Merge pull request #251 from HARSH-nith/master

Fix #250 Add Object Detection Workbook

Object Detection/ObjectDetectioninImage.py

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+# Before running this code download the yolo3-tiny.weights file from link given in link given in yoloweights file
+import cv2
+import numpy as np
+
+# Load Yolo
+net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
+classes = []
+with open("coco.names", "r") as f:
+    classes = [line.strip() for line in f.readlines()]
+layer_names = net.getLayerNames()
+output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+colors = np.random.uniform(0, 255, size=(len(classes), 3))
+
+# Loading image
+img = cv2.imread("Provide_path_to_image_here")
+img = cv2.resize(img, None, fx=0.9, fy=0.9)
+height, width, channels = img.shape
+
+# Detecting objects
+blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
+
+net.setInput(blob)
+outs = net.forward(output_layers)
+
+# Showing informations on the screen
+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:
+            # Object detected
+            center_x = int(detection[0] * width)
+            center_y = int(detection[1] * height)
+            w = int(detection[2] * width)
+            h = int(detection[3] * height)
+
+            # Rectangle coordinates
+            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)
+print(indexes)
+font = cv2.FONT_HERSHEY_PLAIN
+for i in range(len(boxes)):
+    if i in indexes:
+        x, y, w, h = boxes[i]
+        label = str(classes[class_ids[i]])
+        color = colors[class_ids[i]]
+        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
+        cv2.putText(img, label, (x, y + 30), font, 2, color, 2)
+
+
+cv2.imshow("Image", img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()

Object Detection/ObjectDetectioninVideos.py

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+# Before running this code download the yolo4-tiny.weights file from link given in link given in yoloweights file
+
+import cv2 as cv
+import time
+Conf_threshold = 0.4
+NMS_threshold = 0.4
+COLORS = [(0, 255, 0), (0, 0, 255), (255, 0, 0),
+          (255, 255, 0), (255, 0, 255), (0, 255, 255)]
+class_name = []
+with open('classes.txt', 'r') as f:
+    class_name = [cname.strip() for cname in f.readlines()]
+net = cv.dnn.readNet('yolov4-tiny.weights', 'yolov4-tiny.cfg')
+net.setPreferableBackend(cv.dnn.DNN_BACKEND_CUDA)
+net.setPreferableTarget(cv.dnn.DNN_TARGET_CUDA_FP16)
+
+model = cv.dnn_DetectionModel(net)
+model.setInputParams(size=(416, 416), scale=1/255, swapRB=True)
+
+cap = cv.VideoCapture('Provide_path_to_video_here')
+starting_time = time.time()
+frame_counter = 0
+while True:
+    ret, frame = cap.read()
+    frame = cv.resize(frame, (0,0), fx=0.8,fy=0.8)
+    frame_counter += 1
+    if ret == False:
+        break
+    classes, scores, boxes = model.detect(frame, Conf_threshold, NMS_threshold)
+    for (classid, score, box) in zip(classes, scores, boxes):
+        color = COLORS[int(classid) % len(COLORS)]
+        label = "%s : %f" % (class_name[classid[0]], score)
+        cv.rectangle(frame, box, color, 1)
+        cv.putText(frame, label, (box[0], box[1]-10),
+                   cv.FONT_HERSHEY_COMPLEX, 0.3, color, 1)
+    endingTime = time.time() - starting_time
+    fps = frame_counter/endingTime
+    cv.putText(frame, f'FPS: {fps}', (20, 50),
+               cv.FONT_HERSHEY_COMPLEX, 0.7, (0, 255, 0), 2)
+    cv.imshow('frame', frame)
+    key = cv.waitKey(1)
+    if key == ord('q'):
+        break
+cap.release()
+cv.destroyAllWindows()

Object Detection/classes.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

Object Detection/coco.names

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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