Spaces:
Sleeping
Sleeping
File size: 2,559 Bytes
2590409 3228ab0 2590409 3228ab0 2590409 3228ab0 2590409 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
import numpy as np
from scipy import signal
from config import AMPLITUDE_THRESHOLD, DISTRIBUTION_THRESHOLD, ATTENUATION_THRESHOLD, REFLECTION_THRESHOLD
def calculate_amplitude(image):
return np.max(image)
def classify_amplitude(amplitude):
return "强" if amplitude > AMPLITUDE_THRESHOLD else "弱"
def calculate_distribution_range(box):
width, height = box[2] - box[0], box[3] - box[1]
return width * height
def classify_distribution_range(area):
return "大" if area > DISTRIBUTION_THRESHOLD else "小"
def calculate_attenuation_rate(image):
gradient = np.gradient(np.array(image).mean(axis=2))
return np.mean(np.abs(gradient))
def classify_attenuation_rate(rate):
return "快" if rate > ATTENUATION_THRESHOLD else "慢"
def count_reflections(image, prominence=10):
gray = np.mean(np.array(image), axis=2)
peaks, _ = signal.find_peaks(np.mean(gray, axis=1), prominence=prominence)
return len(peaks)
def classify_reflections(count):
return "多次反射" if count >= REFLECTION_THRESHOLD else "单次反射"
def extract_features(image, detection_result):
"""
Extract features from the image and detection result.
Args:
image: PIL Image
detection_result: Dictionary containing detection results
Returns:
Dictionary of features
"""
np_image = np.array(image)
amplitude = calculate_amplitude(np_image)
amplitude_class = classify_amplitude(amplitude)
# Handle box calculation
if detection_result and 'boxes' in detection_result and len(detection_result['boxes']) > 0:
box = detection_result['boxes'][0]
if not isinstance(box, list):
box = box.tolist()
distribution_range = calculate_distribution_range(box)
distribution_class = classify_distribution_range(distribution_range)
else:
distribution_range = 0
distribution_class = "小"
attenuation_rate = calculate_attenuation_rate(np_image)
attenuation_class = classify_attenuation_rate(attenuation_rate)
reflection_count = count_reflections(np_image)
reflection_class = classify_reflections(reflection_count)
return {
"振幅": amplitude_class,
"分布范围": distribution_class,
"衰减速度": attenuation_class,
"反射次数": reflection_class,
"振幅值": float(amplitude),
"分布范围值": float(distribution_range),
"衰减速度值": float(attenuation_rate),
"反射次数值": int(reflection_count)
}
|