add cleanup + move scripts to utils + move data directory + updated d…

…evcontainer to use opencv-headless

.devcontainer/devcontainer.json

@@ -1,25 +1,23 @@
 {
-    "name": "Python 3",
-    // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
-    "image": "mcr.microsoft.com/devcontainers/python:0-3.11-bullseye",
-    "features": {
-        "ghcr.io/devcontainers-contrib/features/coverage-py:2": {}
-    },
-
-    // Use 'forwardPorts' to make a list of ports inside the container available locally.
-    // "forwardPorts": [],
-
-    // Use 'postCreateCommand' to run commands after the container is created.
-    "postCreateCommand": "pip3 install --user -r requirements.txt",
-
-    // Configure tool-specific properties.
-    "customizations": {
-        // Configure properties specific to VS Code.
-        "vscode": {
-          // Add the IDs of extensions you want installed when the container is created.
-          "extensions": [
-            "streetsidesoftware.code-spell-checker"
-          ]
-        }
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:0-3.11-bullseye",
+  "features": {
+    "ghcr.io/devcontainers-contrib/features/coverage-py:2": {}
+  },
+  // Use 'forwardPorts' to make a list of ports inside the container available locally.
+  // "forwardPorts": [],
+  // Use 'postCreateCommand' to run commands after the container is created.
+  // We will override with opencv-python-headless as we cannot show the visualizations.
+  "postCreateCommand": "pip3 install --user -r requirements.txt && pip install opencv-python-headless",
+  // Configure tool-specific properties.
+  "customizations": {
+    // Configure properties specific to VS Code.
+    "vscode": {
+      // Add the IDs of extensions you want installed when the container is created.
+      "extensions": [
+        "streetsidesoftware.code-spell-checker"
+      ]
     }
-}
+  }
+}

.gitignore

@@ -1,3 +1,4 @@
-assets/videos
+data/video.mp4
 __pycache__
 .venv
+.env

.vscode/launch.json

@@ -0,0 +1,15 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python Debugger: Current File",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        }
+    ]
+}

example_inference.py

@@ -1,8 +1,7 @@
 from uugai_python_color_prediction.ColorPrediction import ColorPrediction
 from uugai_python_dynamic_queue.MessageBrokers import RabbitMQ
 from uugai_python_kerberos_vault.KerberosVault import KerberosVault
-
-from assets.scripts.read_first_frame import read_first_frame
+from utils.read_first_frame import read_first_frame
 from dotenv import load_dotenv
 import os
 
@@ -40,7 +39,7 @@
     # Retrieve media from the Kerberos Vault
     print("4) Retrieving media from the Kerberos Vault...")
     resp = kerberos_vault.retrieve_media(
-        message, media_type='video', media_savepath='assets/videos/video.mp4')
+        message, media_type='video', media_savepath='data/video.mp4')
 
     ############################################
     # Implement your own logic here
@@ -49,11 +48,15 @@
     # In this example, we will perform color prediction on the first frame of
     # the video
     print("5) Perform action on the media... (in this case color prediction)")
-    main_colors, _, _ = ColorPrediction.find_main_colors(image=read_first_frame('assets/videos/video.mp4'),
+    main_colors, _, _ = ColorPrediction.find_main_colors(image=read_first_frame('data/video.mp4'),
                                                          min_clusters=1,
                                                          max_clusters=5,
                                                          downsample_factor=0.95,
                                                          increase_elbow=0,
                                                          verbose=False,
                                                          plot=False)
     print("\tMain colors found in the first frame:", main_colors.tolist())
+
+    # Cleanup the video
+    print("6) Cleaning up the video...")
+    os.remove('data/video.mp4')