计算机视觉中目标检测的数据预处理

import cv2
import os
import numpy as np
# Function to calculate image quality score (example implementation)
def calculate_image_quality(image):
# Add your image quality calculation logic here
# This could involve techniques such as blur detection, sharpness measurement, etc.
# Return a quality score or metric for the given image
return 0.0
# Function to calculate object coverage score (example implementation)
def calculate_object_coverage(image, bounding_boxes):
# Add your object coverage calculation logic here
# This could involve measuring the percentage of image area covered by objects
# Return a coverage score or metric for the given image
return 0.0
# Directory containing the dataset
dataset_dir = “path/to/your/dataset”
# Iterate over the images in the dataset
for image_name in os.listdir(dataset_dir):
image_path = os.path.join(dataset_dir, image_name)
image = cv2.imread(image_path)
# Example: Calculate image quality score
quality_score = calculate_image_quality(image)
# Example: Calculate object coverage score
bounding_boxes = [] # Retrieve bounding boxes for the image (you need to implement this)
coverage_score = calculate_object_coverage(image, bounding_boxes)
# Decide on the selection criteria and thresholds
# You can modify this based on your specific problem and criteria
if quality_score > 0.8 and coverage_score > 0.5:
# This image meets the desired criteria, so you can perform further processing or save it as needed
# For example, you can copy the image to another directory for further processing or analysis
selected_image_path = os.path.join(“path/to/selected/images”, image_name)
cv2.imwrite(selected_image_path, image)

import cv2
import numpy as np
import os
# Directory paths
dataset_dir = “path/to/your/dataset”
output_dir = “path/to/preprocessed/data”
# Create the output directory if it doesn’t exist
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Iterate over the images in the dataset
for image_name in os.listdir(dataset_dir):
image_path = os.path.join(dataset_dir, image_name)
annotation_path = os.path.join(dataset_dir, image_name.replace(“.jpg”, “.txt”))
# Read the image
image = cv2.imread(image_path)
# Read the annotation file (assuming it contains bounding box coordinates)
with open(annotation_path, “r”) as file:
lines = file.readlines()
bounding_boxes = []
for line in lines:
# Parse the bounding box coordinates
class_id, x, y, width, height = map(float, line.split())
# Example: Perform any necessary data preprocessing steps
# Here, we can normalize the bounding box coordinates to values between 0 and 1
normalized_x = x / image.shape[1]
normalized_y = y / image.shape[0]
normalized_width = width / image.shape[1]
normalized_height = height / image.shape[0]
# Store the normalized bounding box coordinates
bounding_boxes.append([class_id, normalized_x, normalized_y, normalized_width, normalized_height])
# Example: Perform any additional preprocessing steps on the image
# For instance, you can resize the image to a desired size or apply data augmentation techniques
# Save the preprocessed image
preprocessed_image_path = os.path.join(output_dir, image_name)
cv2.imwrite(preprocessed_image_path, image)
# Save the preprocessed annotation (in the same format as the original annotation file)
preprocessed_annotation_path = os.path.join(output_dir, image_name.replace(“.jpg”, “.txt”))
with open(preprocessed_annotation_path, “w”) as file:
for bbox in bounding_boxes:
class_id, x, y, width, height = bbox
file.write(f”{class_id} {x} {y} {width} {height}\n”)