Datasets:
Tasks:
Object Detection
Formats:
webdataset
Languages:
English
Size:
< 1K
ArXiv:
Tags:
webdataset
License:
File size: 3,071 Bytes
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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.")
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