From 40175f59d13ba1ef1529fe3f80353820889161d9 Mon Sep 17 00:00:00 2001
From: Shivam Khandelwal <skbly7@gmail.com>
Date: Fri, 16 Apr 2021 20:57:00 +0530
Subject: [PATCH] Rebased public facing starter kit with internal version
---
README.md | 5 ++---
core/metrics | 1 +
docs/DATASET.md | 4 ++--
requirements.txt | 4 +++-
test.py | 23 +++++++++++------------
utility/verify_or_download_data.py | 19 +++++++++++++++++--
6 files changed, 36 insertions(+), 20 deletions(-)
create mode 160000 core/metrics
diff --git a/README.md b/README.md
index 1ba44c7..de94573 100644
--- a/README.md
+++ b/README.md
@@ -67,7 +67,7 @@ You can add your SSH Keys to your GitLab account by going to your profile settin
3. **Install** competition specific dependencies!
```
- cd airborne_detection_starter_kit
+ cd airborne-detection-starter-kit
pip3 install -r requirements.txt
```
@@ -137,9 +137,8 @@ You need to make sure that your model can predict airborne objects for each flig
## Local evaluation
-You can also test end to end evaluation on your own systems. This step will provide you random scores.
+You can also test end to end evaluation on your own systems. The scripts are available in `core/metrics` folder.
-TBA
# 📎 Important links
diff --git a/core/metrics b/core/metrics
new file mode 160000
index 0000000..3a3dc7e
--- /dev/null
+++ b/core/metrics
@@ -0,0 +1 @@
+Subproject commit 3a3dc7eb623027d0081fadb7e43eeca3ddd9be8d
diff --git a/docs/DATASET.md b/docs/DATASET.md
index 6f67112..7c5a84a 100644
--- a/docs/DATASET.md
+++ b/docs/DATASET.md
@@ -122,8 +122,8 @@ This library will provide you quick access and download only the files you requi
```python
from core.dataset import Dataset
dataset = Dataset(local_path='data/part1', s3_path='s3://airborne-obj-detection-challenge-training/part1/')
-dataset.add(local_path='data/part1', s3_path='s3://airborne-obj-detection-challenge-training/part1/')
-dataset.add(local_path='data/part1', s3_path='s3://airborne-obj-detection-challenge-training/part1/')
+dataset.add(local_path='data/part2', s3_path='s3://airborne-obj-detection-challenge-training/part2/')
+dataset.add(local_path='data/part3', s3_path='s3://airborne-obj-detection-challenge-training/part3/')
```
NOTE: You don't need to have `groundtruth.json` pre-downloaded, it will automatically download, save and load them for you.
diff --git a/requirements.txt b/requirements.txt
index a6e4e06..d17d22b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,4 +7,6 @@ boto3
opencv-python<4.3
bounding-box
numpy
-Pillow
\ No newline at end of file
+Pillow
+imgaug
+core/metrics
diff --git a/test.py b/test.py
index 4b22304..0a0ffad 100644
--- a/test.py
+++ b/test.py
@@ -30,27 +30,26 @@ class RandomPredictor(AirbornePredictor):
NOTE: In case you want to load your model, please do so in `predict_setup` function.
"""
def inference(self, flight_id):
- # In this random example, we are making use of dataset exploration i.e. objects are generally located somewhere near
- # center range, and similarly for typical range of frames they are visible, etc...
+
class_name = random.choice(["Airplane1", "Helicopter1"])
track_id = random.randint(0, 3)
bbox = [random.uniform(1300, 1500), random.uniform(1000, 1200), random.uniform(50, 100), random.uniform(50, 100)]
-
- initial_empty_frames = random.randint(500, 900)
- frame_with_airborne_object = random.randint(100, 200)
+
+ i = random.randint(500, 900)
+ j = random.randint(100, 200)
for frame_image in self.get_all_frame_images(flight_id):
# frame_image_path = self.get_frame_image_location(flight_id, frame_image)
# img = Image.open(frame_image_path)
- # Do something... (example of loading images for evaluation)
+ # Do something...
- initial_empty_frames -= 1
- if initial_empty_frames > 0:
+ i -= 1
+ if i > 0:
continue
-
- frame_with_airborne_object -= 1
- if frame_with_airborne_object > 0:
- confidence = random.uniform(0.5, 1)
+
+ j -= 1
+ if j > 0:
+ confidence = random.uniform(0.7, 1)
self.register_object_and_location(class_name, track_id, bbox, confidence, frame_image)
diff --git a/utility/verify_or_download_data.py b/utility/verify_or_download_data.py
index eae74ba..34cdea8 100644
--- a/utility/verify_or_download_data.py
+++ b/utility/verify_or_download_data.py
@@ -1,4 +1,19 @@
-import os
+import os, sys
+sys.path.append(os.path.dirname(os.path.realpath(os.getcwd())))
+sys.path.append(os.path.realpath(os.getcwd()))
+
+from core.dataset import Dataset
if __name__ == "__main__":
- print("Easy to use download script to be added here!")
+ dataset = Dataset(local_path='data/part1', s3_path='s3://airborne-obj-detection-challenge-training/part1/')
+ dataset.add(local_path='data/part2', s3_path='s3://airborne-obj-detection-challenge-training/part2/')
+ dataset.add(local_path='data/part3', s3_path='s3://airborne-obj-detection-challenge-training/part3/')
+
+ i = 1
+ all_flights = dataset.get_flight_ids()
+ for flight_id in all_flights:
+ print("Downloading Flight#%s (%s/%s)..." % (flight_id, i, len(all_flights)))
+ dataset.get_flight_by_id(flight_id).download()
+ i += 1
+
+ print("Download complete.")
--
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