.active_config:
+        active_config_name = st.session_state.active_config.get("name")
+        col2.write(f"active config: {active_config_name}")
+
+    if st.session_state.active_config:
+        if col3.button("Trigger", use_container_width=True):
+            st.session_state.trigger = True
+            response = requests.post(url_trigger)
+            item_id = response.json().get("item_id")
+            st.session_state.item_id = item_id
+            col3.write(f"item id: {item_id}")
+
+    columns = st.columns(2)
+
+    if st.session_state.item_id and (st.session_state.active_config is not None):
+        time.sleep(5)
+
+        url_metadata = URL_ORCH + f"items/{st.session_state.item_id}"
+        response = requests.get(url_metadata)
+        metadata = response.json()
+        decision = metadata["decision"]
+        inferences = metadata["inferences"]
+
+        print("decision", decision)
+        print("inferences", inferences)
+
+        cameras = st.session_state.active_config["cameras"]
+        for i, camera in enumerate(cameras):
+            url_binaries = (
+                URL_ORCH + f"items/{st.session_state.item_id}/binaries/{camera}"
+            )
+            response = requests.get(url_binaries)
+            image = response.content
+            # If metadata is not empty, we plot the predictions
+            if filtering_items_that_have_predictions(metadata, camera):
+                image = Image.open(BytesIO(image))
+                image = plot_predictions(image, camera, metadata)
+            columns[i].image(image, channels="BGR", width=450)
+            if inferences.get(camera):
+                columns[i].markdown(inferences[camera])
+
+        st.markdown(
+            f"<h1 style='text-align: center; color: #e67e22;'>{decision}</h1>",
+            unsafe_allow_html=True,
+        )
+
+
+if __name__ == "__main__":
+    main()

edge_streamlit/prediction_boxes.py

@@ -0,0 +1,64 @@
+from PIL import Image, ImageDraw, ImageFont
+
+
+def filtering_items_that_have_predictions(metadata: dict, camera_id: str):
+    if metadata == {} or metadata is None:
+        return False
+    elif metadata["inferences"] == {}:
+        return False
+    for model_results in metadata["inferences"][camera_id].values():
+        if model_results == "NO_DECISION" or model_results == {}:
+            return False
+        for prediction in model_results.values():
+            if "location" not in prediction:
+                return False
+    return True
+
+
+def plot_predictions(img: Image, camera_id: str, metadata: dict):
+    if metadata["inferences"] == {}:
+        return img
+    camera_prediction_metadata = metadata["inferences"][camera_id]
+    models = camera_prediction_metadata.keys()
+    for model in models:
+        detected_objects = camera_prediction_metadata[model].values()
+        for detected_object in detected_objects:
+            bbox = detected_object["location"]
+            label = detected_object["label"]
+            img = draw_bbox(img, bbox, label)
+
+    return img
+
+
+def draw_bbox(img, bbox, label):
+    draw = ImageDraw.Draw(img)
+    width, height = img.size
+
+    # Convert normalized coordinates to pixel values
+    top_left_x = int(bbox[0] * width)
+    top_left_y = int(bbox[1] * height)
+    bottom_right_x = int(bbox[2] * width)
+    bottom_right_y = int(bbox[3] * height)
+
+    # Draw the bounding box
+    draw.rectangle(
+        [top_left_x, top_left_y, bottom_right_x, bottom_right_y], outline="red", width=2
+    )
+
+    # Load a font
+    font = ImageFont.load_default()
+
+    # Calculate text size and position
+    text_size = draw.textbbox((0, 0), label, font=font)[2:]
+    text_x = top_left_x
+    text_y = top_left_y - text_size[1] if top_left_y - text_size[1] > 0 else top_left_y
+
+    # Draw the label background
+    draw.rectangle(
+        [text_x, text_y, text_x + text_size[0], text_y + text_size[1]], fill="red"
+    )
+
+    # Draw the label text
+    draw.text((text_x, text_y), label, fill="white", font=font)
+
+    return img

edge_streamlit/requirements.txt

@@ -0,0 +1,3 @@
+opencv-python-headless==4.7.0.72
+streamlit==1.41.0
+numpy==1.24.1