Update app.py

app.py

@@ -1,28 +1,44 @@
+import os
+
+from gradio_webrtc import WebRTC
+import requests
+from PIL import Image
+
+import matplotlib.pyplot as plt
+
+from random import choice
+import io
+
+import gradio as gr
+
+import cv2
+import numpy as np
+
+from io import BytesIO
+import random
+import tempfile
+from pathlib import Path
 
 import torch
 from transformers import pipeline
 
 from PIL import Image
 
-import matplotlib.pyplot as plt
 import matplotlib.patches as patches
 
-from random import choice
-import io
 
 detector50 = pipeline(model="facebook/detr-resnet-50")
 
 detector101 = pipeline(model="facebook/detr-resnet-101")
 
 
-import gradio as gr
 
 COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
             "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
             "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
 
 fdic = {
-    "family" : "Impact",
+#    "family" : "Impact",
     "style" : "italic",
     "size" : 15,
     "color" : "yellow",
@@ -30,12 +46,33 @@ fdic = {
 }
 
 
-def get_figure(in_pil_img, in_results):
+def infer(model, in_pil_img):
+
+    results = None
+    if model == "detr-resnet-101":
+        results = detector101(in_pil_img)
+    else:
+        results = detector50(in_pil_img)
+
+    return results
+
+
+#######################################
+
+
+def query_data(model, in_pil_img: Image.Image):
+    return infer(model, in_pil_img)
+
+
+
+def get_figure(in_pil_img):
     plt.figure(figsize=(16, 10))
     plt.imshow(in_pil_img)
-    #pyplot.gcf()
+    
     ax = plt.gca()
 
+    in_results = query_data(in_pil_img)
+
     for prediction in in_results:
         selected_color = choice(COLORS)
 
@@ -50,15 +87,8 @@ def get_figure(in_pil_img, in_results):
     return plt.gcf()
 
 
-def infer(model, in_pil_img):
-
-    results = None
-    if model == "detr-resnet-101":
-        results = detector101(in_pil_img)
-    else:
-        results = detector50(in_pil_img)
-
-    figure = get_figure(in_pil_img, results)
+def infer(in_pil_img):
+    figure = get_figure(in_pil_img)
 
     buf = io.BytesIO()
     figure.savefig(buf, bbox_inches='tight')
@@ -68,39 +98,91 @@ def infer(model, in_pil_img):
     return output_pil_img
 
 
-with gr.Blocks(title="DETR Object Detection - ClassCat",
-                    css=".gradio-container {background:lightyellow;}"
-               ) as demo:
-    #sample_index = gr.State([])
-
-    gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">DETR Object Detection</div>""")
-
-    gr.HTML("""<h4 style="color:navy;">1. Select a model.</h4>""")
-
-    model = gr.Radio(["detr-resnet-50", "detr-resnet-101"], value="detr-resnet-50", label="Model name")
+def process_single_frame(frame):
+    # 将 BGR 转换为 RGB
+    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    
+    # 创建 PIL 图像对象
+    pil_image = Image.fromarray(rgb_frame)
 
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">2-a. Select an example by clicking a thumbnail below.</h4>""")
-    gr.HTML("""<h4 style="color:navy;">2-b. Or upload an image by clicking on the canvas.</h4>""")
+    # 获取带有标注信息的图像
+    figure = get_figure(pil_image)
 
+    buf = BytesIO()
+    figure.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
+    buf.seek(0)
+    annotated_image = Image.open(buf).convert('RGB')
+
+    return np.array(annotated_image)
+
+
+def infer_video(input_video_path):
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        # output_video_path = Path(tmp_dir) / "output.mp4"
+        cap = cv2.VideoCapture(input_video_path)
+
+        if not cap.isOpened():
+            raise ValueError("无法打开输入视频文件")
+
+        # width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        # height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        # fps = cap.get(cv2.CAP_PROP_FPS)
+        # fourcc = int(cap.get(cv2.CAP_PROP_FOURCC))  # 使用原始视频的编码器
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # 获取总帧数
+
+        # out = cv2.VideoWriter(str(output_video_path), fourcc, fps, (width, height))
+
+        frame_count = 0
+        try:
+            while frame_count < total_frames:
+                ret, frame = cap.read()
+                if not ret:
+                    print(f"提前结束：在第 {frame_count} 帧时无法读取帧")
+                    break
+                
+                frame_count += 1
+
+                # 处理单帧并转换为 OpenCV 格式（BGR）
+                processed_frame = process_single_frame(frame)
+                bgr_frame = cv2.cvtColor(processed_frame, cv2.COLOR_RGB2BGR)
+
+                yield bgr_frame
+
+                # 可选：显示进度
+                if frame_count % 30 == 0:
+                    print(f"已处理 {frame_count}/{total_frames} 帧")
+
+                # if frame_count == 48:
+                #     print("测试结束")
+                #     return None
+
+        finally:
+            cap.release()
+        
+        return None
+
+    
+# 更新 Gradio 接口以支持视频输入和输出
+with gr.Blocks(title="长沙电网项目",
+               css=".gradio-container {background:lightyellow;}"
+              ) as demo:
+    gr.HTML("<div style='font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;'>长沙电网项目</div>")
+    
     with gr.Row():
-        input_image = gr.Image(label="Input image", type="pil")
-        output_image = gr.Image(label="Output image with predicted instances", type="pil")
-
-    gr.Examples(['samples/cats.jpg', 'samples/detectron2.png', 'samples/cat.jpg', 'samples/hotdog.jpg'], inputs=input_image)
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">3. Then, click "Infer" button to predict object instances. It will take about 10 seconds (on cpu)</h4>""")
-
-    send_btn = gr.Button("Infer")
-    send_btn.click(fn=infer, inputs=[model, input_image], outputs=[output_image])
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">Reference</h4>""")
-    gr.HTML("""<ul>""")
-    gr.HTML("""<li><a href="https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_attention.ipynb" target="_blank">Hands-on tutorial for DETR</a>""")
-    gr.HTML("""</ul>""")
+        input_video = gr.Video(label="输入视频")
+        output_video = WebRTC(label="WebRTC Stream",
+                                   rtc_configuration=None,
+                                   mode="receive",
+                                   modality="video")
+        detect = gr.Button("Detect", variant="primary")
+    output_video.stream(
+        fn=infer_video,
+        inputs=[input_video],
+        outputs=[output_video],
+        trigger=detect.click
+    )
 
+demo.launch(debug=True)
 
 #demo.queue()
 demo.launch(debug=True)