今天是第14天最近地震頻繁因此我們可以寫一個yolo辨識氣候變遷的程式，以下是程式碼
import cv2
import numpy as np
import time
from ultralytics import YOLO
from collections import defaultdict
# 載入YOLOv8模型
model = YOLO('yolov8n.pt')
# 設定自訂的標籤
model.names = {0: 'flood', 1: 'wildfire'}
# 影片輸入路徑與輸出路徑
input_video = 'climate_change_video.mp4'
output_video = 'annotated_output.mp4'
# 設定視頻輸出參數
cap = cv2.VideoCapture(input_video)
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
out = cv2.VideoWriter(output_video, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_width, frame_height))
# 計算場景中各類物體佔比
def calculate_object_proportion(results, frame_area):
object_areas = defaultdict(int)
for r in results:
for box in r.boxes:
label = model.names[int(box.cls)]
x1, y1, x2, y2 = map(int, box.xyxy)
object_area = (x2 - x1) * (y2 - y1)
object_areas[label] += object_area
proportions = {label: area / frame_area for label, area in object_areas.items()}
return proportions
# 發送警報函數
def send_alert(detection_type):
# 這裡可以加入發送電子郵件或通知的邏輯
print(f"警報：偵測到 {detection_type}！")
# 處理每一幀影片
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_area = frame.shape[0] * frame.shape[1]
start_time = time.time()
results = model(frame)
# 在影像上繪製偵測結果
annotated_frame = results[0].plot()
# 計算並顯示佔比
proportions = calculate_object_proportion(results, frame_area)
for label, prop in proportions.items():
cv2.putText(annotated_frame, f"{label}: {prop*100:.2f}%", (10, 30 + 30*list(proportions.keys()).index(label)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
# 發送警報（如果檢測到洪水或火災）
for label in proportions.keys():
if label in ['flood', 'wildfire']:
send_alert(label)
# 儲存每一幀到影片
out.write(annotated_frame)
# 顯示影像
cv2.imshow('YOLOv8 - Climate Change Detection', annotated_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
print(f"Processed frame in {time.time() - start_time:.2f} seconds")
# 釋放資源
cap.release()
out.release()
cv2.destroyAllWindows()
import cv2
import numpy as np
import time
from ultralytics import YOLO
from collections import defaultdict
cv2：OpenCV，用於影像處理和顯示。numpy：數值計算庫，這裡用於處理數據。time：用於計算處理時間。YOLO：從Ultralytics庫載入YOLO模型。defaultdict：用於計算每種類別的物體面積。model = YOLO('yolov8n.pt')
model.names = {0: 'flood', 1: 'wildfire'}
YOLO('yolov8n.pt')：載入預訓練的YOLOv8模型。yolov8n.pt是YOLOv8的輕量版本模型檔案。model.names：設定模型的標籤，這裡我們假設模型已經經過微調，能夠識別“flood”（洪水）和“wildfire”（森林火災）。input_video = 'climate_change_video.mp4'
output_video = 'annotated_output.mp4'
cap = cv2.VideoCapture(input_video)
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
out = cv2.VideoWriter(output_video, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_width, frame_height))
cv2.VideoCapture(input_video)：開啟輸入影片。frame_width, frame_height, fps：獲取影片的寬度、高度和幀率。cv2.VideoWriter(output_video, ...)：設定輸出影片的寫入參數，將處理後的幀寫入新影片。def calculate_object_proportion(results, frame_area):
object_areas = defaultdict(int)
for r in results:
for box in r.boxes:
label = model.names[int(box.cls)]
x1, y1, x2, y2 = map(int, box.xyxy)
object_area = (x2 - x1) * (y2 - y1)
object_areas[label] += object_area
proportions = {label: area / frame_area for label, area in object_areas.items()}
return proportions
calculate_object_proportion：計算每種類別物體在影像中的佔比。results：YOLO模型的檢測結果。frame_area：影像的總面積。object_areas：記錄每種類別的物體總面積。proportions：計算每種類別物體的佔比。def send_alert(detection_type):
# 這裡可以加入發送電子郵件或通知的邏輯
print(f"警報：偵測到 {detection_type}！")
send_alert：當偵測到特定的物體（如洪水或森林火災）時，發送警報。print：這裡僅是示範，可以擴展為實際的通知系統。while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_area = frame.shape[0] * frame.shape[1]
start_time = time.time()
results = model(frame)
# 在影像上繪製偵測結果
annotated_frame = results[0].plot()
# 計算並顯示佔比
proportions = calculate_object_proportion(results, frame_area)
for label, prop in proportions.items():
cv2.putText(annotated_frame, f"{label}: {prop*100:.2f}%", (10, 30 + 30*list(proportions.keys()).index(label)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
# 發送警報（如果檢測到洪水或火災）
for label in proportions.keys():
if label in ['flood', 'wildfire']:
send_alert(label)
# 儲存每一幀到影片
out.write(annotated_frame)
# 顯示影像
cv2.imshow('YOLOv8 - Climate Change Detection', annotated_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
print(f"Processed frame in {time.time() - start_time:.2f} seconds")
while cap.isOpened()：持續處理影片的每一幀。frame_area：計算影像面積。results = model(frame)：使用YOLO模型檢測當前幀中的物體。annotated_frame = results[0].plot()：在影像上繪製檢測結果。calculate_object_proportion(results, frame_area)：計算每種類別物體的佔比並顯示在影像上。send_alert(label)：如果檢測到關心的物體（洪水或森林火災），發送警報。out.write(annotated_frame)：將處理過的幀寫入輸出影片。cv2.imshow('YOLOv8 - Climate Change Detection', annotated_frame)：顯示當前幀。cv2.waitKey(1) & 0xFF == ord('q')：按下‘q’鍵退出。cap.release()
out.release()
cv2.destroyAllWindows()
cap.release()：釋放視頻捕捉對象。out.release()：釋放視頻寫入對象。cv2.destroyAllWindows()：關閉所有OpenCV窗口。這個腳本展示了如何使用YOLOv8模型來偵測和分析氣候變遷相關的影像資料，並將結果處理成影片，進行實時顯示和警報。我可以更進一步擴展功能，例如更高級的分析、不同的警報方式或整合到更大規模的系統中。
import cv2
import numpy as np
import time
from ultralytics import YOLO
from collections import defaultdict
cv2：OpenCV，用於影像處理和顯示。numpy：數值計算庫，這裡用於處理數據。time：用於計算處理時間。YOLO：從Ultralytics庫載入YOLO模型。defaultdict：用於計算每種類別的物體面積。model = YOLO('yolov8n.pt')
model.names = {0: 'flood', 1: 'wildfire'}
YOLO('yolov8n.pt')：載入預訓練的YOLOv8模型。yolov8n.pt是YOLOv8的輕量版本模型檔案。model.names：設定模型的標籤，這裡我們假設模型已經經過微調，能夠識別“flood”（洪水）和“wildfire”（森林火災）。input_video = 'climate_change_video.mp4'
output_video = 'annotated_output.mp4'
cap = cv2.VideoCapture(input_video)
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
out = cv2.VideoWriter(output_video, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_width, frame_height))
cv2.VideoCapture(input_video)：開啟輸入影片。frame_width, frame_height, fps：獲取影片的寬度、高度和幀率。cv2.VideoWriter(output_video, ...)：設定輸出影片的寫入參數，將處理後的幀寫入新影片。def calculate_object_proportion(results, frame_area):
object_areas = defaultdict(int)
for r in results:
for box in r.boxes:
label = model.names[int(box.cls)]
x1, y1, x2, y2 = map(int, box.xyxy)
object_area = (x2 - x1) * (y2 - y1)
object_areas[label] += object_area
proportions = {label: area / frame_area for label, area in object_areas.items()}
return proportions
calculate_object_proportion：計算每種類別物體在影像中的佔比。results：YOLO模型的檢測結果。frame_area：影像的總面積。object_areas：記錄每種類別的物體總面積。proportions：計算每種類別物體的佔比。def send_alert(detection_type):
# 這裡可以加入發送電子郵件或通知的邏輯
print(f"警報：偵測到 {detection_type}！")
send_alert：當偵測到特定的物體（如洪水或森林火災）時，發送警報。print：這裡僅是示範，可以擴展為實際的通知系統。while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_area = frame.shape[0] * frame.shape[1]
start_time = time.time()
results = model(frame)
# 在影像上繪製偵測結果
annotated_frame = results[0].plot()
# 計算並顯示佔比
proportions = calculate_object_proportion(results, frame_area)
for label, prop in proportions.items():
cv2.putText(annotated_frame, f"{label}: {prop*100:.2f}%", (10, 30 + 30*list(proportions.keys()).index(label)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
# 發送警報（如果檢測到洪水或火災）
for label in proportions.keys():
if label in ['flood', 'wildfire']:
send_alert(label)
# 儲存每一幀到影片
out.write(annotated_frame)
# 顯示影像
cv2.imshow('YOLOv8 - Climate Change Detection', annotated_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
print(f"Processed frame in {time.time() - start_time:.2f} seconds")
while cap.isOpened()：持續處理影片的每一幀。frame_area：計算影像面積。results = model(frame)：使用YOLO模型檢測當前幀中的物體。annotated_frame = results[0].plot()：在影像上繪製檢測結果。calculate_object_proportion(results, frame_area)：計算每種類別物體的佔比並顯示在影像上。send_alert(label)：如果檢測到關心的物體（洪水或森林火災），發送警報。out.write(annotated_frame)：將處理過的幀寫入輸出影片。cv2.imshow('YOLOv8 - Climate Change Detection', annotated_frame)：顯示當前幀。cv2.waitKey(1) & 0xFF == ord('q')：按下‘q’鍵退出。cap.release()
out.release()
cv2.destroyAllWindows()
cap.release()：釋放視頻捕捉對象。out.release()：釋放視頻寫入對象。cv2.destroyAllWindows()：關閉所有OpenCV窗口。這個腳本展示了如何使用YOLOv8進行即時氣候變遷影像的偵測，例如洪水和森林火災。它能在影像上標註檢測到的物體，計算每種物體的佔比，並發出警報。這種系統可以幫助我們更好地理解和應對氣候變遷的影響。
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