video version

app.py

@@ -1,12 +1,213 @@
+import cv2
 import torch
 from ultralytics import YOLO
 import gradio as gr
+import threading
+import time
+import os
+import zipfile
+from datetime import datetime
+import pandas as pd
+import tempfile
 
-model = YOLO("best0625.pt")  # 或者是從 hf_hub_download 取得
+# === 模型與設定 ===
+model = YOLO("best0628.pt")
+TARGET_CLASS_NAME = "kumay"
+save_dir = "saved_bears"
+log_path = os.path.join(save_dir, "detection_log.csv")
+os.makedirs(save_dir, exist_ok=True)
 
-def detect(image):
-    results = model(image)
-    return results[0].plot()  # 回傳帶框圖像
+# === 全域狀態 ===
+latest_frame = None
+lock = threading.Lock()
+streaming = False
 
-iface = gr.Interface(fn=detect, inputs="image", outputs="image")
-iface.launch(show_api=True)
+# 初始化 log 檔
+if not os.path.exists(log_path):
+    with open(log_path, "w") as f:
+        f.write("frame_id,timestamp,timestamp_diff,filename,class,confidence\n")
+
+last_detection_time = None
+frame_counter = 0
+
+# === Webcam 持續讀取 ===
+def webcam_reader():
+    global latest_frame
+    cap = cv2.VideoCapture(0)
+    while True:
+        ret, frame = cap.read()
+        if ret:
+            with lock:
+                latest_frame = frame.copy()
+        time.sleep(0.03)
+
+# === 偵測與儲存 ===
+def detect_and_save(frame):
+    global last_detection_time, frame_counter
+    results = model(frame)
+    names = results[0].names
+    has_bear = False
+    best_conf = 0
+    best_cls_name = ""
+
+    for box in results[0].boxes:
+        cls_id = int(box.cls[0])
+        cls_name = names[cls_id]
+        conf = float(box.conf[0])
+        if cls_name == TARGET_CLASS_NAME and conf >= 0.85:
+            has_bear = True
+            if conf > best_conf:
+                best_conf = conf
+                best_cls_name = cls_name
+
+    if has_bear:
+        timestamp = datetime.now()
+        timestamp_str = timestamp.strftime("%Y%m%d_%H%M%S_%f")[:-3]
+        filename = os.path.join(save_dir, f"bear_{timestamp_str}.png")
+
+        for box in results[0].boxes:
+            cls_id = int(box.cls[0])
+            cls_name = names[cls_id]
+            conf = float(box.conf[0])
+            if cls_name == TARGET_CLASS_NAME and conf >= 0.85:
+                xyxy = box.xyxy[0].cpu().numpy().astype(int)
+                cv2.putText(
+                    frame,
+                    f"{cls_name}: {conf:.2f}",
+                    (xyxy[0], xyxy[1] - 10),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.6,
+                    (0, 255, 0),
+                    2,
+                )
+                cv2.rectangle(frame, (xyxy[0], xyxy[1]), (xyxy[2], xyxy[3]), (0, 255, 0), 2)
+
+        cv2.imwrite(filename, frame)
+        print(f"📸 偵測到 {best_cls_name}，儲存：{filename}")
+        assert os.path.exists(filename)
+
+        diff = (timestamp - last_detection_time).total_seconds() if last_detection_time else 0.0
+        with open(log_path, "a") as f:
+            f.write(f"{frame_counter},{timestamp},{diff:.3f},{filename},{best_cls_name},{best_conf:.4f}\n")
+        last_detection_time = timestamp
+
+    frame_counter += 1
+    return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+# === Webcam 處理 ===
+def get_annotated_frame():
+    global latest_frame
+    with lock:
+        frame = latest_frame.copy() if latest_frame is not None else None
+    if frame is None:
+        return None
+    return detect_and_save(frame)
+
+def streaming_loop():
+    global streaming
+    while streaming:
+        frame = get_annotated_frame()
+        if frame is not None:
+            with lock:
+                cv2.imwrite("latest_stream.png", cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
+        time.sleep(0.2)
+
+def start_stream():
+    global streaming
+    streaming = True
+    threading.Thread(target=streaming_loop, daemon=True).start()
+
+def stop_stream():
+    global streaming
+    streaming = False
+
+# === 影片偵測 ===
+def detect_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    W = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    H = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    output_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+    out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (W, H))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        annotated = detect_and_save(frame)
+        out.write(cv2.cvtColor(annotated, cv2.COLOR_RGB2BGR))
+
+    cap.release()
+    out.release()
+    print(f"✅ 影片處理完成：{output_path}")
+    return output_path
+
+# === ZIP 功能 ===
+def create_zip():
+    zip_path = "detection_package.zip"
+    with zipfile.ZipFile(zip_path, "w") as zipf:
+        for fname in os.listdir(save_dir):
+            fpath = os.path.join(save_dir, fname)
+            if os.path.isfile(fpath):
+                zipf.write(fpath, arcname=os.path.join("saved_bears", fname))
+        if os.path.exists(log_path):
+            zipf.write(log_path, arcname="detection_log.csv")
+    return zip_path
+
+def read_csv():
+    if os.path.exists(log_path):
+        df = pd.read_csv(log_path)
+        if "frame_id" in df.columns:
+            return df.sort_values(by="frame_id", ascending=False).reset_index(drop=True)
+        return df
+    return []
+
+def get_latest_image():
+    return "latest_stream.png" if os.path.exists("latest_stream.png") else None
+
+# === 啟動 webcam 執行緒 ===
+threading.Thread(target=webcam_reader, daemon=True).start()
+
+# === Gradio UI ===
+with gr.Blocks() as demo:
+    gr.Markdown("## 🐻 台灣黑熊偵測系統")
+
+    with gr.Tab("📹 上傳影片辨識"):
+        gr.Markdown("上傳影片，逐幀偵測台灣黑熊，並自動儲存出現畫面")
+        video_input = gr.Video()
+        video_output = gr.Video()
+        video_button = gr.Button("上傳並分析影片")
+        video_button.click(fn=detect_video, inputs=video_input, outputs=video_output)
+
+    with gr.Tab("📷 即時攝影機偵測"):
+        gr.Markdown("啟用 webcam 進行即時偵測，若出現台灣黑熊則自動儲存影像")
+        webcam_output = gr.Image(
+            label="即時辨識結果",
+            interactive=False,
+            type="filepath",
+            value=get_latest_image,
+            every=0.2
+        )
+        with gr.Row():
+            start_btn = gr.Button("▶️ 開始直播")
+            stop_btn = gr.Button("⏹ 停止直播")
+        start_btn.click(fn=start_stream, inputs=[], outputs=[])
+        stop_btn.click(fn=stop_stream, inputs=[], outputs=[])
+
+    with gr.Tab("📁 下載與預覽"):
+        gr.Markdown("### 預覽與下載偵測圖片與紀錄檔")
+        log_df = gr.Dataframe(label="detection_log.csv 預覽", interactive=False)
+        load_log_btn = gr.Button("🔄 重新載入紀錄檔")
+        load_log_btn.click(fn=read_csv, outputs=log_df)
+
+        csv_file = gr.File(value=log_path, label="⬇️ 下載 CSV 檔")
+
+        gr.Markdown("### 打包圖片與紀錄檔（.zip）")
+        zip_btn = gr.Button("📦 產生 ZIP 檔")
+        zip_file = gr.File(label="⬇️ 點我下載壓縮檔")
+        zip_btn.click(fn=create_zip, outputs=zip_file)
+
+# 啟動
+if __name__ == "__main__":
+    demo.launch()

best0625.pt → best0628.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6550b7ee01b41d3e14918c42966f47722ff6530302dc6574994ed67649e057b0
-size 6218787
+oid sha256:1536806a99f859860f48938f253c99edd6da12af5de188fb5ecd50ea58f89a90
+size 22516707

requirements.txt

@@ -1,3 +1,6 @@
 ultralytics
 gradio
 torch
+opencv-python
+numpy
+