Create app.py

app.py

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+from flask import Flask, render_template, request, redirect, url_for, jsonify
+import cv2
+import numpy as np
+from tensorflow.lite.python.interpreter import Interpreter
+import os
+
+# Define paths to your model and label files
+MODEL_PATH = "custom_model_lite/detect.tflite"
+LABEL_PATH = "custom_model_lite/labelmap.txt"
+
+# Function to load the TFLite model and labels
+def load_model():
+    interpreter = Interpreter(model_path=MODEL_PATH)
+    interpreter.allocate_tensors()
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+    height = input_details[0]['shape'][1]
+    width = input_details[0]['shape'][2]
+
+    with open(LABEL_PATH, 'r') as f:
+        labels = [line.strip() for line in f.readlines()]
+
+    print(f"Model loaded. Input shape: {input_details[0]['shape']}")
+    return interpreter, input_details, output_details, height, width, labels
+
+# Function to preprocess the image for the model
+def preprocess_image(image, input_details, height, width):
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image_resized = cv2.resize(image_rgb, (width, height))
+    input_data = np.expand_dims(image_resized, axis=0)
+
+    if input_details[0]['dtype'] == np.float32:
+        input_data = (np.float32(input_data) - 127.5) / 127.5
+
+    print(f"Image preprocessed: shape {input_data.shape}, dtype {input_data.dtype}")
+    return input_data
+
+# Function to perform object detection and draw bounding boxes
+def detect_objects(image, interpreter, input_details, output_details, labels):
+    input_data = preprocess_image(image, input_details, height, width)
+    interpreter.set_tensor(input_details[0]['index'], input_data)
+    interpreter.invoke()
+
+    boxes = interpreter.get_tensor(output_details[1]['index'])[0]  # bounding box coordinates
+    classes = interpreter.get_tensor(output_details[3]['index'])[0]  # class index
+    scores = interpreter.get_tensor(output_details[0]['index'])[0]  # confidence scores
+
+    print(f"Detections: {len(scores)} objects detected")
+
+    for i in range(len(scores)):
+        if scores[i] > 0.1:  # confidence threshold
+            ymin, xmin, ymax, xmax = boxes[i]
+            ymin = int(max(1, ymin * image.shape[0]))
+            xmin = int(max(1, xmin * image.shape[1]))
+            ymax = int(min(image.shape[0], ymax * image.shape[0]))
+            xmax = int(min(image.shape[1], xmax * image.shape[1]))
+            cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
+            label = f'{labels[int(classes[i])]}: {scores[i] * 100:.2f}%'
+            cv2.putText(image, label, (xmin, ymin - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
+            print(f"Object {i}: {label} at [{xmin}, {ymin}, {xmax}, {ymax}]")
+
+    return image
+
+# Initialize the Flask app
+app = Flask(__name__, static_folder='static')
+
+# Load the TFLite model and labels
+interpreter, input_details, output_details, height, width, labels = load_model()
+
+@app.route('/', methods=['GET', 'POST'])
+def upload_and_detect():
+    if request.method == 'POST':
+        if 'file' not in request.files:
+            print("No file part in the request")
+            return redirect(request.url)
+        file = request.files['file']
+        if file.filename == '':
+            print("No selected file")
+            return redirect(request.url)
+
+        # Read the image file
+        image = cv2.imdecode(np.frombuffer(file.read(), np.uint8), cv2.IMREAD_COLOR)
+        if image is None:
+            print("Failed to read image")
+            return redirect(request.url)
+        
+        print(f"Image uploaded: {file.filename}, shape: {image.shape}")
+
+        # Perform object detection
+        processed_image = detect_objects(image, interpreter, input_details, output_details, labels)
+
+        # Ensure the static directory exists
+        if not os.path.exists(app.static_folder):
+            os.makedirs(app.static_folder)
+
+        # Save processed image
+        save_path = os.path.join(app.static_folder, 'detected.jpg')
+        cv2.imwrite(save_path, processed_image)
+        print(f"Processed image saved at: {save_path}")
+
+        # Send back the path to the processed image
+        return jsonify({'image_url': url_for('static', filename='detected.jpg')})
+
+    return render_template('upload.html')
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=8000)