Upload app.py

new app.py,Add compress oversized images to 1024 size.

app.py

@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+
+import torch
+import gradio as gr
+from transformers import Owlv2Processor, Owlv2ForObjectDetection
+import spaces
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+from PIL import Image
+import numpy as np
+# 设置设备
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+#引入模型和推理器
+model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble").to(device)
+processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble")
+#载入图像
+@spaces.GPU
+#输入图像，搜索文本，检测分数
+def query_image(img, text_queries, score_threshold,compress):
+    img = load_image_as_np_array(img, compress)
+    text_queries = text_queries
+    #分割搜索文本
+    text_queries = text_queries.split(",")
+    #转换为正方行torch矩阵
+    #（长宽边最大的那个设置为size）
+    size = max(img.shape[:2])
+    target_sizes = torch.Tensor([[size, size]])
+    #创建输入（搜索文本和图像转换为torch张量发送到GPU）
+    inputs = processor(text=text_queries, images=img, return_tensors="pt").to(device)
+    #禁用梯度计算，运行推理
+    with torch.no_grad():
+        outputs = model(**inputs)
+    #输出分数和边界框信息
+    outputs.logits = outputs.logits.cpu()
+    outputs.pred_boxes = outputs.pred_boxes.cpu()
+    #导出输出结果
+    results = processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes)
+    #分类存储输出结果的边界框，分数，标签
+    boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]
+
+    #创建空列表
+    result_labels = []
+#    all_result = []
+    #遍历分类存储的输出结果
+    for box, score, label in zip(boxes, scores, labels):
+        #转换为整数
+        box = [int(i) for i in box.tolist()]
+        #过滤阈值以下的目标
+        if score < score_threshold:
+            continue
+        result_labels.append((box, text_queries[label.item()]))
+    text = len(result_labels)
+    return img, result_labels,img, result_labels
+p
+
+#图像输入：图像压缩
+def load_image_as_np_array(img, compress=False):
+    # 输入图像文件
+    # with Image.open(image_path) as img:
+        # 转换为RGB
+        # img = img.convert("RGB")
+    #数组-图像
+    img = Image.fromarray(img)
+        #图像压缩
+    if compress:
+            # 获取图像尺寸
+        width, height = img.size
+
+            # 检查图像分辨率是否大于2048
+        max_dimension = max(width, height)
+        if max_dimension > 1024:
+                # Calculate the new size, maintaining the aspect ratio
+            scale_factor = 1024 / max_dimension
+            new_width = int(width * scale_factor)
+            new_height = int(height * scale_factor)
+
+                # 缩放图像
+            img = img.resize((new_width, new_height))
+
+        # 图像-数组
+    img = np.array(img)
+    return img
+
+demo1 = gr.Interface(
+    query_image,
+    inputs=[gr.Image(),
+            gr.Text(value="insect",label="提示词(多个用,分开)"),
+            gr.Slider(0, 1, value=0.2,label="确信度阈值"),
+            gr.Checkbox(value=True,label="图像压缩")],
+    outputs=[gr.Annotatedimage()],
+    title="Zero-Shot Object Detection with OWLv2",
+)
+demo1.launch()