Spaces:
Runtime error
Runtime error
| 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 |