Team SneakyTurtle Submission

.gitignore

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+.env

Deployment/app.py

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+from flask import Flask, render_template, request, url_for
+from prediction import predict
+
+app = Flask(__name__, template_folder = "templates")
+
+
+# Controllers
+@app.route("/")
+def mainPage():
+    return render_template("index.html", page = "Home")
+
+@app.route("/predict", methods = ["GET", "POST"])
+def home():
+    output = "hold"
+    if request.method == "POST":
+        f = request.files['file']
+        f.save("static/inputImage.jpg")
+        output = "show"
+        predict()
+    return render_template("index.html", output = output, page = "Predict")
+
+
+@app.route("/team")
+def team():
+    return render_template("index.html", page = "Team")
+
+if __name__ == "__main__":
+    app.run(debug = True)

Deployment/prediction.py

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+import cv2
+import supervision as sv
+import torch
+import pytorch_lightning as pl
+from transformers import DetrForObjectDetection, DetrImageProcessor
+from PIL import Image
+import numpy as np
+from torchvision.utils import draw_bounding_boxes
+
+
+
+
+
+DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+CHECKPOINT = 'facebook/detr-resnet-50'
+CONFIDENCE_TRESHOLD = 0.5
+IOU_TRESHOLD = 0.8
+image_processor = DetrImageProcessor.from_pretrained(CHECKPOINT, ignore_mismatched_sizes  = True )
+model = DetrForObjectDetection.from_pretrained(CHECKPOINT, ignore_mismatched_sizes  = True)
+
+
+model.load_state_dict(torch.load("vehicle_det.pth", map_location = DEVICE), strict = False)
+
+
+categories = {0: {'id': 0, 'name': 'cars', 'supercategory': 'none'},
+ 1: {'id': 1, 'name': 'biker', 'supercategory': 'cars'},
+ 2: {'id': 2, 'name': 'pedestrian', 'supercategory': 'cars'},
+ 3: {'id': 3, 'name': 'car', 'supercategory': 'cars'},
+ 4: {'id': 4, 'name': 'trafficLight', 'supercategory': 'cars'}}
+
+id2label = {k: v['name'] for k,v in categories.items()}
+label2id = {v['name']: k for k,v in categories.items()}
+box_annotator = sv.BoxAnnotator()
+
+
+
+def predict():
+    image = np.asarray(Image.open("static/inputImage.jpg"))
+
+
+    with torch.no_grad():
+
+        # load image and predict
+        inputs = image_processor(images=image, return_tensors='pt')
+        outputs = model(**inputs)
+
+        # post-process
+        target_sizes = torch.tensor([image.shape[:2]])
+        results = image_processor.post_process_object_detection(
+            outputs=outputs, 
+            threshold=CONFIDENCE_TRESHOLD, 
+            target_sizes=target_sizes
+        )[0]
+
+    # annotate
+    detections = sv.Detections.from_transformers(transformers_results=results).with_nms(threshold=0.5)
+    print(detections[0])
+    labels = [f"{id2label[class_id]} {confidence:.2f}" for _, mask, confidence, class_id, _ in detections if class_id <= 4]
+    xys = [xyxy for xyxy, mask, confidence, class_id, _ in detections if class_id <= 4]
+    print(labels)
+    palette = ["orange", "blue", "green", "pink", "black"]
+    colors = [palette[label2id[_.split()[0]]] for _ in labels]
+    for i in range(len(labels)):
+        print(xys[i][0])
+        cv2.rectangle(image, (int(xys[i][0]), int(xys[i][1])), (int(xys[i][2]), int(xys[i][3])), (0, 255, 0), 2)
+        cv2.putText(image, labels[i], (int(xys[i][0]), int(xys[i][1])), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2, cv2.LINE_AA)
+    # frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels )
+    cv2.imwrite("static/output.jpg", cv2.cvtColor(image, cv2.COLOR_RGB2BGR))

Deployment/requirements.txt

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+aiohttp==3.8.4
+aiosignal==1.3.1
+async-timeout==4.0.2
+attrs==23.1.0
+blinker==1.6.2
+certifi==2023.5.7
+charset-normalizer==3.1.0
+click==8.1.3
+colorama==0.4.6
+contourpy==1.0.7
+cycler==0.11.0
+datasets==2.12.0
+dill==0.3.6
+filelock==3.12.0
+Flask==2.3.2
+fonttools==4.39.4
+frozenlist==1.3.3
+fsspec==2023.5.0
+huggingface-hub==0.15.1
+idna==3.4
+importlib-metadata==6.6.0
+importlib-resources==5.12.0
+itsdangerous==2.1.2
+Jinja2==3.1.2
+kiwisolver==1.4.4
+lightning-utilities==0.8.0
+MarkupSafe==2.1.3
+matplotlib==3.7.1
+mpmath==1.3.0
+multidict==6.0.4
+multiprocess==0.70.14
+networkx==3.1
+numpy==1.24.3
+opencv-python==4.7.0.72
+packaging==23.1
+pandas==2.0.2
+Pillow==9.5.0
+pyarrow==12.0.0
+pyparsing==3.0.9
+python-dateutil==2.8.2
+pytorch-lightning==2.0.3
+pytz==2023.3
+PyYAML==6.0
+regex==2023.6.3
+requests==2.31.0
+responses==0.18.0
+safetensors==0.3.1
+six==1.16.0
+supervision==0.9.0
+sympy==1.12
+timm==0.9.2
+tokenizers==0.13.3
+torch==2.0.1
+torchmetrics==0.11.4
+torchvision==0.15.2
+tqdm==4.65.0
+transformers==4.30.0
+typing_extensions==4.6.3
+tzdata==2023.3
+urllib3==2.0.3
+Werkzeug==2.3.5
+xxhash==3.2.0
+yarl==1.9.2
+zipp==3.15.0

Deployment/static/styles/style.css

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+body {
+    background-color: #f1f1f1;
+}
+.ourTeam-hedding p{
+    color: #979797;
+}
+.ourTeam-hedding strong{
+    color: black;
+}
+.ourTeam-hedding{
+    margin-bottom: 50px;
+}
+.ourTeam-hedding h1{
+    font-size: 25px;
+    font-weight: bold;
+    color: #145889;
+}
+.ourTeam-box{
+    border-radius: 2px;
+    border-top: 6px solid #5DDDD3;
+    margin: 0px;
+    background-color: #FFFFFF;
+    margin-bottom: 30px;
+}
+.section1{
+    padding: 30px 0px 30px 0px;
+}
+.section1 img{
+    border-radius: 50%;
+    height: 130px;
+    width: 130px;
+}
+.section2 p{
+    font-weight: bold;
+    color: #5DDDD3;
+    letter-spacing: 1px;
+}
+.section2 span{
+    color: #979597;
+}
+.section3{
+    background-color: #5DDDD3;
+}
+.section3 i{
+    color: #ffffff !important;
+    padding: 15px;
+    font-size: 15px;
+}
+.section-info{
+    border-top: 6px solid #90DFAA;
+}
+.section-info p{
+    color: #90DFAA;
+}
+.section-info .section3{
+    background-color: #90DFAA;
+}
+.section-danger{
+    border-top: 6px solid #FD8469;
+}
+.section-danger p{
+    color: #FD8469;
+}
+.section-danger .section3{
+    background-color: #FD8469;
+}
+
+