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import os
import cv2
import numpy as np
import base64
from flask import Flask, render_template_string, request, redirect, flash
import roboflow
import torch
from collections import Counter
app = Flask(__name__)
app.secret_key = 'your_secret_key' # Replace with a secure secret key
#########################################
# 1. Initialize the Models
#########################################
# --- Roboflow Box Detection Model ---
API_KEY = "wLjPoPYaLmrqCIOFA0RH" # Replace with your actual API key
PROJECT_ID = "base-model-box-r4suo-8lkk1-6dbqh" # Replace with your Roboflow project ID
VERSION_NUMBER = "2" # Replace with your trained model version number
rf = roboflow.Roboflow(api_key=API_KEY)
workspace = rf.workspace()
project = workspace.project(PROJECT_ID)
version = project.version(VERSION_NUMBER)
box_model = version.model # This model is trained for detecting boxes
# --- YOLOv5 Pretrained Model for Persons & Cars ---
# Using Ultralytics YOLOv5s (pretrained) from Torch Hub
yolov5_model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
# We'll filter YOLO detections to only include persons and cars.
YOLO_FILTER_CLASSES = {"person", "car"}
#########################################
# 2. Helper Functions
#########################################
def compute_iou(boxA, boxB):
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
interWidth = max(0, xB - xA)
interHeight = max(0, yB - yA)
interArea = interWidth * interHeight
boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
if boxAArea + boxBArea - interArea == 0:
return 0
return interArea / float(boxAArea + boxBArea - interArea)
def custom_nms(preds, iou_threshold=0.3):
preds = sorted(preds, key=lambda x: x["confidence"], reverse=True)
filtered_preds = []
for pred in preds:
keep = True
for kept in filtered_preds:
if compute_iou(pred["box"], kept["box"]) > iou_threshold:
keep = False
break
if keep:
filtered_preds.append(pred)
return filtered_preds
def process_image(image_path):
"""
Process the uploaded image using both detection pipelines:
(a) Box detection via Roboflow (with measurement using an ArUco marker).
(b) YOLOv5 detection for persons and cars.
Returns the annotated image and a list of detection info dictionaries.
"""
image = cv2.imread(image_path)
if image is None:
return None, "Could not read the image."
img_height, img_width = image.shape[:2]
detection_info = [] # List to hold all detection results for display
# --- (a) Roboflow Box Detection & Measurement ---
results = box_model.predict(image_path, confidence=50, overlap=30).json()
predictions = results.get("predictions", [])
processed_preds = []
for prediction in predictions:
x, y, width, height = prediction["x"], prediction["y"], prediction["width"], prediction["height"]
x1 = int(round(x - width / 2))
y1 = int(round(y - height / 2))
x2 = int(round(x + width / 2))
y2 = int(round(y + height / 2))
# Clamp coordinates to image dimensions
x1 = max(0, min(x1, img_width - 1))
y1 = max(0, min(y1, img_height - 1))
x2 = max(0, min(x2, img_width - 1))
y2 = max(0, min(y2, img_height - 1))
processed_preds.append({
"box": (x1, y1, x2, y2),
"class": prediction["class"],
"confidence": prediction["confidence"]
})
box_detections = custom_nms(processed_preds, iou_threshold=0.3)
# Detect ArUco marker for measurement (only applicable for boxes)
marker_real_width_cm = 10.0 # The marker is 10cm x 10cm
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
aruco_dict = cv2.aruco.getPredefinedDictionary(cv2.aruco.DICT_6X6_250)
aruco_params = cv2.aruco.DetectorParameters()
corners, ids, _ = cv2.aruco.detectMarkers(gray, aruco_dict, parameters=aruco_params)
if ids is not None and len(corners) > 0:
marker_corners = corners[0].reshape((4, 2))
cv2.aruco.drawDetectedMarkers(image, corners, ids)
marker_width_pixels = np.linalg.norm(marker_corners[0] - marker_corners[1])
marker_height_pixels = np.linalg.norm(marker_corners[1] - marker_corners[2])
marker_pixel_size = (marker_width_pixels + marker_height_pixels) / 2.0
conversion_factor = marker_real_width_cm / marker_pixel_size
else:
conversion_factor = None
# Draw box detections and record measurement info (only for boxes)
for pred in box_detections:
x1, y1, x2, y2 = pred["box"]
label = pred["class"]
confidence = pred["confidence"]
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
if conversion_factor is not None:
box_width_pixels = x2 - x1
box_height_pixels = y2 - y1
box_width_cm = box_width_pixels * conversion_factor
box_height_cm = box_height_pixels * conversion_factor
size_text = f"{box_width_cm:.1f}x{box_height_cm:.1f} cm"
detection_info.append({
"class": label,
"confidence": f"{confidence:.2f}",
"width_cm": f"{box_width_cm:.1f}",
"height_cm": f"{box_height_cm:.1f}"
})
else:
size_text = ""
detection_info.append({
"class": label,
"confidence": f"{confidence:.2f}",
"width_cm": "N/A",
"height_cm": "N/A"
})
text = f"{label} ({confidence:.2f}) {size_text}"
(text_width, text_height), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
cv2.rectangle(image, (x1, y1 - text_height - baseline - 5), (x1 + text_width, y1 - 5), (0, 255, 0), -1)
cv2.putText(image, text, (x1, y1 - 5 - baseline), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
# --- (b) YOLOv5 for Persons & Cars ---
# Convert image to RGB for YOLO (it expects RGB)
img_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
yolo_results = yolov5_model(img_rgb)
df = yolo_results.pandas().xyxy[0]
for _, row in df.iterrows():
if row['name'] in YOLO_FILTER_CLASSES:
xmin = int(row['xmin'])
ymin = int(row['ymin'])
xmax = int(row['xmax'])
ymax = int(row['ymax'])
conf = row['confidence']
label = row['name']
cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (255, 0, 0), 2)
text = f"{label} ({conf:.2f})"
(text_width, text_height), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
cv2.rectangle(image, (xmin, ymin - text_height - baseline - 5), (xmin + text_width, ymin - 5), (255, 0, 0), -1)
cv2.putText(image, text, (xmin, ymin - 5 - baseline), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
detection_info.append({
"class": label,
"confidence": f"{conf:.2f}",
"width_cm": "N/A",
"height_cm": "N/A"
})
# --- Build Top Summary Text ---
detection_counts = Counter(det["class"] for det in detection_info)
if detection_counts:
top_text = ", ".join(f"{cls}: {count}" for cls, count in detection_counts.items())
(info_width, info_height), info_baseline = cv2.getTextSize(top_text, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
cv2.rectangle(image, (5, 5), (5 + info_width, 5 + info_height + info_baseline), (0, 255, 0), -1)
cv2.putText(image, top_text, (5, 5 + info_height), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
return image, detection_info
#########################################
# 3. Flask Routes
#########################################
@app.route('/', methods=['GET', 'POST'])
def index():
image_data = None
detection_info = None
if request.method == 'POST':
if 'file' not in request.files:
flash('No file part')
return redirect(request.url)
file = request.files['file']
if file.filename == '':
flash('No selected file')
return redirect(request.url)
upload_path = "uploaded.jpg"
file.save(upload_path)
processed_image, detection_info = process_image(upload_path)
if processed_image is None:
flash("Error processing image.")
else:
retval, buffer = cv2.imencode('.jpg', processed_image)
image_data = base64.b64encode(buffer).decode('utf-8')
os.remove(upload_path)
return render_template_string('''
<!doctype html>
<html>
<head>
<title>Multi-Detection & Measurement</title>
<!-- Bootstrap CSS -->
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css">
<style>
body {
background-color: #f8f9fa;
font-family: "Segoe UI", Tahoma, Geneva, Verdana, sans-serif;
}
.container {
margin-top: 30px;
}
.header {
text-align: center;
margin-bottom: 30px;
}
.card {
margin-bottom: 30px;
}
.result-img {
width: 100%;
border: 1px solid #ddd;
padding: 5px;
}
.table-responsive {
margin-top: 20px;
}
.footer {
text-align: center;
font-size: 0.9em;
color: #777;
margin-top: 30px;
}
</style>
</head>
<body>
<div class="container">
<h1 class="header">Multi-Detection & Measurement</h1>
<!-- Upload Form -->
<div class="card">
<div class="card-body">
<form method="post" enctype="multipart/form-data">
<div class="form-group">
<label for="file">Choose an image to upload:</label>
<input type="file" class="form-control-file" name="file" accept="image/*" id="file">
</div>
<button type="submit" class="btn btn-primary">Upload</button>
</form>
{% with messages = get_flashed_messages() %}
{% if messages %}
<div class="alert alert-danger mt-3">
<ul>
{% for message in messages %}
<li>{{ message }}</li>
{% endfor %}
</ul>
</div>
{% endif %}
{% endwith %}
</div>
</div>
{% if image_data or detection_info %}
<div class="row">
<div class="col-md-8">
<div class="card">
<div class="card-header">
Processed Image
</div>
<div class="card-body">
<img src="data:image/jpeg;base64,{{ image_data }}" alt="Processed Image" class="result-img">
</div>
</div>
</div>
<div class="col-md-4">
<div class="card">
<div class="card-header">
Detection Results
</div>
<div class="card-body">
<p>Total Results: <strong>{{ detection_info|length }}</strong></p>
<div class="table-responsive">
<table class="table table-striped table-bordered">
<thead class="thead-dark">
<tr>
<th>#</th>
<th>Class</th>
<th>Confidence</th>
<th>Width (cm)</th>
<th>Height (cm)</th>
</tr>
</thead>
<tbody>
{% for det in detection_info %}
<tr>
<td>{{ loop.index }}</td>
<td>{{ det.class }}</td>
<td>{{ det.confidence }}</td>
<td>{{ det.width_cm }}</td>
<td>{{ det.height_cm }}</td>
</tr>
{% endfor %}
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
{% endif %}
<div class="footer">
<p>© 2023 Multi-Detection App. All rights reserved.</p>
</div>
</div>
<!-- Bootstrap JS and dependencies -->
<script src="https://code.jquery.com/jquery-3.5.1.slim.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/umd/popper.min.js"></script>
<script src="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/js/bootstrap.min.js"></script>
</body>
</html>
''', image_data=image_data, detection_info=detection_info)
#########################################
# Run the App
#########################################
if __name__ == '__main__':
app.run() |