Commit by SK

Data/coco.txt

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+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+pottedplant
+bed
+diningtable
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush

YOLOV3-SOFTWARE.py

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+import numpy as np
+import cv2
+import datetime
+
+image_path = '/Users/skmirajulislam/Documents/Python/ML/ObjectDection/img/car-road.jpg'
+yolov3_weights = '/Users/skmirajulislam/Documents/Python/ML/ObjectDection/Data/yolov3.weights'
+yolov3_cfg = '/Users/skmirajulislam/Documents/Python/ML/ObjectDection/Data/yolov3.cfg'
+min_confidence = 0.2  # Adjust this threshold as needed
+
+net = cv2.dnn.readNet(yolov3_weights, yolov3_cfg)
+np.random.seed(543210)
+
+classes = []
+with open('/Users/skmirajulislam/Documents/Python/ML/ObjectDection/Data/coco.txt', 'r') as f:
+    classes = f.read().splitlines()
+colors = np.random.uniform(0, 255, size=(len(classes), 3))
+
+# image = cv2.imread(image_path)
+cap = cv2.VideoCapture(0)
+if not cap.isOpened():
+    print("Error occurred in video stream or file")
+
+# List for database with time
+Mongo = []
+# Adjust this value to control the bounding box reduction (e.g., 0.8 for 80% reduction)
+bbox_reduction_factor = 0.8
+
+# Create a named window with a larger size
+cv2.namedWindow("Detected Objects", cv2.WINDOW_NORMAL)
+cv2.resizeWindow("Detected Objects", 800, 600)  # Set your desired width and height
+
+while cap.isOpened():
+    ret, image = cap.read()
+    if ret:
+        height, width = image.shape[0], image.shape[1]
+        blob = cv2.dnn.blobFromImage(image, 1/255, (416, 416), (0, 0, 0), swapRB=True, crop=False)
+
+        net.setInput(blob)
+        output_layer_names = net.getUnconnectedOutLayersNames()
+        detected_objects = net.forward(output_layer_names)
+
+        boxes = []  # To store bounding box information
+
+        for detection in detected_objects:
+            for obj in detection:
+                scores = obj[5:]
+                class_id = np.argmax(scores)
+                confidence = scores[class_id]
+
+                if confidence > min_confidence:
+                    center_x = int(obj[0] * width)
+                    center_y = int(obj[1] * height)
+                    w = int(obj[2] * width)
+                    h = int(obj[3] * height)
+
+                    # Reduce the bounding box dimensions
+                    w *= bbox_reduction_factor
+                    h *= bbox_reduction_factor
+
+                    upper_left_x = int(center_x - w / 2)
+                    upper_left_y = int(center_y - h / 2)
+                    lower_right_x = int(center_x + w / 2)
+                    lower_right_y = int(center_y + h / 2)
+
+                    boxes.append([upper_left_x, upper_left_y, lower_right_x, lower_right_y, confidence, class_id])
+
+        # Apply Non-Maximum Suppression (NMS)
+        if len(boxes) > 0:
+            boxes = np.array(boxes)
+            confidences = boxes[:, 4].astype(float)
+            class_ids = boxes[:, 5].astype(int)
+
+            indices = cv2.dnn.NMSBoxes(boxes[:, :4], confidences, min_confidence, 0.4)
+
+            for i in indices:
+                box = boxes[i]  # Use i directly
+                x1, y1, x2, y2, confidence, class_id = box
+                class_id = int(class_id)  # Ensure class_id is an integer
+
+                # Ensure the coordinates are integers and within image boundaries
+                x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+                x1 = max(0, x1)
+                y1 = max(0, y1)
+                x2 = min(width - 1, x2)
+                y2 = min(height - 1, y2)
+
+                prediction_text = f"{classes[class_id]}: {confidence:.2f}%"
+                cv2.rectangle(image, (x1, y1), (x2, y2), colors[class_id], 3)
+                cv2.putText(image, prediction_text, (x1, y1 - 15 if y1 > 30 else y1 + 15), cv2.FONT_HERSHEY_SIMPLEX, 0.6, colors[class_id], 2)
+
+                Mongo.append(classes[class_id])
+
+        cv2.imshow("Detected Objects", image)
+
+        if cv2.waitKey(25) & 0xFF == ord('q'):
+            print("Detected items are:")
+            now = datetime.datetime.now()
+            Curr_time = now.strftime("%B/%d/%Y %H:%M:%S")
+            data_db_set = set(Mongo)
+            data_db_list = list(data_db_set)
+            data_db_list.insert(0, Curr_time)
+            print(data_db_list)
+            break
+    else:
+        break
+
+cap.release()
+cv2.destroyAllWindows()

YOLOV3-TESTING.py

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+import numpy as np
+import cv2
+import datetime
+
+image_path = '/Users/skmirajulislam/Documents/MyPython/ML/ObjectDection/img/car-road.jpg'
+yolov3_weights = '/Users/skmirajulislam/Documents/Python/ML/ObjectDection/Data/yolov3.weights'
+yolov3_cfg = '/Users/skmirajulislam/Documents/Python/ML/ObjectDection/Data/yolov3.cfg'
+min_confidence = 0.2  # Adjust this threshold as needed
+
+net = cv2.dnn.readNet(yolov3_weights, yolov3_cfg)
+np.random.seed(543210)
+
+classes = []
+with open('/Users/skmirajulislam/Documents/Python/ML/ObjectDection/Data/coco.txt', 'r') as f:
+    classes = f.read().splitlines()
+colors = np.random.uniform(0, 255, size=(len(classes), 3))
+
+# image = cv2.imread(image_path)
+cap = cv2.VideoCapture(0)
+if not cap.isOpened():
+    print("Error occurred in video stream or file")
+
+# List for database with time
+Mongo = []
+# Adjust this value to control the bounding box reduction (e.g., 0.8 for 80% reduction)
+bbox_reduction_factor = 0.8
+
+while cap.isOpened():
+    ret, image = cap.read()
+    if ret:
+        height, width = image.shape[0], image.shape[1]
+        blob = cv2.dnn.blobFromImage(image, 1/255, (416, 416), (0, 0, 0), swapRB=True, crop=False)
+
+        net.setInput(blob)
+        output_layer_names = net.getUnconnectedOutLayersNames()
+        detected_objects = net.forward(output_layer_names)
+
+        boxes = []  # To store bounding box information
+
+        for detection in detected_objects:
+            for obj in detection:
+                scores = obj[5:]
+                class_id = np.argmax(scores)
+                confidence = scores[class_id]
+
+                if confidence > min_confidence:
+                    center_x = int(obj[0] * width)
+                    center_y = int(obj[1] * height)
+                    w = int(obj[2] * width)
+                    h = int(obj[3] * height)
+
+                    # Reduce the bounding box dimensions
+                    w *= bbox_reduction_factor
+                    h *= bbox_reduction_factor
+
+                    upper_left_x = int(center_x - w / 2)
+                    upper_left_y = int(center_y - h / 2)
+                    lower_right_x = int(center_x + w / 2)
+                    lower_right_y = int(center_y + h / 2)
+
+                    boxes.append([upper_left_x, upper_left_y, lower_right_x, lower_right_y, confidence, class_id])
+
+        # Apply Non-Maximum Suppression (NMS)
+        if len(boxes) > 0:
+            boxes = np.array(boxes)
+            confidences = boxes[:, 4].astype(float)
+            class_ids = boxes[:, 5].astype(int)
+
+            indices = cv2.dnn.NMSBoxes(boxes[:, :4], confidences, min_confidence, 0.4)
+
+            for i in indices:
+                box = boxes[i]  # Use i directly
+                x1, y1, x2, y2, confidence, class_id = box
+                class_id = int(class_id)  # Ensure class_id is an integer
+
+                # Ensure the coordinates are integers and within image boundaries
+                x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+                x1 = max(0, x1)
+                y1 = max(0, y1)
+                x2 = min(width - 1, x2)
+                y2 = min(height - 1, y2)
+
+                prediction_text = f"{classes[class_id]}: {confidence:.2f}%"
+                cv2.rectangle(image, (x1, y1), (x2, y2), colors[class_id], 3)
+                cv2.putText(image, prediction_text, (x1, y1 - 15 if y1 > 30 else y1 + 15), cv2.FONT_HERSHEY_SIMPLEX, 0.6, colors[class_id], 2)
+
+                Mongo.append(classes[class_id])
+
+        cv2.imshow("Detected Objects", image)
+
+        if cv2.waitKey(25) & 0xFF == ord('q'):
+            print("Detected items are:")
+            now = datetime.datetime.now()
+            Curr_time = now.strftime("%B/%d/%Y %H:%M:%S")
+            data_db_set = set(Mongo)
+            data_db_list = list(data_db_set)
+            data_db_list.insert(0, Curr_time)
+            print(data_db_list)
+            break
+    else:
+        break
+
+cap.release()
+cv2.destroyAllWindows()