edited .pkl and added files to create the dir for obj detection

.gitattributes

@@ -1,2 +1,3 @@
 dataset_screenshot.png filter=lfs diff=lfs merge=lfs -text
 *.mp4 filter=lfs diff=lfs merge=lfs -text
+pmfeed_4_3_16_bboxes_and_labels.pkl filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,6 @@
+*.png
+*.jpg
+*.out
+*.pt
+*submit.sh
+*.txt

object_detector_benchmark/create_yolo_dataset.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+mkdir 8_calves_yolo
+mkdir 8_calves_yolo/train
+mkdir 8_calves_yolo/val
+mkdir 8_calves_yolo/test
+mkdir 8_calves_yolo/train/labels
+mkdir 8_calves_yolo/train/images
+mkdir 8_calves_yolo/val/labels
+mkdir 8_calves_yolo/val/images
+cp ../hand_labelled_frames/amfeed_4_3_*png 8_calves_yolo/val/images
+cp ../hand_labelled_frames/amfeed_[2-3]_3_*png 8_calves_yolo/train/images
+cp ../hand_labelled_frames/pmfeed_[1-3]_3_*png 8_calves_yolo/train/images
+cp ../hand_labelled_frames/amfeed_4_3_*txt 8_calves_yolo/val/labels
+cp ../hand_labelled_frames/amfeed_[2-3]_3_*txt 8_calves_yolo/train/labels
+cp ../hand_labelled_frames/pmfeed_[1-3]_3_*txt 8_calves_yolo/train/labels
+

object_detector_benchmark/create_yolo_testset.py

@@ -0,0 +1,60 @@
+import cv2
+import os
+import pandas as pd
+from tqdm import tqdm
+# Path to the input video
+video_path = "../pmfeed_4_3_16.mp4"
+
+# Directory to save frames
+output_dir = "8_calves_yolo/test/images"
+os.makedirs(output_dir, exist_ok=True)
+
+# Load the video
+video_capture = cv2.VideoCapture(video_path)
+frame_number = 1
+with tqdm(total=67760, desc="Extracting Frames") as pbar:
+    while True:
+        # Read the frame
+        ret, frame = video_capture.read()
+        # If no frame is returned, the video has ended
+        if not ret:
+            break
+        
+        # Save the frame with a sequential name
+        frame_filename = os.path.join(output_dir, f"frame_{frame_number:05d}.png")
+        cv2.imwrite(frame_filename, frame)
+        
+        # Increment the frame number
+        frame_number += 1
+        pbar.update(1)
+# Release the video capture
+video_capture.release()
+
+print(f"Frames saved to: {output_dir}")
+
+
+output_dir = "8_calves_yolo/test/labels"
+os.makedirs(output_dir, exist_ok=True)
+df = pd.read_pickle("../pmfeed_4_3_16_bboxes_and_labels.pkl")
+df.reset_index(drop=True, inplace=True)
+i = 0
+j = 0
+frame_id = 1
+with tqdm(total=len(df)) as pbar:
+    while i < len(df):
+        while j < len(df) and df.loc[j]["frame_id"] == df.loc[i]["frame_id"]:
+            j += 1
+            pbar.update(1)
+
+        temp = df.loc[i:j - 1]
+        temp.to_csv(
+            f'{output_dir}/frame_{frame_id:05d}.txt', 
+            header=None, 
+            index=None, 
+            sep=" ", 
+            mode="w",
+            columns=["class", "x", "y", "w", "h"]
+        )
+        frame_id += 1
+        i = j
+print(f"Labels saved to: {output_dir}")