Refactored and implemented match result object with simplified results
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1 changed files with 114 additions and 110 deletions
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@ -1,103 +1,18 @@
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from concurrent.futures import ProcessPoolExecutor, as_completed
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from dataclasses import dataclass, field
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import numpy as np
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import cv2
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import logging
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logger = logging.getLogger()
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logger.setLevel(logging.DEBUG)
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def normalize_image(image: np.ndarray) -> np.ndarray:
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return cv2.resize(image, (160*2,90*2))
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return cv2.resize(image, (160 * 2, 90 * 2))
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def compare_images(image: np.ndarray, target_image: np.ndarray) -> float:
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err = np.mean((image.astype(float) - target_image.astype(float)) ** 2)
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max_val = 255.0 if image.dtype == np.uint8 else 1.0
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return 1 - (err / (max_val**2))
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def compare_episode_to_image(path_to_episode: str, image: np.ndarray) -> float:
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video = cv2.VideoCapture(path_to_episode)
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frame_number = 0
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frame_count = video.get(cv2.CAP_PROP_FRAME_COUNT)
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scores = []
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while frame_number < frame_count:
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video.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
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success, frame = video.read()
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if not success:
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break
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frame = normalize_image(frame)
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scores.append(compare_images(frame, image))
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frame_number += 24
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return np.max(scores) # np.percentile(scores, 90)
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def plot_value(values):
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import matplotlib.pyplot as plt
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# Your data array
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y_values = values
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# x-axis is just the index
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x_values = list(range(len(y_values)))
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# Create the plot
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plt.figure(figsize=(60, 20))
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plt.plot(x_values, y_values, marker='o', linestyle='-', color='blue', label='Series A')
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plt.title('Simple Plot')
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plt.xlabel('Index')
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plt.ylabel('Value')
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plt.grid(True)
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plt.legend()
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# Show the plot
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plt.savefig("plot.png")
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def compare_episode_to_images(path_to_episode: str, images: list[np.ndarray]) -> float:
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# return sum(compare_episode_to_image(path_to_episode, image) for image in images) / len(images)
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video = cv2.VideoCapture(path_to_episode)
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scores = []
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frame_number = 0
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frame_count = video.get(cv2.CAP_PROP_FRAME_COUNT)
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while True:
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# while frame_number < frame_count:
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# video.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
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success, frame = video.read()
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if not success:
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break
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frame = normalize_image(frame)
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for ref_index in range(len(images)):
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score = compare_images(frame, images[ref_index])
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scores.append(score)
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frame_number += 24
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# print(scores)
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video.set(cv2.CAP_PROP_POS_FRAMES, scores.index(max(scores)))
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success, frame = video.read()
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if success:
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cv2.imwrite("match.png", normalize_image(frame))
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cv2.imwrite("ref.png", normalize_image(images[0]))
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video.release()
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plot_value(scores)
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return np.max(scores) # np.percentile(scores, 90)
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def match_episodes_to_references(episodes: list[str], references: dict[str, list[np.ndarray]]) -> dict[str, dict[str, float]]:
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results = {}
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# Compare to episodes
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for episode in episodes:
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results[episode] = efficient_episode_matching(episode, references)
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print(results[episode])
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return results
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return float(1 - (err / (max_val**2)))
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def _match_episode_batch(
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@ -156,8 +71,10 @@ def _match_episode_batch(
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def efficient_episode_matching(
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path_to_episode: str,
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references: dict[str, list[np.ndarray]],
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*,
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batch_size: int = 1440,
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step_size: int = 24,
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significance_threshold: float = 0.8,
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) -> dict[str, float]:
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"""_summary_
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@ -166,6 +83,7 @@ def efficient_episode_matching(
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references (dict[str, list[np.ndarray]]): _description_
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batch_size (int, optional): Batch size in frame count for multicore frame matching. At least step_size+1. Defaults to 1440, 24fps for 1 minute.
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step_size (int, optional): Number of frames to skip on intial match-step. Defaults to 24.
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significance_threshold (float, optional): Matching threshold for coarse matching, above granular matching is performed. Defaults to 0.8.
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Returns:
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dict[str, float]: _description_
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@ -175,7 +93,7 @@ def efficient_episode_matching(
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# Load and prepare episode
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video = cv2.VideoCapture(path_to_episode)
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frame_count = video.get(cv2.CAP_PROP_FRAME_COUNT)
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frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
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logging.info(f"Loading and preprocessing [{frame_count}] frames ...")
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normalized_frames = []
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@ -200,7 +118,13 @@ def efficient_episode_matching(
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results: dict[str, float] = {ref: 0.0 for ref in references.keys()}
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with ProcessPoolExecutor() as executor:
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futures = [
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executor.submit(_match_episode_batch, batch, references, step_size)
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executor.submit(
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_match_episode_batch,
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batch,
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references,
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step_size,
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significance_threshold,
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)
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for batch in batches
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]
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for i, future in enumerate(as_completed(futures)):
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@ -215,20 +139,113 @@ def efficient_episode_matching(
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logging.info(f"Finished [{path_to_episode}]")
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results_msg = "Results"
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for ref, score in results.items():
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results_msg += f"\n{ref}\t{score:.4f}"
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results_msg += f"\n{ref}\t{score:.4f}{'\tWinner' if score == max(results.values()) else ''}"
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logging.info(results_msg)
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return results
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@dataclass
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class EpisodesMatchResult:
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scores: dict[str, dict[str, float]] = field(default_factory=dict)
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"""Raw scores from matching. For each episode, the score to each reference."""
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perfect_match: bool = False
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"""If true, scores were unambiguous amd simplified to reference_by_episode and episode_by_reference."""
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reference_by_episode: dict[str, str] | None = None
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"""Simplified form of results, if unambiguous (see perfect_match). Episode as key, reference as value."""
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@property
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def episode_by_reference(self) -> dict[str, str] | None:
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"""Simplified form of results, if unambiguous (see perfect_match). Refernce as key, episode as value."""
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if self.reference_by_episode is None:
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return None
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return {ref: episode for episode, ref in self.reference_by_episode}
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def simplify_match(self) -> None:
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"""Simplifies score if perfect match given."""
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ref_by_ep = {}
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# Match for episodes
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for episode, ref_scores in self.scores.items():
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winner_score = max(ref_scores.values())
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winner_ref = [
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ref for ref, score in ref_scores.items() if score == winner_score
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][0]
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ref_by_ep[episode] = winner_ref
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# Validate winners with matches for references
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references = list(list(self.scores.values())[0].keys())
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for ref in references:
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episode_scores = {
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episode: ref_scores[ref] for episode, ref_scores in self.scores.items()
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}
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winner_score = max(episode_scores.values())
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winner_episode = [
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epi for epi, score in episode_scores.items() if score == winner_score
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][0]
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# Make sure that no other episode as this ref as winner
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# Winner episode might have a higher scoring ref, in case there are more refs than episodes
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# => So ref might not be assigned at all
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for episode, ep_ref in ref_by_ep.items():
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if episode == winner_episode:
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continue
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elif ep_ref == ref:
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return # Not a perfect match
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self.perfect_match = True
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self.reference_by_episode = ref_by_ep
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def __str__(self) -> str:
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msg = f"Perfect Match: {self.perfect_match}"
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if self.perfect_match and self.reference_by_episode:
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for ep, ref in self.reference_by_episode.items():
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msg += f"\n{ep}\t{ref}"
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else:
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msg += "\nBy Episode:"
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for ep, scores in self.scores.items():
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msg += f"{ep}"
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for ref, value in scores.items():
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msg += f"\t{ref}\t{value}"
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msg += "\nBy Reference:"
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for ref in list(self.scores.values())[0].keys():
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msg += f"{ref}"
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for episode, scores in self.scores.items():
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msg += f"\t{episode}\t{scores[ref]}"
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return msg
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def match_episodes_to_references(
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episodes: list[str], references: dict[str, list[np.ndarray]]
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) -> EpisodesMatchResult:
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results = EpisodesMatchResult()
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# Compare refs to episode
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for episode in episodes:
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results.scores[episode] = efficient_episode_matching(
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episode, references, significance_threshold=0.9
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)
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results.simplify_match()
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return results
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if __name__ == "__main__":
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logger = logging.getLogger()
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logger.setLevel(logging.DEBUG)
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episodes = [
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"./episodes/B1_t05.mkv",
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# "./episodes/B1_t06.mkv",
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# "./episodes/B1_t07.mkv",
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# "./episodes/B1_t08.mkv",
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# "./episodes/B1_t09.mkv",
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# "./episodes/B1_t10.mkv",
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"./episodes/B1_t06.mkv",
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"./episodes/B1_t07.mkv",
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"./episodes/B1_t08.mkv",
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"./episodes/B1_t09.mkv",
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"./episodes/B1_t10.mkv",
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]
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references = {
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@ -266,17 +283,4 @@ if __name__ == "__main__":
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results = match_episodes_to_references(episodes, references)
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print(results)
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print("\nBy Episode:")
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for ep, scores in results.items():
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print(f"{ep}")
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for ref, value in scores.items():
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print(f"\t{ref}\t{value}")
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print("\nBy Reference:")
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for ref in list(results.values())[0].keys():
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print(f"{ref}")
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for episode, scores in results.items():
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print(f"\t{episode}\t{scores[ref]}")
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print("Done.")
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