main file

Author: AlirezaPlusOfficial

main.py

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+import pyttsx3
+import cv2
+import numpy as np
+
+# TTS
+engine = pyttsx3.init()
+
+# TTS Functionality
+def speak(text):
+    engine.say(text)
+    engine.runAndWait()
+
+# Load your model here, u can use yolov3 v9 v8 v7 doesn't matter, in my case, I used v3!
+net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
+layer_names = net.getLayerNames()  # Get the layer names
+output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]  # output layers
+
+# Classnames for our model ( in this case YOLOv3 ofc )
+with open("coco.names", "r") as f:
+    classes = [line.strip() for line in f.readlines()]
+
+# Initialize the webcam, in my opinion my webcam device is 1
+# you can check it out in device manager to see how many webcams you've got.
+# example : VideoCapture(0) - VideoCapture(3) - etc...
+cap = cv2.VideoCapture(1)
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        print("Failed to grab frame")
+        break
+
+    height, width, channels = frame.shape
+
+    blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
+    net.setInput(blob)
+    outs = net.forward(output_layers)
+
+    class_ids = []
+    confidences = []
+    boxes = []
+
+    for out in outs:
+        for detection in out:
+            scores = detection[5:]
+            class_id = np.argmax(scores)
+            confidence = scores[class_id]
+            if confidence > 0.5:  # Confidence threshold (You can increase it if you want), by confidence you can tell how much u sure about ur object detection guess, something like that...
+                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)
+
+                boxes.append([x, y, w, h])
+                confidences.append(float(confidence))
+                class_ids.append(class_id)
+
+    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
+
+    if len(indexes) > 0:
+        for i in indexes.flatten():
+            x, y, w, h = boxes[i]
+            label = str(classes[class_ids[i]])
+            confidence = str(round(confidences[i], 2))
+
+            # Drawing frames around detected objects
+            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+            cv2.putText(frame, label + " " + confidence, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+
+            # Calling tts to tell me what Computer watching (like before, u can delete this if u want)
+            speak(f"You are showing me {label}")
+
+    # Window name
+    cv2.imshow("Object Detection by AlirezaPlus", frame)
+
+    # Break on 'q' key ( to quit the instance just press Q (WAITKEY 1 stands for 1 ms thats mean u have to hold ur Q for 1ms which is instant is this case))
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()