import os
import cv2
import json

import imageio

import matplotlib.pyplot as plt


base_path = "/home/datasets/vidor"
base_vid_path = "train/video"
base_ann_path = "train_annotation/training"

output_dir = 'visual_output'

ann_path = os.path.join(base_path, base_ann_path, "0000/2401075277.json")

if not os.path.exists(ann_path):
    print(f"Error: Annotation file not found at path: {ann_path}")
    exit()

# Load annotation data
with open(ann_path) as f:
    annotation_data = json.load(f)

video_path_ = annotation_data["video_path"]
video_path = os.path.join(base_path, base_vid_path, video_path_)
print(video_path)

if not os.path.exists(video_path):
    print(f"Error: Video file not found at path: {video_path}")
    exit()

# Convert relative video path to absolute path
video_path = os.path.abspath(video_path)
print(f"Video path: {video_path}")

fps = annotation_data["fps"]
frame_count = annotation_data["frame_count"]

width = annotation_data["width"]
height = annotation_data["height"]

subject_objects = {obj["tid"]: obj["category"] for obj in annotation_data["subject/objects"]}
trajectories = annotation_data["trajectories"]
relation_instances = annotation_data.get("relation_instances", [])


# Open video
#cap = cv2.VideoCapture(video_path, cv2.CAP_FFMPEG)
#if not cap.isOpened():
    #print(f"Error: Could not open video at path: {video_path}")
    #exit()


# Define the codec and create VideoWriter object

output_dir = os.path.join(output_dir, video_path_[:4])
print(output_dir)
output_path = os.path.join(output_dir, video_path_[5:])
print(output_path)

if not os.path.exists(output_dir):
    # Create the directory if it does not exist
    os.makedirs(output_dir)
    print(f"Directory '{output_dir}' created.")
else:
    print(f"Directory '{output_dir}' already exists.")

reader = imageio.get_reader(video_path, 'ffmpeg')
writer = imageio.get_writer(output_path, fps=fps)


frame_idx = 0

for frame in reader:
    if frame_idx >= frame_count:
        break

    # Convert frame to OpenCV format
    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)

    # Draw bounding boxes for the current frame
    if frame_idx < len(trajectories):
        for obj in trajectories[frame_idx]:
            tid = obj["tid"]
            bbox = obj["bbox"]
            category = subject_objects.get(tid, "unknown")

            xmin, ymin, xmax, ymax = bbox["xmin"], bbox["ymin"], bbox["xmax"], bbox["ymax"]
            cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
            cv2.putText(frame, category, (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

            # Draw relation text
            for relation in relation_instances:
                subject_tid = relation["subject_tid"]
                object_tid = relation["object_tid"]
                predicate = relation["predicate"]
                begin_fid = relation["begin_fid"]
                end_fid = relation["end_fid"]

                if frame_idx >= begin_fid and frame_idx < end_fid and tid in [subject_tid, object_tid]:
                    subject_bbox = [bbox["bbox"] for bbox in trajectories[begin_fid] if bbox["tid"] == subject_tid][0]
                    object_bbox = [bbox["bbox"] for bbox in trajectories[begin_fid] if bbox["tid"] == object_tid][0]

                    subject_x, subject_y, _, _ = subject_bbox
                    object_x, object_y, _, _ = object_bbox

                    text = f"{subject_objects.get(subject_tid, 'unknown')} {predicate} {subject_objects.get(object_tid, 'unknown')}"
                    text_size, _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 5)
                    cv2.rectangle(frame, (xmin + 10, ymin + 20 - text_size[1]), (xmin + 10 + text_size[0], ymin + 30), (0, 0, 0), -1)
                    cv2.putText(frame, text, (xmin + 10, ymin + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)

    # Convert frame back to imageio format
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # Write the frame to the output video
    writer.append_data(frame)
    frame_idx += 1

reader.close()
writer.close()
print('Annotated video saved to {output_path}')