Merge pull request #272 from ramana2074/main

Object Detection

Deep_Learning/Object detection/Dataset/Readme.md

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+# Object Detection using YOLOv3
+
+## 🎯 Goal 
+The main goal of this project is to demonstrate the use of the YOLOv3 (You Only Look Once) object detection algorithm with OpenCV in Python to detect objects in images. The purpose of this project is to showcase the effectiveness of YOLOv3 in real-time object detection.
+
+## 🧵 Dataset 
+The dataset used in this project is the COCO (Common Objects in Context) dataset, which is a large-scale object detection dataset. The dataset can be downloaded from the official COCO website: https://cocodataset.org/#home.
+
+## 🧾 Description 
+This project uses the YOLOv3 algorithm to detect objects in images. The YOLOv3 model is pre-trained on the COCO dataset and is used to detect objects in real-time. The project uses OpenCV to load the YOLOv3 model and detect objects in images.
+
+## 🧮 What I had done! Here are the step-by-step procedures of how the project works:
+
+Loaded the YOLOv3 model using OpenCV.
+Loaded the image to be detected.
+Detected objects in the image using the YOLOv3 model.
+Displayed the image with bounding boxes and labels using matplotlib.
+🚀 Models Implemented The YOLOv3 algorithm is used in this project. YOLOv3 is a state-of-the-art, real-time object detection algorithm that is widely used in computer vision tasks. It is chosen for its high accuracy and speed.
+
+## 📚 Libraries Needed The following libraries are needed for this project:
+
+OpenCV
+NumPy
+Matplotlib
+
+## 📊 Exploratory Data Analysis Results 
+The output of the project is an image with detected objects highlighted by bounding boxes and class labels.
+
+![Output](./Images/Output.png)
+## 📈 Performance of the Models based on the Accuracy Scores 
+The accuracy of the YOLOv3 model is not explicitly calculated in this project. However, the model is known to have high accuracy in object detection tasks.
+
+## 📢 Conclusion 
+The YOLOv3 algorithm is effective in detecting objects in images. The project demonstrates the use of YOLOv3 with OpenCV in Python to detect objects in real-time.
+
+## ✒️ Your Signature
+ Name - Venkata Ramana Billana
+ Github - https://github.com/ramana2074

Deep_Learning/Object detection/Model/main.py

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+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load YOLO model
+# download YOLO models from offical webiste of yolo algorithm
+
+yolo = cv2.dnn.readNet("C:\\Users\\billa\\OneDrive\\Documents\\ABC\\Computer-Vision-Projects\\Object detection\\Dataset\\yolov3.weights", 
+                      "C:\\Users\\billa\\OneDrive\\Documents\\ABC\\Computer-Vision-Projects\\Object detection\\Dataset\\yolov3.cfg")
+
+# Load class names
+classes = []
+with open("C:\\Users\\billa\\OneDrive\\Documents\\ABC\\Computer-Vision-Projects\\Object detection\\Dataset\\coco.names", 'r') as f:
+    classes = f.read().splitlines()
+
+# Load image
+img = cv2.imread("C:\\Users\\billa\\OneDrive\\Desktop\\Programs\\ML_DL\\gg.png")
+if img is None:
+    print("Error loading image.")
+height, width = img.shape[:2]  # Get image height and width
+
+# Prepare the image for YOLO
+blob = cv2.dnn.blobFromImage(img, 1/255, (416, 416), (0, 0, 0), swapRB=True, crop=False)
+yolo.setInput(blob)
+
+# Get output layer names and run forward pass
+output_layers_names = yolo.getUnconnectedOutLayersNames()
+layer_output = yolo.forward(output_layers_names)
+
+# Initialize lists
+boxes = []
+confidences = []
+class_ids = []
+
+for output in layer_output:
+    for detection in output:
+        scores = detection[5:]
+        class_id = np.argmax(scores)
+        confidence = scores[class_id]
+        if confidence > 0.7: 
+            center_x = int(detection[0] * width)
+            center_y = int(detection[1] * height)
+            w = int(detection[2] * width)
+            h = int(detection[3] * height)
+
+            x = int(center_x - w / 2)
+            y = int(center_y - h / 2)
+
+            # Append detection information
+            boxes.append([x, y, w, h])
+            confidences.append(float(confidence))
+            class_ids.append(class_id)
+
+# Perform Non-Maximum Suppression
+indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
+
+# Font for displaying labels
+font = cv2.FONT_HERSHEY_PLAIN
+# Random colors for each box
+colors = np.random.randint(0, 255, size=(len(boxes), 3), dtype='uint8')
+
+# Check if any boxes are returned
+if len(indexes) > 0:
+    indexes = indexes.flatten()  # Flatten the list of indexes
+
+    # Draw bounding boxes and labels
+    for i in indexes:
+        x, y, w, h = boxes[i]
+        label = str(classes[class_ids[i]])
+        color = [int(c) for c in colors[i]]
+        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
+        cv2.putText(img, label, (x, y - 10), font, 2, (255, 255, 255), 2)
+
+# Display the image with matplotlib
+plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+plt.axis('off')  # Hide axis
+plt.show()

Deep_Learning/Object detection/requirements.txt

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+opencv-python==4.5.5.64
+numpy==1.21.4
+matplotlib==3.5.0