add application file

.gitignore

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+__pycache__
+model/dataset
+model/img
+model/loss
+model/PIE
+*/__pycache__

app.py

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+import streamlit as st
+import numpy as np
+
+with st.sidebar:
+    st.image("logo/logo_small_EQUES.png")
+    st.button("カメラと接続")
+
+    from PIL import Image
+    import io 
+    uploaded_file = st.file_uploader('画像をアップロード')
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        img_array = np.array(image)
+        st.image(
+            image, caption='upload images',
+            use_column_width=True
+        )
+
+    option = st.selectbox(
+        "テスト画像の使用",
+        ("テスト画像１", "テスト画像２", "テスト画像３"),
+        index=None,
+        placeholder="テスト画像を選択してください。",
+    )
+   
+if st.button("不審者を検知"):
+    if option != None:
+        image = Image.open(f"img/sample/{option}.JPG")
+    assert image != None,"画像をアップロードしてください。"
+    from run import inference
+    with st.spinner("Operation in progress. Please wait."):
+        output, (labels, scores, indices) = inference(image)
+    st.image("img/detected.png")
+    
+    st.title("解析結果")
+
+    for a,b in zip(labels, scores):
+        st.write(a,b)
+
+    if len(indices) != 0:
+        st.warning('不審者が検知された可能性があります', icon="⚠️")
+    else:
+        st.info('不審者は検知されませんでした', icon="ℹ️")
+
+    
+    st.title("解析結果詳細")
+    st.write(output)
+

run.py

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+import argparse
+import os
+import torch
+from model.detector import *
+from model.backbone import *
+from model.loss import *
+from model.data import Therin
+import datetime
+
+from model.detector.fasterRCNN import FasterRCNN
+from model.backbone.densenet import DenseNet
+from model.utils.engine import *
+from torchvision.models.detection.backbone_utils import resnet_fpn_backbone, _resnet_fpn_extractor
+from torchvision.models.detection import fasterrcnn_resnet50_fpn, FasterRCNN_ResNet50_FPN_V2_Weights
+from torchvision import transforms as T
+from PIL import Image, ImageDraw
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def label_to_text_en(l):
+    d = {0: "creeping", 1: "crawling", 2: "stooping", 3: "climbing", 4: "other"}
+    return d[l]
+
+def label_to_text_ja(l):
+    d = {0: "しのびこんでいる", 1: "這っている", 2: "かがんでいる", 3: "よじ登っている", 4: "その他"}
+    return d[l]
+
+def show_bb(img, x, y, w, h, text, textcolor, bbcolor):
+    draw = ImageDraw.Draw(img)
+    text_w, text_h = draw.textsize(text)
+    label_y = y if y <= text_h else y - text_h
+    draw.rectangle((x, label_y, x+w, label_y+h), outline=bbcolor)
+    draw.rectangle((x, label_y, x+text_w, label_y+text_h), outline=bbcolor, fill=bbcolor)
+    draw.text((x, label_y), text, fill=textcolor)
+
+def postprocess(true_image, o):
+    copy_im = true_image.copy()
+    data = o[0]
+    boxes = data["boxes"]
+    labels = data["labels"].tolist()
+    scores = data["scores"].tolist()
+
+    selected_labels = []
+    selected_scores = []
+    selected_indices = []
+    thresh = 0.30
+    for i, box in enumerate(boxes.tolist()):
+        # if scores[i] > thresh:
+
+        if i == scores.index(max(scores)):
+            show_bb(copy_im, box[0],box[1],box[2],box[3], label_to_text_en(labels[i]) , (255, 255, 255), (255, 0, 0)) #xywh
+            
+            selected_labels.append(label_to_text_ja(labels[i]))
+            selected_scores.append( '{:.3f}'.format(scores[i]))
+            selected_indices.append(i)
+    copy_im.show()
+    copy_im.save("img/detected.png")
+    return selected_labels, selected_scores, selected_indices
+
+
+def inference(image_pil):
+    num_classes = 5
+    backbone = resnet_fpn_backbone('resnet18', False)
+    model = FasterRCNN(backbone, num_classes)
+    model.eval()
+    state_dict = torch.load('model/model/densenet-model-9-mAp--1.0.pth')
+    model.load_state_dict(state_dict["model"])
+    _transform = T.Compose([T.ToTensor()])
+    image = image_pil.convert("RGB")
+    image = _transform(image)
+    output = model([image])
+    res = postprocess(image_pil, output)
+    return output, res