New program to use webcam with Yolo
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MikeTheWatchGuy
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YoloObjectDetection/yolo_video_with_webcam.py
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YoloObjectDetection/yolo_video_with_webcam.py
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# YOLO object detection using a webcam
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# Exact same demo as the read from disk, but instead of disk a webcam is used.
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# import the necessary packages
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import numpy as np
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# import argparse
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import imutils
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import time
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import cv2
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import os
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import PySimpleGUI as sg
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i_vid = r'videos/car_chase_01.mp4'
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o_vid = r'output/car_chase_01_out.mp4'
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y_path = r'yolo-coco'
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sg.ChangeLookAndFeel('LightGreen')
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layout = [
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[sg.Text('YOLO Video Player', size=(18,1), font=('Any',18),text_color='#1c86ee' ,justification='left')],
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[sg.Text('Path to input video'), sg.In(i_vid,size=(40,1), key='input'), sg.FileBrowse()],
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[sg.Text('Optional Path to output video'), sg.In(o_vid,size=(40,1), key='output'), sg.FileSaveAs()],
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[sg.Text('Yolo base path'), sg.In(y_path,size=(40,1), key='yolo'), sg.FolderBrowse()],
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[sg.Text('Confidence'), sg.Slider(range=(0,1),orientation='h', resolution=.1, default_value=.5, size=(15,15), key='confidence')],
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[sg.Text('Threshold'), sg.Slider(range=(0,1), orientation='h', resolution=.1, default_value=.3, size=(15,15), key='threshold')],
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[sg.Text(' '*8), sg.Checkbox('Use webcam', key='_WEBCAM_')],
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[sg.Text(' '*8), sg.Checkbox('Write to disk', key='_DISK_')],
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[sg.OK(), sg.Cancel()]
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]
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win = sg.Window('YOLO Video',
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default_element_size=(21,1),
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text_justification='right',
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auto_size_text=False).Layout(layout)
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event, values = win.Read()
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if event is None or event =='Cancel':
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exit()
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write_to_disk = values['_DISK_']
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use_webcam = values['_WEBCAM_']
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args = values
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win.Close()
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# imgbytes = cv2.imencode('.png', image)[1].tobytes() # ditto
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# load the COCO class labels our YOLO model was trained on
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labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
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LABELS = open(labelsPath).read().strip().split("\n")
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# initialize a list of colors to represent each possible class label
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np.random.seed(42)
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COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
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dtype="uint8")
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# derive the paths to the YOLO weights and model configuration
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weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
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configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])
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# load our YOLO object detector trained on COCO dataset (80 classes)
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# and determine only the *output* layer names that we need from YOLO
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print("[INFO] loading YOLO from disk...")
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net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
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ln = net.getLayerNames()
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ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
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# initialize the video stream, pointer to output video file, and
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# frame dimensions
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vs = cv2.VideoCapture(args["input"])
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writer = None
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(W, H) = (None, None)
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# try to determine the total number of frames in the video file
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try:
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prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \
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else cv2.CAP_PROP_FRAME_COUNT
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total = int(vs.get(prop))
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print("[INFO] {} total frames in video".format(total))
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# an error occurred while trying to determine the total
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# number of frames in the video file
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except:
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print("[INFO] could not determine # of frames in video")
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print("[INFO] no approx. completion time can be provided")
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total = -1
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# loop over frames from the video file stream
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win_started = False
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if use_webcam:
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cap = cv2.VideoCapture(0)
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while True:
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# read the next frame from the file or webcam
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if use_webcam:
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grabbed, frame = cap.read()
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else:
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(grabbed, frame) = vs.read()
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# if the frame was not grabbed, then we have reached the end
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# of the stream
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if not grabbed:
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break
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# if the frame dimensions are empty, grab them
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if W is None or H is None:
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(H, W) = frame.shape[:2]
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# construct a blob from the input frame and then perform a forward
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# pass of the YOLO object detector, giving us our bounding boxes
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# and associated probabilities
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blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
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swapRB=True, crop=False)
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net.setInput(blob)
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start = time.time()
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layerOutputs = net.forward(ln)
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end = time.time()
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# initialize our lists of detected bounding boxes, confidences,
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# and class IDs, respectively
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boxes = []
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confidences = []
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classIDs = []
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# loop over each of the layer outputs
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for output in layerOutputs:
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# loop over each of the detections
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for detection in output:
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# extract the class ID and confidence (i.e., probability)
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# of the current object detection
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scores = detection[5:]
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classID = np.argmax(scores)
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confidence = scores[classID]
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# filter out weak predictions by ensuring the detected
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# probability is greater than the minimum probability
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if confidence > args["confidence"]:
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# scale the bounding box coordinates back relative to
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# the size of the image, keeping in mind that YOLO
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# actually returns the center (x, y)-coordinates of
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# the bounding box followed by the boxes' width and
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# height
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box = detection[0:4] * np.array([W, H, W, H])
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(centerX, centerY, width, height) = box.astype("int")
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# use the center (x, y)-coordinates to derive the top
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# and and left corner of the bounding box
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x = int(centerX - (width / 2))
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y = int(centerY - (height / 2))
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# update our list of bounding box coordinates,
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# confidences, and class IDs
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boxes.append([x, y, int(width), int(height)])
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confidences.append(float(confidence))
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classIDs.append(classID)
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# apply non-maxima suppression to suppress weak, overlapping
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# bounding boxes
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idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"],
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args["threshold"])
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# ensure at least one detection exists
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if len(idxs) > 0:
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# loop over the indexes we are keeping
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for i in idxs.flatten():
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# extract the bounding box coordinates
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(x, y) = (boxes[i][0], boxes[i][1])
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(w, h) = (boxes[i][2], boxes[i][3])
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# draw a bounding box rectangle and label on the frame
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color = [int(c) for c in COLORS[classIDs[i]]]
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cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
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text = "{}: {:.4f}".format(LABELS[classIDs[i]],
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confidences[i])
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cv2.putText(frame, text, (x, y - 5),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
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if write_to_disk:
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#check if the video writer is None
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if writer is None:
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# initialize our video writer
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fourcc = cv2.VideoWriter_fourcc(*"MJPG")
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writer = cv2.VideoWriter(args["output"], fourcc, 30,
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(frame.shape[1], frame.shape[0]), True)
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# some information on processing single frame
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if total > 0:
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elap = (end - start)
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print("[INFO] single frame took {:.4f} seconds".format(elap))
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print("[INFO] estimated total time to finish: {:.4f}".format(
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elap * total))
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#write the output frame to disk
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writer.write(frame)
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imgbytes = cv2.imencode('.png', frame)[1].tobytes() # ditto
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if not win_started:
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win_started = True
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layout = [
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[sg.Text('Yolo Output')],
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[sg.Image(data=imgbytes, key='_IMAGE_')],
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[sg.Exit()]
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]
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win = sg.Window('YOLO Output',
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default_element_size=(14, 1),
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text_justification='right',
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auto_size_text=False).Layout(layout).Finalize()
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image_elem = win.FindElement('_IMAGE_')
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else:
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image_elem.Update(data=imgbytes)
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event, values = win.Read(timeout=0)
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if event is None or event == 'Exit':
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break
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win.Close()
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# release the file pointers
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print("[INFO] cleaning up...")
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writer.release()
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vs.release()
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