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import cv2
import numpy as np
from typing import List, Tuple, Dict, Any
def process_plant_health(frame: np.ndarray) -> Tuple[List[Dict[str, Any]], np.ndarray]:
"""
Assess the health of plants in the frame based on color.
Args:
frame: Input frame as a numpy array.
Returns:
Tuple of (list of detections, annotated frame).
"""
# Convert to HSV color space
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# Define range for green (healthy) and yellow/brown (unhealthy)
lower_green = np.array([35, 50, 50])
upper_green = np.array([85, 255, 255])
lower_unhealthy = np.array([20, 50, 50])
upper_unhealthy = np.array([35, 255, 255])
mask_healthy = cv2.inRange(hsv, lower_green, upper_green)
mask_unhealthy = cv2.inRange(hsv, lower_unhealthy, upper_unhealthy)
# Combine masks to find plants
mask = cv2.bitwise_or(mask_healthy, mask_unhealthy)
kernel = np.ones((5, 5), np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
# Find contours of plants
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
detections = []
for i, contour in enumerate(contours):
area = cv2.contourArea(contour)
if area < 200: # Ignore small contours
continue
x, y, w, h = cv2.boundingRect(contour)
x_min, y_min, x_max, y_max = x, y, x + w, y + h
# Determine health by comparing the area of healthy vs unhealthy pixels
roi_hsv = hsv[y_min:y_max, x_min:x_max]
healthy_pixels = cv2.countNonZero(cv2.inRange(roi_hsv, lower_green, upper_green))
unhealthy_pixels = cv2.countNonZero(cv2.inRange(roi_hsv, lower_unhealthy, upper_unhealthy))
total_pixels = healthy_pixels + unhealthy_pixels
if total_pixels == 0:
continue
health_status = "healthy" if healthy_pixels > unhealthy_pixels else "unhealthy"
label = f"Plant {i+1} - {health_status.capitalize()}"
detections.append({
"box": [x_min, y_min, x_max, y_max],
"label": label,
"type": "plant",
"health": health_status
})
return detections, frame |