Akshath Mangudi - Object Detection

Object-Detection/README.md

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+# Object Detection
+
+The following python script 'object_detection.py' uses a library called OpenCV, which is used in the field of computer vision. The following script runs an object detection algorithm using a haar cascade classifier algorithm. When playing around, please change the path of the haar cascade to your desirable output. 
+
+The output of the script will draw bounding boxes around objects and classify them. The script uses the live webcam as its input.
+
+When running the program in the terminal, please add the correct path to the Caffe and deploy file.
+
+The requirements are in the requirements.txt file. 
+
+I hope you enjoy this script! 
+
+Hacktoberfest 2023

Object-Detection/object_detection.py

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+import numpy as np
+import argparse
+import imutils
+import time
+import cv2
+from imutils.video import VideoStream
+from imutils.video import FPS
+
+argParse = argparse.ArgumentParser()
+argParse.add_argument("-p", "--prototxt", required=True,
+                      help="path to Caffe's deploy.prototxt file")
+argParse.add_argument("-m", "--model", required=True,
+                      help="path to the pretrained Caffe model")
+argParse.add_argument("-c", "--confidence", type=float, default=0.3,
+                      help="Filtering for detections with weak confidence intervals")
+args = vars(argParse.parse_args())
+
+features = ["background", "aeroplane", "bicycle", "bird", "boat",
+            "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
+            "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
+            "sofa", "train", "tvmonitor"]
+
+colors = np.random.uniform(0, 255, size=(len(features), 3))
+
+read_net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
+video_stream = VideoStream(src=0).start()
+time.sleep(2.0)
+frames_per_sec = FPS().start()
+
+while True:
+    frame = video_stream.read()
+    frame = imutils.resize(frame, width=600)
+    (h, w) = frame.shape[:2]
+    blob_from_img = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
+                                          0.007843, (300, 300), 127.5, swapRB=True)
+    read_net.setInput(blob_from_img)
+    detect = read_net.forward()
+
+    for i in np.arange(0, detect.shape[2]):
+        c_int = detect[0, 0, i, 2]
+        if c_int > args["confidence"]:
+            c_index = int(detect[0, 0, i, 1])
+            box = detect[0, 0, i, 3:7] * np.array([w, h, w, h])
+            (startx, starty, endx, endy) = box.astype("int")
+            label = "{}: {:.2f}%".format(features[c_index], c_int * 100)
+            print("Object detected: ", label)
+            cv2.rectangle(frame, (startx, starty), (endx, endy), colors[c_index], 2)
+
+            if starty - 15 > 15:
+                y = starty - 15
+            else:
+                y = starty + 15
+            cv2.putText(frame, label, (startx, y), cv2.FONT_HERSHEY_PLAIN, 0.4, colors[c_index], 2)
+
+    cv2.imshow("Frame", frame)
+
+    key = cv2.waitKey(1)
+    if key == ord("w"):
+        break
+
+    frames_per_sec.update()
+
+frames_per_sec.stop()
+
+print("Time elapsed: {:.2f}".format(frames_per_sec.elapsed()))
+print("FPS: {:.2f}".format(frames_per_sec.fps()))
+
+cv2.destroyAllWindows()
+video_stream.stop()

Object-Detection/requirements.txt

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+opencv-python
+opencv-contrib-python
+imutils
+matplotlib
+numpy