identification_benchmark/crop_pmfeed_4_3_16.py

import cv2
import pandas as pd
import pickle
import os

# Files
pickle_filename = "../pmfeed_4_3_16_bboxes_and_labels.pkl"
video_filename = "../pmfeed_4_3_16.mp4"
output_dir = "all_crops_pmfeed_4_3_16"

# Create output directory if it doesn't exist
os.makedirs(output_dir, exist_ok=True)

# Load the bounding boxes DataFrame from the pickle file
with open(pickle_filename, "rb") as f:
    df = pickle.load(f)

# Open the video file
cap = cv2.VideoCapture(video_filename)
if not cap.isOpened():
    raise IOError(f"Cannot open video file {video_filename}")

# Get video dimensions
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print(f"Video dimensions: {frame_width}x{frame_height}")

# Initialize sliding window pointers and frame counter
num_rows = len(df)
i = 0
frames_processed = 0
# max_frames = 3  # only process first 3 frames

while i < num_rows:
    # Get the current frame_id for this sliding window
    current_frame_id = int(df.iloc[i]["frame_id"])
    j = i
    # Move j until the frame_id changes
    while j < num_rows and df.iloc[j]["frame_id"] == current_frame_id:
        j += 1

    # Set the video to the appropriate frame (frame_id is assumed to be 1-indexed)
    cap.set(cv2.CAP_PROP_POS_FRAMES, current_frame_id - 1)
    ret, frame = cap.read()
    if not ret:
        print(f"Warning: Could not read frame {current_frame_id}")
        i = j
        continue

    # Process all bounding boxes for this frame (from row i to j-1)
    for index in range(i, j):
        row = df.iloc[index]
        # Assuming coordinates are normalized: (center x, center y, width, height)
        x_center = row["x"]
        y_center = row["y"]
        bbox_width = row["w"]
        bbox_height = row["h"]

        # Convert normalized coordinates to absolute pixel values
        left = int((x_center - bbox_width / 2) * frame_width)
        top = int((y_center - bbox_height / 2) * frame_height)
        right = int((x_center + bbox_width / 2) * frame_width)
        bottom = int((y_center + bbox_height / 2) * frame_height)

        # Clamp the coordinates to within the frame dimensions
        left = max(left, 0)
        top = max(top, 0)
        right = min(right, frame_width)
        bottom = min(bottom, frame_height)

        # Skip if resulting crop dimensions are invalid
        if right - left <= 0 or bottom - top <= 0:
            print(f"Warning: Invalid crop dimensions for frame {current_frame_id}, tracklet {row['tracklet_id']}")
            continue

        # Crop the image
        crop_img = frame[top:bottom, left:right]

        # Save crop image with filename format: "pmfeed_4_3_16_frame_<frame_id>_cow_<tracklet_id>.jpg"
        filename = f"pmfeed_4_3_16_frame_{current_frame_id}_cow_{int(row['tracklet_id'])}.jpg"
        output_path = os.path.join(output_dir, filename)
        cv2.imwrite(output_path, crop_img)
        print(f"Saved crop: {output_path}")

    frames_processed += 1
    i = j

# Release video capture
cap.release()
print("Cropping all frames completed.")