object_detection

Author: nishant4500

OpenCV Projects/README.md

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+# Object Detection with OpenCV and SSD MobileNet v3
+## Description
+![Screenshot 2024-06-19 121308](https://github.com/nishant4500/object-detection/assets/135944619/44175bfa-25f2-40e5-8f36-297ff2517fba)
+![Screenshot 2024-06-19 121106](https://github.com/nishant4500/object-detection/assets/135944619/158f42c7-2c72-4c48-b0dc-9bc55faaf7e2)
+
+
+This Python script leverages OpenCV and a pre-trained SSD MobileNet v3 deep learning model for real-time object detection in video streams. It utilizes the COCO (Common Objects in Context) dataset for object class recognition.
+
+## Requirements
+
+- Python 3.x ([Download](https://www.python.org/downloads/))
+- OpenCV library: Install via `pip install opencv-python`
+- NumPy library: Install via `pip install numpy` (usually included with OpenCV)
+- `coco.names` file: Contains class labels for the COCO dataset (download from a reliable source)
+- `ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt` file: Model configuration file (download from a reliable source)
+- `frozen_inference_graph.pb` file: Model weights file (download from a reliable source)
+
+## Instructions
+
+1. **Download necessary files:**
+   - Obtain the `coco.names`, `ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt`, and `frozen_inference_graph.pb` files from a trusted source (ensure compatibility with your OpenCV version). Place them in the same directory as your Python script.
+
+2. **Run the script:**
+   - Open a terminal or command prompt, navigate to the directory containing your script and files.
+   - Execute the script using:
+     ```sh
+     python object_detection.py
+     ```
+
+## Code Breakdown
+
+1. **Imports:**
+   - `cv2`: Imports the OpenCV library for computer vision tasks.
+
+2. **Threshold and Video Capture:**
+   - `thres`: Sets the confidence threshold for object detection (adjust as needed).
+   - `cap`: Initializes a video capture object (`VideoCapture(1)`) to access your webcam (or a different video source by providing its index or path).
+   - `cap.set()`: Sets video capture properties:
+     - `3`: Width (adjust for desired resolution)
+     - `4`: Height (adjust for desired resolution)
+     - `10`: Brightness (adjust for lighting conditions)
+
+3. **Load Class Names:**
+   - `classNames`: Creates an empty list to store object class names.
+   - `classFile`: Path to the `coco.names` file.
+   - Loads class names from the file and splits them into a list.
+
+4. **Load Model Configuration and Weights:**
+   - `configPath`: Path to the `ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt` file.
+   - `weightsPath`: Path to the `frozen_inference_graph.pb` file.
+   - `net`: Creates a detection model object using `cv2.dnn_DetectionModel()`.
+   - Sets model input size, scale, mean, and color channel swapping parameters for compatibility with the model.
+
+5. **Main Loop:**
+   - `while True`: Continuously captures frames from the video stream.
+   - `success, img`: Reads a frame and checks for success. Exits if unsuccessful.
+   - `classIds`, `confs`, `bbox`: Performs object detection using the model on the current frame.
+     - `classIds`: List of detected object class IDs.
+     - `confs`: List of corresponding confidence scores (0-1).
+     - `bbox`: List of bounding boxes (coordinates) for detected objects.
+   - Prints detected object IDs and bounding boxes for debugging (optional).
+
+6. **Draw Bounding Boxes and Labels:**
+   - Checks if any objects were detected (`len(classIds) != 0`).
+   - Iterates through detected objects using `zip`:
+     - `box`: Current bounding box coordinates.
+     - `classId`: Current object class ID (minus 1 for indexing).
+     - `confidence`: Current object confidence score.
+   - Draws a green rectangle around the detected object using `cv2.rectangle()`.
+   - Displays the corresponding class name (uppercase) and confidence score (rounded to two decimal places) using `cv2.putText()`.
+
+7. **Display and Exit:**
+   - `cv2.imshow()`: Displays the processed frame with bounding boxes and labels in a window titled "Output".
+   - `cv2.waitKey(1)`: Waits for a key press for 1 millisecond.
+   - Exits the loop and releases resources if the 'q' key is pressed.
+
+8. **Cleanup:**
+   - Releases the video capture object and closes all OpenCV windows.
+     ```python
+     cap.release()
+     cv2.destroyAllWindows()
+     ```
+
+##  License
+
+This project is licensed under the MIT License.
+thannk 
+
+

OpenCV Projects/coco.names

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+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+street sign
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+hat
+backpack
+umbrella
+shoe
+eye glasses
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+plate
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+couch
+potted plant
+bed
+mirror
+dining table
+window
+desk
+toilet
+door
+tv
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+blender
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
+hair brush

OpenCV Projects/code.py

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+import cv2
+
+# Threshold to detect object
+thres = 0.45
+
+# Initialize the video capture
+cap = cv2.VideoCapture(0)
+cap.set(3, 1280)  # Set the width
+cap.set(4, 720)  # Set the height
+cap.set(10, 70)  # Set the brightness
+
+# Load class names from the coco.names file
+classNames = []
+classFile = 'coco.names'
+with open(classFile, 'rt') as f:
+    classNames = f.read().rstrip('\n').split('\n')
+
+# Load model configuration and weights
+configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
+weightsPath = 'frozen_inference_graph.pb'
+
+# Create a detection model
+net = cv2.dnn_DetectionModel(weightsPath, configPath)
+net.setInputSize(320, 320)
+net.setInputScale(1.0 / 127.5)
+net.setInputMean((127.5, 127.5, 127.5))
+net.setInputSwapRB(True)
+
+while True:
+    success, img = cap.read()
+    if not success:
+        break
+
+    classIds, confs, bbox = net.detect(img, confThreshold=thres)
+    print(classIds, bbox)
+
+    if len(classIds) != 0:
+        for classId, confidence, box in zip(classIds.flatten(), confs.flatten(), bbox):
+            cv2.rectangle(img, box, color=(0, 255, 0), thickness=2)
+            cv2.putText(img, classNames[classId - 1].upper(), (box[0] + 10, box[1] + 30),
+                        cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
+            cv2.putText(img, str(round(confidence * 100, 2)), (box[0] + 200, box[1] + 30),
+                        cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
+
+    cv2.imshow("Output", img)
+    if cv2.waitKey(1) & 0xFF == ord('q'):  # Press 'q' to quit
+        break
+
+cap.release()
+cv2.destroyAllWindows()