object detection

Author: sudhar-01

object_detection-sudhar-01.py

@@ -0,0 +1,84 @@
+import cv2 as cv
+import numpy as np
+
+confThreshold = 0.5
+nmsThreshold = 0.5
+
+def getOutputsNames(net):
+    layersNames = net.getLayerNames()
+    return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+
+def postprocess(frame, outs):
+    frameHeight = frame.shape[0]
+    frameWidth = frame.shape[1]
+    classIds = []
+    confidences = []
+    boxes = []
+
+    for out in outs:
+        for detection in out:
+            scores = detection[5:]
+            classId = np.argmax(scores)
+            confidence = scores[classId]
+            if confidence > confThreshold:
+                center_x = int(detection[0] * frameWidth)
+                center_y = int(detection[1] * frameHeight)
+                width = int(detection[2] * frameWidth)
+                height = int(detection[3] * frameHeight)
+                left = int(center_x - width / 2)
+                top = int(center_y - height / 2)
+                classIds.append(classId)
+                confidences.append(float(confidence))
+                boxes.append([left, top, width, height])
+
+    indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
+    for i in indices:
+        i = i[0]
+        box = boxes[i]
+        left = box[0]
+        top = box[1]
+        width = box[2]
+        height = box[3]
+        color = (255,255,0)
+        if classIds[i] == 0:          # Blue if person
+            color = (255,0,0)
+        elif classIds[i] == 2:        # Green if car  
+            color = (0,255,0)
+        elif classIds[i] == 7:        # Red if Truck
+            color = (0,0,255)
+        
+        label = '%s:%s' %  (classes[classIds[i]],round(confidences[i]*100))
+        cv.putText(frame,label,(left,top-10),cv.FONT_HERSHEY_SIMPLEX,0.3,color,1)
+        cv.rectangle(frame, (left, top), (left+width, top+height),color, 2)
+
+classes = []
+with open(r"path\\coco.names") as f:
+    classes = f.read().rstrip('\n').split('\n')
+print(classes)
+
+
+modelConfiguration = "path\\yolov3.cfg"
+weights = "path\\yolov3.weights"
+net = cv.dnn.readNetFromDarknet(modelConfiguration,weights)
+net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
+net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
+
+
+
+cap = cv.VideoCapture("demo_Trim.mp4")
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret:
+        print("Can't receive frame (stream end?). Exiting ...")
+        break
+    blob = cv.dnn.blobFromImage(frame,1/225,(416,416),[0,0,0],1)
+    net.setInput(blob)
+    outs = net.forward(getOutputsNames(net))
+    postprocess(frame, outs)
+
+    cv.imshow("frame",frame)
+    if cv.waitKey(100) == 27:
+        break
+cap.release()
+cv.destroyAllWindows()
+