Added the code for object detection for images (#75)

Added a code for object detection for images, 2 test images, models folder containing the models.

Backend/object_detection.py

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+import cv2
+import numpy as np
+import tensorflow as tf
+import tensorflow_hub as hub
+
+# Load the SSD MobileNet V2 model from TensorFlow Hub
+detector = hub.load("https://www.kaggle.com/models/tensorflow/ssd-mobilenet-v2/TensorFlow2/fpnlite-320x320/1")
+
+# Mapping class IDs to class names for COCO dataset
+class_names = {
+    1: 'person',
+    2: 'bicycle',
+    3: 'car',
+    4: 'motorcycle',
+    5: 'airplane',
+    6: 'bus',
+    7: 'train',
+    8: 'truck',
+    9: 'boat',
+    10: 'traffic light',
+    11: 'fire hydrant',
+    12: 'stop sign',
+    13: 'parking meter',
+    14: 'bench',
+    15: 'bird',
+    16: 'cat',
+    17: 'dog',
+    18: 'horse',
+    19: 'sheep',
+    20: 'cow',
+    21: 'elephant',
+    22: 'bear',
+    23: 'zebra',
+    24: 'giraffe',
+    25: 'backpack',
+    26: 'umbrella',
+    27: 'handbag',
+    28: 'tie',
+    29: 'suitcase',
+    30: 'frisbee',
+    31: 'skis',
+    32: 'snowboard',
+    33: 'sports ball',
+    34: 'kite',
+    35: 'baseball bat',
+    36: 'baseball glove',
+    37: 'skateboard',
+    38: 'surfboard',
+    39: 'tennis racket',
+    40: 'bottle',
+    41: 'wine glass',
+    42: 'cup',
+    43: 'fork',
+    44: 'knife',
+    45: 'spoon',
+    46: 'bowl',
+    47: 'banana',
+    48: 'apple',
+    49: 'sandwich',
+    50: 'orange',
+    51: 'broccoli',
+    52: 'carrot',
+    53: 'hot dog',
+    54: 'pizza',
+    55: 'donut',
+    56: 'cake',
+    57: 'chair',
+    58: 'couch',
+    59: 'potted plant',
+    60: 'bed',
+    61: 'dining table',
+    62: 'toilet',
+    63: 'TV',
+    64: 'laptop',
+    65: 'mouse',
+    66: 'remote',
+    67: 'keyboard',
+    68: 'cell phone',
+    69: 'microwave',
+    70: 'oven',
+    71: 'toaster',
+    72: 'sink',
+    73: 'refrigerator',
+    74: 'book',
+    75: 'clock',
+    76: 'vase',
+    77: 'scissors',
+    78: 'teddy bear',
+    79: 'hair drier',
+    80: 'toothbrush'
+}
+
+# Function to perform object detection
+def detect_objects(image):
+    # Convert the image to a tensor and resize it
+    image_resized = cv2.resize(image, (320, 320))  # Resize to model input size
+    input_tensor = tf.convert_to_tensor(image_resized)
+    input_tensor = input_tensor[tf.newaxis, ...]  # Add batch dimension
+
+    # Perform inference
+    detector_output = detector(input_tensor)
+
+    return detector_output
+
+def load_image(image_path):
+    # Load an image from a file path
+    image = cv2.imread(image_path)
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+def draw_boxes(image, boxes, class_ids, scores, threshold=0.5):
+    height, width, _ = image.shape
+    found_items = []  # List to store found items
+
+    for i in range(len(scores)):
+        if scores[i] > threshold:
+            box = boxes[i]
+            (ymin, xmin, ymax, xmax) = box
+            # Scale bounding box to original image dimensions
+            xmin = int(xmin * width)
+            xmax = int(xmax * width)
+            ymin = int(ymin * height)
+            ymax = int(ymax * height)
+
+            # Get class name from class ID
+            class_name = class_names.get(class_ids[i], 'Unknown')
+
+            # Print class name and confidence score
+            print(f'Detected Class: {class_name}, Confidence: {scores[i] * 100:.2f}%')
+
+            # Check if the detected object is a laptop or a cell phone
+            if class_ids[i] == 64:  # Class ID for 'laptop'
+                found_items.append('Laptop')
+            elif class_ids[i] == 67:  # Class ID for 'cell phone'
+                found_items.append('Cell Phone')
+
+            # Draw bounding box
+            cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (255, 0, 0), 2)
+            cv2.putText(image, f'{class_name} ({scores[i]:.2f})', 
+                        (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+
+    # Print found items
+    if found_items:
+        print("Found:", ", ".join(set(found_items)))  # Print unique found items
+    else:
+        print("None")
+
+    return image
+
+# Main function to execute the detection
+def main(image_path):
+    image = load_image(image_path)
+    detector_output = detect_objects(image)
+
+    # Extract data from detector output
+    boxes = detector_output["detection_boxes"].numpy()[0]  # Bounding boxes
+    class_ids = detector_output["detection_classes"].numpy()[0].astype(int)  # Class IDs
+    scores = detector_output["detection_scores"].numpy()[0]  # Confidence scores
+
+    # Draw bounding boxes on the image
+    image_with_boxes = draw_boxes(image, boxes, class_ids, scores)
+
+    # Display the image
+    cv2.imshow('Object Detection', cv2.cvtColor(image_with_boxes, cv2.COLOR_RGB2BGR))
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+# Call the main function with the path to your image
+image_path = "test-image2.jpg"  # Change this to your image path
+main(image_path)