Add new file

Obj_main.py

+import cv2
+import time
+import argparse
+import numpy as np
+import array
+
+arr1 = list()
+
+
+def yolov3(yolo_weights, yolo_cfg, coco_names):
+    net = cv2.dnn.readNet(yolo_weights, yolo_cfg)
+    classes = open(coco_names).read().strip().split("\n")
+    layer_names = net.getLayerNames()
+    output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
+
+    return net, classes, output_layers
+
+
+def perform_detection(net, img, output_layers, w, h, confidence_threshold):
+    blob = cv2.dnn.blobFromImage(img, 1 / 255., (224, 224), swapRB=True, crop=True)
+    net.setInput(blob)
+    layer_outputs = net.forward(output_layers)
+
+    boxes = []
+    confidences = []
+    class_ids = []
+
+    for output in layer_outputs:
+        for detection in output:
+            scores = detection[5:]
+            class_id = np.argmax(scores)
+            confidence = scores[class_id]
+
+            # Object is deemed to be detected
+            if confidence > confidence_threshold:
+                # center_x, center_y, width, height = (detection[0:4] * np.array([w, h, w, h])).astype('int')
+                center_x, center_y, width, height = list(map(int, detection[0:4] * [w, h, w, h]))
+                # print(center_x, center_y, width, height)
+
+                top_left_x = int(center_x - (width / 2))
+                top_left_y = int(center_y - (height / 2))
+
+                boxes.append([top_left_x, top_left_y, width, height])
+                confidences.append(float(confidence))
+                class_ids.append(class_id)
+
+    return boxes, confidences, class_ids
+
+
+def draw_boxes(boxes, confidences, class_ids, classes, img, colors, confidence_threshold, NMS_threshold):
+    indexes = cv2.dnn.NMSBoxes(boxes, confidences, confidence_threshold, NMS_threshold)
+
+    FONT = cv2.FONT_HERSHEY_SIMPLEX
+
+    if len(indexes) > 0:
+        for i in indexes.flatten():
+            x, y, w, h = boxes[i]
+            # print(len(colors[class_ids[i]]))
+            color = colors[i]
+            cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
+            # text = f"{class_ids[i]} -- {confidences[i]}"
+            text = "{}: {:.4f}".format(classes[class_ids[i]], confidences[i])
+            cv2.putText(img, text, (x, y - 5), FONT, 0.5, color, 2)
+            v = (classes[class_ids[i]])
+            #print('Detetcted : ', (classes[class_ids[i]]))
+            if v not in arr1:
+                arr1.append(v)
+
+    cv2.imshow("Detection", img)
+    print ('detected Items are : ', arr1)
+    return arr1
+
+def dectection_video_file(webcam, video_path, yolo_weights, yolo_cfg, coco_names, confidence_threshold, nms_threshold):
+    net, classes, output_layers = yolov3(yolo_weights, yolo_cfg, coco_names)
+    colors = np.random.uniform(0, 255, size=(len(classes), 3))
+
+    if webcam:
+        video = cv2.VideoCapture(0)
+        time.sleep(2.0)
+    else:
+        video = cv2.VideoCapture(video_path)
+
+    while True:
+        ret, image = video.read()
+        h, w, _ = image.shape
+        boxes, confidences, class_ids = perform_detection(net, image, output_layers, w, h, confidence_threshold)
+        Items = draw_boxes(boxes, confidences, class_ids, classes, image, colors, confidence_threshold, nms_threshold)
+
+        key = cv2.waitKey(1) & 0xFF
+        if key == ord("q"):
+            break
+            quit()
+    video.release()
+    return Items
+
+
+if __name__ == '__main__':
+    
+    ap = argparse.ArgumentParser()
+    ap.add_argument('--video', help='Path to video file', default=None)
+    ap.add_argument('--camera', help='To use the live feed from web-cam', type=bool, default=True)
+    ap.add_argument('--weights', help='Path to model weights', type=str, default='yolov3-tiny.weights')
+    ap.add_argument('--configs', help='Path to model configs',type=str, default='yolov3-tiny.cfg')
+    ap.add_argument('--class_names', help='Path to class-names text file', type=str, default='coco.names')
+    ap.add_argument('--conf_thresh', help='Confidence threshold value', default=0.5)
+    ap.add_argument('--nms_thresh', help='Confidence threshold value', default=0.4)
+    args = vars(ap.parse_args())
+
+    image_path = args['image']
+    yolo_weights, yolo_cfg, coco_names = args['weights'], args['configs'], args['class_names']
+    confidence_threshold = args['conf_thresh']
+    nms_threshold = args['nms_thresh']
+
+
+
+    webcam = args['camera']
+    video_path = args['video']
+    dectection_video_file(webcam, video_path, yolo_weights, yolo_cfg, coco_names, confidence_threshold, nms_threshold)