From 6327799556b9761674128014df2a49a1895126c1 Mon Sep 17 00:00:00 2001
From: Kristofers Solo <kristians.cagulis@gmail.com>
Date: Sat, 10 Dec 2022 16:20:06 +0200
Subject: [PATCH] Created traffic light extraction program

---
 src/detector/extract_traffic_lights.py | 83 ++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)
 create mode 100644 src/detector/extract_traffic_lights.py

diff --git a/src/detector/extract_traffic_lights.py b/src/detector/extract_traffic_lights.py
new file mode 100644
index 0000000..862b4ff
--- /dev/null
+++ b/src/detector/extract_traffic_lights.py
@@ -0,0 +1,83 @@
+"""This program extracts traffic lights from images."""
+
+import logging
+from pathlib import Path
+
+import cv2
+from detector.object_detection import (
+    LABEL_TRAFFIC_LIGHT,
+    load_ssd_coco,
+    perform_object_detection,
+)
+from detector.paths import CROPPED_IMAGES_PATH, INPUT_PATH, LOGS_PATH
+
+# Set up logging
+logger = logging.getLogger(__name__)
+handler = logging.FileHandler(str(Path.joinpath(LOGS_PATH, f"{__name__}.log")))
+formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
+handler.setFormatter(formatter)
+logger.addHandler(handler)
+
+
+def extract_traffic_lights() -> None:
+    files = Path.iterdir(INPUT_PATH)
+
+    # Load the object detection model
+    this_model = load_ssd_coco()
+
+    # Keep track of the number of traffic lights found
+    traffic_light_count = 0
+
+    # Keep track of the number of image files that were processed
+    file_count = 0
+
+    # Display a count of the number of images we need to process
+    # logger.info(f"Number of Images: {len(files)}")
+
+    # Go through each image file, one at a time
+    for file in files:
+
+        # Detect objects in the image
+        # img_rgb is the original image in RGB format
+        # out is a dictionary containing the results of object detection
+        # file_name is the name of the file
+        img_rgb, out, file_name = perform_object_detection(model=this_model, file_name=file, save_annotated=None, model_traffic_lights=None)
+
+        # Every 10 files that are processed
+        if file_count % 10 == 0:
+
+            # Display a count of the number of files that have been processed
+            logger.info(f"Images processed: {file_count}")
+
+            # Display the total number of traffic lights that have been identified so far
+            logger.info(f"Number of Traffic lights identified: {traffic_light_count}")
+
+        # Increment the number of files by 1
+        file_count += 1
+
+        # For each traffic light (i.e. bounding box) that was detected
+        for idx, _ in enumerate(out["boxes"]):
+
+            # Extract the type of object that was detected
+            obj_class = out["detection_classes"][idx]
+
+            # If the object that was detected is a traffic light
+            if obj_class == LABEL_TRAFFIC_LIGHT:
+
+                # Extract the coordinates of the bounding box
+                box = out["boxes"][idx]
+
+                # Extract (i.e. crop) the traffic light from the image
+                traffic_light = img_rgb[box["y"]:box["y2"], box["x"]:box["x2"]]
+
+                # Convert the traffic light from RGB format into BGR format
+                traffic_light = cv2.cvtColor(traffic_light, cv2.COLOR_RGB2BGR)
+
+                # Store the cropped image in a folder named 'traffic_light_cropped'
+                cv2.imwrite(str(Path.joinpath(CROPPED_IMAGES_PATH, f"{traffic_light_count}.jpg")), traffic_light)
+
+                # Increment the number of traffic lights by 1
+                traffic_light_count += 1
+
+    # Display the total number of traffic lights identified
+    logger.info(f"Number of Traffic lights identified: {traffic_light_count}")