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import os
import cv2
import numpy as np
import random
def detect_green_rectangles(image_path):
image = cv2.imread(image_path)
if image is None:
print(f"Image {image_path} could not be read.")
return [], None
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower_green = np.array([40, 40, 40])
upper_green = np.array([80, 255, 255])
mask = cv2.inRange(hsv, lower_green, upper_green)
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
bboxes = []
for contour in contours:
epsilon = 0.02 * cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, epsilon, True)
if len(approx) == 4:
x, y, w, h = cv2.boundingRect(approx)
if w > 20 and h > 20:
bboxes.append([x, y, w, h])
return bboxes, image
def test_detection(image_dir, output_dir, K):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
image_files = [f for f in os.listdir(image_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
random.shuffle(image_files)
for i, image_file in enumerate(image_files[:K]):
image_path = os.path.join(image_dir, image_file)
bboxes, image = detect_green_rectangles(image_path)
if image is None:
continue
for bbox in bboxes:
x, y, w, h = bbox
color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
cv2.rectangle(image, (x, y), (x+w, y+h), color, 2)
output_path = os.path.join(output_dir, f"test_output_{i+1}.png")
cv2.imwrite(output_path, image)
print(f"Processed {image_file}: {len(bboxes)} green rectangles detected")
print(f"Processed {K} images. Results saved in {output_dir}")
# Example usage
image_dir = '/workspace/mimic_cxr'
test_output_dir = '/workspace/test_output'
K = 10 # Number of images to test
# Run the test function
test_detection(image_dir, test_output_dir, K) |