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Configuration error
Configuration error
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import yaml
import cv2
import numpy as np
from pathlib import Path
class ImageProcessor:
def __init__(self, yaml_path):
with open(yaml_path, 'r') as f:
self.config = yaml.safe_load(f)
self.images_info = []
self.reference_size = None
self._load_images()
def _load_images(self):
for img_config in self.config['images']:
img = cv2.imread(img_config['path'])
if img is None:
raise ValueError(f"Cannot load image: {img_config['path']}")
info = {
'image': img,
'duration': float(img_config.get('duration', 1.0)),
'translation': img_config.get('translation', [0, 0]),
'scale': float(img_config.get('scale', 1.0))
}
self.images_info.append(info)
if self.reference_size is None:
self.reference_size = (img.shape[1], img.shape[0])
def _translate_image(self, img, translation):
"""Perform only translation"""
height, width = img.shape[:2]
# Calculate translation amount (pixels)
tx = int(width * translation[0] / 100)
ty = int(height * translation[1] / 100)
# Create translation matrix
M = np.float32([[1, 0, tx], [0, 1, ty]])
# Apply translation while maintaining original dimensions
translated = cv2.warpAffine(img, M, (width, height))
return translated
def _crop_black_borders(self, img):
"""Crop out black borders from the image"""
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Threshold to identify non-black areas
_, thresh = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY)
# Find bounding box of non-black pixels
coords = cv2.findNonZero(thresh)
if coords is None:
return img
x, y, w, h = cv2.boundingRect(coords)
# Crop the image to the bounding box
return img[y:y+h, x:x+w]
def _scale_image(self, img, scale, target_size):
"""Scale the image"""
if scale <= 1:
return cv2.resize(img, target_size)
# First scale up
height, width = img.shape[:2]
scaled_width = int(width * scale)
scaled_height = int(height * scale)
scaled = cv2.resize(img, (scaled_width, scaled_height))
# Center-crop to target dimensions
start_x = (scaled_width - target_size[0]) // 2
start_y = (scaled_height - target_size[1]) // 2
cropped = scaled[start_y:start_y+target_size[1],
start_x:start_x+target_size[0]]
return cropped
def _transform_image(self, img, translation, scale):
"""Apply transformations in sequence: translation → cropping → scaling"""
original_size = (img.shape[1], img.shape[0])
# 1. Translation
translated = self._translate_image(img, translation)
# 2. Black border cropping
cropped = self._crop_black_borders(translated)
# 3. Scale back to original dimensions
transformed = self._scale_image(cropped, scale, original_size)
return transformed
def create_video(self, output_path, fps=25):
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, fps, self.reference_size)
try:
for info in self.images_info:
# Transform image
transformed = self._transform_image(
info['image'],
info['translation'],
info['scale']
)
# Resize to reference dimensions if needed
if transformed.shape[:2] != (self.reference_size[1], self.reference_size[0]):
transformed = cv2.resize(transformed, self.reference_size)
# Write video frames
n_frames = int(info['duration'] * fps)
for _ in range(n_frames):
out.write(transformed)
finally:
out.release()
# Enhance video quality
self._improve_video_quality(output_path)
def _improve_video_quality(self, video_path):
import subprocess
temp_path = video_path + '.temp.mp4'
cmd = [
'ffmpeg', '-i', video_path,
'-c:v', 'libx264',
'-preset', 'slow',
'-crf', '18',
'-y',
temp_path
]
subprocess.run(cmd)
import os
os.replace(temp_path, video_path)
def main():
processor = ImageProcessor('tools/i2v_config.yaml')
processor.create_video('convertd_video.mp4', fps=25)
if __name__ == '__main__':
main()
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