Moved from this repo to its own repo so that the clone isn't so HUGE
This commit is contained in:
PySimpleGUI
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# PySimpleGUI openCV YOLO Deep Learning
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# PySimpleGUI openCV YOLO Deep Learning
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To save room in the PySimpleGUI Repo, this project has been moved to its own repo on GitHub
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You'll now find the project at: https://github.com/PySimpleGUI/PySimpleGUI-YOLO
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## Running the Demos
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You will need to pip install openCV and PySimpleGUI
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```
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pip install opencv-python
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pip install pysimplegui
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```
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Run any of the .py files in the top level directory:
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```
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yolo.py - single image processing
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yolo_video.py Video display
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yolo_video_with_webcam.py - webcam or file source. Option to write to hard drive
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```
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And you'll need the training data. It's 242 MB and too large for GitHub:
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https://www.dropbox.com/s/0pq7le6fwtbarkc/yolov3.weights?dl=1
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## Learn More
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This code has an article associated with it that will step you through the code (minus GUI part).
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https://www.pyimagesearch.com/2018/11/12/yolo-object-detection-with-opencv/
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## Acknowledgements
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This software is provided by Dr. Adrian Rosebrock of the pyimagesearch organization.
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https://www.pyimagesearch.com
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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 PySimpleGUIQt 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=(22,1), 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=(0,10),orientation='h', resolution=1, default_value=5, size=(15,15), key='confidence'), sg.T(' ', key='_CONF_OUT_')],
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[sg.Text('Threshold'), sg.Slider(range=(0,10), orientation='h', resolution=1, default_value=3, size=(15,15), key='threshold'), sg.T(' ', key='_THRESH_OUT_')],
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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(), sg.Stretch()],
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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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gui_confidence = args["confidence"]/10
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gui_threshold = args["threshold"]/10
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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 > gui_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, gui_confidence, gui_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 Playback in PySimpleGUI Window', size=(30,1))],
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[sg.Image(data=imgbytes, key='_IMAGE_')],
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[sg.Text('Confidence'),
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sg.Slider(range=(0, 10), orientation='h', resolution=1, default_value=5, size=(15, 15), key='confidence'),
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sg.Text('Threshold'),
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sg.Slider(range=(0, 10), orientation='h', resolution=1, default_value=3, size=(15, 15), key='threshold')],
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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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gui_confidence = values['confidence']/10
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gui_threshold = values['threshold']/10
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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() if writer is not None else None
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vs.release()
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person
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bicycle
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car
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motorbike
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aeroplane
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bus
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train
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truck
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boat
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traffic light
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fire hydrant
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stop sign
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parking meter
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bench
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bird
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cat
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dog
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horse
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sheep
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cow
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elephant
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bear
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zebra
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giraffe
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backpack
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umbrella
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handbag
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tie
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suitcase
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frisbee
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skis
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snowboard
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sports ball
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kite
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baseball bat
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baseball glove
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skateboard
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surfboard
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tennis racket
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bottle
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wine glass
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cup
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fork
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knife
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spoon
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bowl
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banana
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apple
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sandwich
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orange
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broccoli
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carrot
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hot dog
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pizza
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donut
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cake
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chair
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sofa
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pottedplant
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bed
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diningtable
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toilet
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tvmonitor
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laptop
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mouse
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remote
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keyboard
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cell phone
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microwave
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oven
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toaster
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sink
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refrigerator
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book
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clock
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vase
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scissors
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teddy bear
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hair drier
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toothbrush
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[net]
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# Testing
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# batch=1
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# subdivisions=1
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# Training
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batch=64
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subdivisions=16
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width=608
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height=608
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channels=3
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momentum=0.9
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decay=0.0005
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angle=0
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saturation = 1.5
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exposure = 1.5
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hue=.1
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learning_rate=0.001
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burn_in=1000
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max_batches = 500200
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policy=steps
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steps=400000,450000
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scales=.1,.1
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[convolutional]
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batch_normalize=1
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filters=32
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size=3
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stride=1
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pad=1
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activation=leaky
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# Downsample
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[convolutional]
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batch_normalize=1
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filters=64
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size=3
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stride=2
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=32
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size=1
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stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=64
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
# Downsample
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=3
|
|
||||||
stride=2
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=64
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=64
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
# Downsample
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=3
|
|
||||||
stride=2
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
# Downsample
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=3
|
|
||||||
stride=2
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
# Downsample
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=1024
|
|
||||||
size=3
|
|
||||||
stride=2
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=1024
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=1024
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=1024
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=1024
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[shortcut]
|
|
||||||
from=-3
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
######################
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
filters=1024
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
filters=1024
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=512
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
filters=1024
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
filters=255
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
|
|
||||||
[yolo]
|
|
||||||
mask = 6,7,8
|
|
||||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
|
||||||
classes=80
|
|
||||||
num=9
|
|
||||||
jitter=.3
|
|
||||||
ignore_thresh = .7
|
|
||||||
truth_thresh = 1
|
|
||||||
random=1
|
|
||||||
|
|
||||||
|
|
||||||
[route]
|
|
||||||
layers = -4
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[upsample]
|
|
||||||
stride=2
|
|
||||||
|
|
||||||
[route]
|
|
||||||
layers = -1, 61
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
filters=512
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
filters=512
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=256
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
filters=512
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
filters=255
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
|
|
||||||
[yolo]
|
|
||||||
mask = 3,4,5
|
|
||||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
|
||||||
classes=80
|
|
||||||
num=9
|
|
||||||
jitter=.3
|
|
||||||
ignore_thresh = .7
|
|
||||||
truth_thresh = 1
|
|
||||||
random=1
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
[route]
|
|
||||||
layers = -4
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[upsample]
|
|
||||||
stride=2
|
|
||||||
|
|
||||||
[route]
|
|
||||||
layers = -1, 36
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
filters=256
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
filters=256
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
filters=128
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
batch_normalize=1
|
|
||||||
size=3
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
filters=256
|
|
||||||
activation=leaky
|
|
||||||
|
|
||||||
[convolutional]
|
|
||||||
size=1
|
|
||||||
stride=1
|
|
||||||
pad=1
|
|
||||||
filters=255
|
|
||||||
activation=linear
|
|
||||||
|
|
||||||
|
|
||||||
[yolo]
|
|
||||||
mask = 0,1,2
|
|
||||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
|
||||||
classes=80
|
|
||||||
num=9
|
|
||||||
jitter=.3
|
|
||||||
ignore_thresh = .7
|
|
||||||
truth_thresh = 1
|
|
||||||
random=1
|
|
||||||
|
|
||||||
|
|
@ -1,3 +0,0 @@
|
||||||
You must download this 242 MB file in order to run the Yolo demo program
|
|
||||||
|
|
||||||
https://www.dropbox.com/s/0pq7le6fwtbarkc/yolov3.weights?dl=1
|
|
||||||
|
|
@ -1,164 +0,0 @@
|
||||||
# USAGE
|
|
||||||
# python yolo.py --image images/baggage_claim.jpg --yolo yolo-coco
|
|
||||||
"""
|
|
||||||
A Yolo image processor with a GUI front-end
|
|
||||||
The original code was command line driven. Now these parameters are collected via a GUI
|
|
||||||
|
|
||||||
old usage: yolo_video.py [-h] -i INPUT -o OUTPUT -y YOLO [-c CONFIDENCE]
|
|
||||||
[-t THRESHOLD]
|
|
||||||
"""
|
|
||||||
|
|
||||||
# import the necessary packages
|
|
||||||
import numpy as np
|
|
||||||
import argparse
|
|
||||||
import time
|
|
||||||
import cv2
|
|
||||||
import os
|
|
||||||
import PySimpleGUIQt as sg
|
|
||||||
|
|
||||||
layout = [
|
|
||||||
[sg.Text('YOLO')],
|
|
||||||
[sg.Text('Path to image'), sg.In(r'C:/Python/PycharmProjects/YoloObjectDetection/images/baggage_claim.jpg',size=(40,1), key='image'), sg.FileBrowse()],
|
|
||||||
[sg.Text('Yolo base path'), sg.In(r'yolo-coco',size=(40,1), key='yolo'), sg.FolderBrowse()],
|
|
||||||
[sg.Text('Confidence'), sg.Slider(range=(0,10),orientation='h', resolution=1, default_value=5, size=(15,15), key='confidence')],
|
|
||||||
[sg.Text('Threshold'), sg.Slider(range=(0,10), orientation='h', resolution=1, default_value=3, size=(15,15), key='threshold')],
|
|
||||||
[sg.OK(), sg.Cancel(), sg.Stretch()]
|
|
||||||
]
|
|
||||||
|
|
||||||
win = sg.Window('YOLO',
|
|
||||||
default_element_size=(14,1),
|
|
||||||
text_justification='right',
|
|
||||||
auto_size_text=False).Layout(layout)
|
|
||||||
event, values = win.Read()
|
|
||||||
args = values
|
|
||||||
win.Close()
|
|
||||||
# construct the argument parse and parse the arguments
|
|
||||||
# ap = argparse.ArgumentParser()
|
|
||||||
# ap.add_argument("-i", "--image", required=True,
|
|
||||||
# help="path to input image")
|
|
||||||
# ap.add_argument("-y", "--yolo", required=True,
|
|
||||||
# help="base path to YOLO directory")
|
|
||||||
# ap.add_argument("-c", "--confidence", type=float, default=0.5,
|
|
||||||
# help="minimum probability to filter weak detections")
|
|
||||||
# ap.add_argument("-t", "--threshold", type=float, default=0.3,
|
|
||||||
# help="threshold when applyong non-maxima suppression")
|
|
||||||
# args = vars(ap.parse_args())
|
|
||||||
|
|
||||||
# load the COCO class labels our YOLO model was trained on
|
|
||||||
args['threshold'] = float(args['threshold']/10)
|
|
||||||
args['confidence'] = float(args['confidence']/10)
|
|
||||||
|
|
||||||
labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
|
|
||||||
LABELS = open(labelsPath).read().strip().split("\n")
|
|
||||||
|
|
||||||
# initialize a list of colors to represent each possible class label
|
|
||||||
np.random.seed(42)
|
|
||||||
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
|
|
||||||
dtype="uint8")
|
|
||||||
|
|
||||||
# derive the paths to the YOLO weights and model configuration
|
|
||||||
weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
|
|
||||||
configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])
|
|
||||||
|
|
||||||
# load our YOLO object detector trained on COCO dataset (80 classes)
|
|
||||||
print("[INFO] loading YOLO from disk...")
|
|
||||||
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
|
|
||||||
|
|
||||||
# load our input image and grab its spatial dimensions
|
|
||||||
image = cv2.imread(args["image"])
|
|
||||||
|
|
||||||
(H, W) = image.shape[:2]
|
|
||||||
|
|
||||||
# determine only the *output* layer names that we need from YOLO
|
|
||||||
ln = net.getLayerNames()
|
|
||||||
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
|
|
||||||
|
|
||||||
# construct a blob from the input image and then perform a forward
|
|
||||||
# pass of the YOLO object detector, giving us our bounding boxes and
|
|
||||||
# associated probabilities
|
|
||||||
blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),
|
|
||||||
swapRB=True, crop=False)
|
|
||||||
net.setInput(blob)
|
|
||||||
start = time.time()
|
|
||||||
layerOutputs = net.forward(ln)
|
|
||||||
end = time.time()
|
|
||||||
|
|
||||||
# show timing information on YOLO
|
|
||||||
print("[INFO] YOLO took {:.6f} seconds".format(end - start))
|
|
||||||
|
|
||||||
# initialize our lists of detected bounding boxes, confidences, and
|
|
||||||
# class IDs, respectively
|
|
||||||
boxes = []
|
|
||||||
confidences = []
|
|
||||||
classIDs = []
|
|
||||||
|
|
||||||
# loop over each of the layer outputs
|
|
||||||
for output in layerOutputs:
|
|
||||||
# loop over each of the detections
|
|
||||||
for detection in output:
|
|
||||||
# extract the class ID and confidence (i.e., probability) of
|
|
||||||
# the current object detection
|
|
||||||
scores = detection[5:]
|
|
||||||
classID = np.argmax(scores)
|
|
||||||
confidence = scores[classID]
|
|
||||||
|
|
||||||
# filter out weak predictions by ensuring the detected
|
|
||||||
# probability is greater than the minimum probability
|
|
||||||
if confidence > args["confidence"]:
|
|
||||||
# scale the bounding box coordinates back relative to the
|
|
||||||
# size of the image, keeping in mind that YOLO actually
|
|
||||||
# returns the center (x, y)-coordinates of the bounding
|
|
||||||
# box followed by the boxes' width and height
|
|
||||||
box = detection[0:4] * np.array([W, H, W, H])
|
|
||||||
(centerX, centerY, width, height) = box.astype("int")
|
|
||||||
|
|
||||||
# use the center (x, y)-coordinates to derive the top and
|
|
||||||
# and left corner of the bounding box
|
|
||||||
x = int(centerX - (width / 2))
|
|
||||||
y = int(centerY - (height / 2))
|
|
||||||
|
|
||||||
# update our list of bounding box coordinates, confidences,
|
|
||||||
# and class IDs
|
|
||||||
boxes.append([x, y, int(width), int(height)])
|
|
||||||
confidences.append(float(confidence))
|
|
||||||
classIDs.append(classID)
|
|
||||||
|
|
||||||
# apply non-maxima suppression to suppress weak, overlapping bounding
|
|
||||||
# boxes
|
|
||||||
idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"],
|
|
||||||
args["threshold"])
|
|
||||||
|
|
||||||
# ensure at least one detection exists
|
|
||||||
if len(idxs) > 0:
|
|
||||||
# loop over the indexes we are keeping
|
|
||||||
for i in idxs.flatten():
|
|
||||||
# extract the bounding box coordinates
|
|
||||||
(x, y) = (boxes[i][0], boxes[i][1])
|
|
||||||
(w, h) = (boxes[i][2], boxes[i][3])
|
|
||||||
|
|
||||||
# draw a bounding box rectangle and label on the image
|
|
||||||
color = [int(c) for c in COLORS[classIDs[i]]]
|
|
||||||
cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
|
|
||||||
text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
|
|
||||||
cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,
|
|
||||||
0.5, color, 2)
|
|
||||||
|
|
||||||
# show the output image
|
|
||||||
imgbytes = cv2.imencode('.png', image)[1].tobytes() # ditto
|
|
||||||
|
|
||||||
|
|
||||||
layout = [
|
|
||||||
[sg.Text('Yolo Output')],
|
|
||||||
[sg.Image(data=imgbytes)],
|
|
||||||
[sg.OK(), sg.Cancel()]
|
|
||||||
]
|
|
||||||
|
|
||||||
win = sg.Window('YOLO',
|
|
||||||
default_element_size=(14,1),
|
|
||||||
text_justification='right',
|
|
||||||
auto_size_text=False).Layout(layout)
|
|
||||||
event, values = win.Read()
|
|
||||||
win.Close()
|
|
||||||
|
|
||||||
# cv2.imshow("Image", image)
|
|
||||||
cv2.waitKey(0)
|
|
||||||
|
|
@ -1,207 +0,0 @@
|
||||||
# USAGE
|
|
||||||
# python yolo_video.py --input videos/airport.mp4 --output output/airport_output.avi --yolo yolo-coco
|
|
||||||
|
|
||||||
# import the necessary packages
|
|
||||||
import numpy as np
|
|
||||||
# import argparse
|
|
||||||
import imutils
|
|
||||||
import time
|
|
||||||
import cv2
|
|
||||||
import os
|
|
||||||
import PySimpleGUI as sg
|
|
||||||
|
|
||||||
i_vid = r'videos\car_chase_01.mp4'
|
|
||||||
# o_vid = r'videos\car_chase_01_out.mp4'
|
|
||||||
y_path = r'yolo-coco'
|
|
||||||
layout = [
|
|
||||||
[sg.Text('YOLO Video Player', size=(18,1), font=('Any',18),text_color='#1c86ee' ,justification='left')],
|
|
||||||
[sg.Text('Path to input video'), sg.In(i_vid,size=(40,1), key='input'), sg.FileBrowse()],
|
|
||||||
# [sg.Text('Path to output video'), sg.In(o_vid,size=(40,1), key='output'), sg.FileSaveAs()],
|
|
||||||
[sg.Text('Yolo base path'), sg.In(y_path,size=(40,1), key='yolo'), sg.FolderBrowse()],
|
|
||||||
[sg.Text('Confidence'), sg.Slider(range=(0,1),orientation='h', resolution=.1, default_value=.5, size=(15,15), key='confidence')],
|
|
||||||
[sg.Text('Threshold'), sg.Slider(range=(0,1), orientation='h', resolution=.1, default_value=.3, size=(15,15), key='threshold')],
|
|
||||||
[sg.OK(), sg.Cancel()]
|
|
||||||
]
|
|
||||||
|
|
||||||
win = sg.Window('YOLO Video',
|
|
||||||
default_element_size=(14,1),
|
|
||||||
text_justification='right',
|
|
||||||
auto_size_text=False).Layout(layout)
|
|
||||||
event, values = win.Read()
|
|
||||||
if event is None or event =='Cancel':
|
|
||||||
exit()
|
|
||||||
args = values
|
|
||||||
|
|
||||||
win.Close()
|
|
||||||
|
|
||||||
|
|
||||||
# imgbytes = cv2.imencode('.png', image)[1].tobytes() # ditto
|
|
||||||
|
|
||||||
# load the COCO class labels our YOLO model was trained on
|
|
||||||
labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
|
|
||||||
LABELS = open(labelsPath).read().strip().split("\n")
|
|
||||||
|
|
||||||
# initialize a list of colors to represent each possible class label
|
|
||||||
np.random.seed(42)
|
|
||||||
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
|
|
||||||
dtype="uint8")
|
|
||||||
|
|
||||||
# derive the paths to the YOLO weights and model configuration
|
|
||||||
weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
|
|
||||||
configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])
|
|
||||||
|
|
||||||
# load our YOLO object detector trained on COCO dataset (80 classes)
|
|
||||||
# and determine only the *output* layer names that we need from YOLO
|
|
||||||
print("[INFO] loading YOLO from disk...")
|
|
||||||
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
|
|
||||||
ln = net.getLayerNames()
|
|
||||||
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
|
|
||||||
|
|
||||||
# initialize the video stream, pointer to output video file, and
|
|
||||||
# frame dimensions
|
|
||||||
vs = cv2.VideoCapture(args["input"])
|
|
||||||
writer = None
|
|
||||||
(W, H) = (None, None)
|
|
||||||
|
|
||||||
# try to determine the total number of frames in the video file
|
|
||||||
try:
|
|
||||||
prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \
|
|
||||||
else cv2.CAP_PROP_FRAME_COUNT
|
|
||||||
total = int(vs.get(prop))
|
|
||||||
print("[INFO] {} total frames in video".format(total))
|
|
||||||
|
|
||||||
# an error occurred while trying to determine the total
|
|
||||||
# number of frames in the video file
|
|
||||||
except:
|
|
||||||
print("[INFO] could not determine # of frames in video")
|
|
||||||
print("[INFO] no approx. completion time can be provided")
|
|
||||||
total = -1
|
|
||||||
|
|
||||||
# loop over frames from the video file stream
|
|
||||||
win_started = False
|
|
||||||
while True:
|
|
||||||
# read the next frame from the file
|
|
||||||
(grabbed, frame) = vs.read()
|
|
||||||
|
|
||||||
# if the frame was not grabbed, then we have reached the end
|
|
||||||
# of the stream
|
|
||||||
if not grabbed:
|
|
||||||
break
|
|
||||||
|
|
||||||
# if the frame dimensions are empty, grab them
|
|
||||||
if W is None or H is None:
|
|
||||||
(H, W) = frame.shape[:2]
|
|
||||||
|
|
||||||
# construct a blob from the input frame and then perform a forward
|
|
||||||
# pass of the YOLO object detector, giving us our bounding boxes
|
|
||||||
# and associated probabilities
|
|
||||||
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
|
|
||||||
swapRB=True, crop=False)
|
|
||||||
net.setInput(blob)
|
|
||||||
start = time.time()
|
|
||||||
layerOutputs = net.forward(ln)
|
|
||||||
end = time.time()
|
|
||||||
|
|
||||||
# initialize our lists of detected bounding boxes, confidences,
|
|
||||||
# and class IDs, respectively
|
|
||||||
boxes = []
|
|
||||||
confidences = []
|
|
||||||
classIDs = []
|
|
||||||
|
|
||||||
# loop over each of the layer outputs
|
|
||||||
for output in layerOutputs:
|
|
||||||
# loop over each of the detections
|
|
||||||
for detection in output:
|
|
||||||
# extract the class ID and confidence (i.e., probability)
|
|
||||||
# of the current object detection
|
|
||||||
scores = detection[5:]
|
|
||||||
classID = np.argmax(scores)
|
|
||||||
confidence = scores[classID]
|
|
||||||
|
|
||||||
# filter out weak predictions by ensuring the detected
|
|
||||||
# probability is greater than the minimum probability
|
|
||||||
if confidence > args["confidence"]:
|
|
||||||
# scale the bounding box coordinates back relative to
|
|
||||||
# the size of the image, keeping in mind that YOLO
|
|
||||||
# actually returns the center (x, y)-coordinates of
|
|
||||||
# the bounding box followed by the boxes' width and
|
|
||||||
# height
|
|
||||||
box = detection[0:4] * np.array([W, H, W, H])
|
|
||||||
(centerX, centerY, width, height) = box.astype("int")
|
|
||||||
|
|
||||||
# use the center (x, y)-coordinates to derive the top
|
|
||||||
# and and left corner of the bounding box
|
|
||||||
x = int(centerX - (width / 2))
|
|
||||||
y = int(centerY - (height / 2))
|
|
||||||
|
|
||||||
# update our list of bounding box coordinates,
|
|
||||||
# confidences, and class IDs
|
|
||||||
boxes.append([x, y, int(width), int(height)])
|
|
||||||
confidences.append(float(confidence))
|
|
||||||
classIDs.append(classID)
|
|
||||||
|
|
||||||
# apply non-maxima suppression to suppress weak, overlapping
|
|
||||||
# bounding boxes
|
|
||||||
idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"],
|
|
||||||
args["threshold"])
|
|
||||||
|
|
||||||
# ensure at least one detection exists
|
|
||||||
if len(idxs) > 0:
|
|
||||||
# loop over the indexes we are keeping
|
|
||||||
for i in idxs.flatten():
|
|
||||||
# extract the bounding box coordinates
|
|
||||||
(x, y) = (boxes[i][0], boxes[i][1])
|
|
||||||
(w, h) = (boxes[i][2], boxes[i][3])
|
|
||||||
|
|
||||||
# draw a bounding box rectangle and label on the frame
|
|
||||||
color = [int(c) for c in COLORS[classIDs[i]]]
|
|
||||||
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
|
|
||||||
text = "{}: {:.4f}".format(LABELS[classIDs[i]],
|
|
||||||
confidences[i])
|
|
||||||
cv2.putText(frame, text, (x, y - 5),
|
|
||||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
|
|
||||||
|
|
||||||
# check if the video writer is None
|
|
||||||
# if writer is None:
|
|
||||||
# # initialize our video writer
|
|
||||||
# fourcc = cv2.VideoWriter_fourcc(*"MJPG")
|
|
||||||
# writer = cv2.VideoWriter(args["output"], fourcc, 30,
|
|
||||||
# (frame.shape[1], frame.shape[0]), True)
|
|
||||||
#
|
|
||||||
# # some information on processing single frame
|
|
||||||
# if total > 0:
|
|
||||||
# elap = (end - start)
|
|
||||||
# print("[INFO] single frame took {:.4f} seconds".format(elap))
|
|
||||||
# print("[INFO] estimated total time to finish: {:.4f}".format(
|
|
||||||
# elap * total))
|
|
||||||
|
|
||||||
# write the output frame to disk
|
|
||||||
# writer.write(frame)
|
|
||||||
imgbytes = cv2.imencode('.png', frame)[1].tobytes() # ditto
|
|
||||||
|
|
||||||
if not win_started:
|
|
||||||
win_started = True
|
|
||||||
layout = [
|
|
||||||
[sg.Text('Yolo Output')],
|
|
||||||
[sg.Image(data=imgbytes, key='_IMAGE_')],
|
|
||||||
[sg.Exit()]
|
|
||||||
]
|
|
||||||
win = sg.Window('YOLO Output',
|
|
||||||
default_element_size=(14, 1),
|
|
||||||
text_justification='right',
|
|
||||||
auto_size_text=False).Layout(layout).Finalize()
|
|
||||||
image_elem = win.FindElement('_IMAGE_')
|
|
||||||
else:
|
|
||||||
image_elem.Update(data=imgbytes)
|
|
||||||
|
|
||||||
event, values = win.Read(timeout=0)
|
|
||||||
if event is None or event == 'Exit':
|
|
||||||
break
|
|
||||||
|
|
||||||
|
|
||||||
win.Close()
|
|
||||||
|
|
||||||
# release the file pointers
|
|
||||||
print("[INFO] cleaning up...")
|
|
||||||
writer.release()
|
|
||||||
vs.release()
|
|
||||||
|
|
@ -1,222 +0,0 @@
|
||||||
# YOLO object detection using a webcam
|
|
||||||
# Exact same demo as the read from disk, but instead of disk a webcam is used.
|
|
||||||
# import the necessary packages
|
|
||||||
import numpy as np
|
|
||||||
# import argparse
|
|
||||||
import imutils
|
|
||||||
import time
|
|
||||||
import cv2
|
|
||||||
import os
|
|
||||||
import PySimpleGUI as sg
|
|
||||||
|
|
||||||
i_vid = r'videos\car_chase_01.mp4'
|
|
||||||
o_vid = r'output\car_chase_01_out.mp4'
|
|
||||||
y_path = r'yolo-coco'
|
|
||||||
sg.ChangeLookAndFeel('LightGreen')
|
|
||||||
layout = [
|
|
||||||
[sg.Text('YOLO Video Player', size=(18,1), font=('Any',18),text_color='#1c86ee' ,justification='left')],
|
|
||||||
[sg.Text('Path to input video'), sg.In(i_vid,size=(40,1), key='input'), sg.FileBrowse()],
|
|
||||||
[sg.Text('Optional Path to output video'), sg.In(o_vid,size=(40,1), key='output'), sg.FileSaveAs()],
|
|
||||||
[sg.Text('Yolo base path'), sg.In(y_path,size=(40,1), key='yolo'), sg.FolderBrowse()],
|
|
||||||
[sg.Text('Confidence'), sg.Slider(range=(0,1),orientation='h', resolution=.1, default_value=.5, size=(15,15), key='confidence')],
|
|
||||||
[sg.Text('Threshold'), sg.Slider(range=(0,1), orientation='h', resolution=.1, default_value=.3, size=(15,15), key='threshold')],
|
|
||||||
[sg.Text(' '*8), sg.Checkbox('Use webcam', key='_WEBCAM_')],
|
|
||||||
[sg.Text(' '*8), sg.Checkbox('Write to disk', key='_DISK_')],
|
|
||||||
[sg.OK(), sg.Cancel()]
|
|
||||||
]
|
|
||||||
|
|
||||||
win = sg.Window('YOLO Video',
|
|
||||||
default_element_size=(21,1),
|
|
||||||
text_justification='right',
|
|
||||||
auto_size_text=False).Layout(layout)
|
|
||||||
event, values = win.Read()
|
|
||||||
if event is None or event =='Cancel':
|
|
||||||
exit()
|
|
||||||
write_to_disk = values['_DISK_']
|
|
||||||
use_webcam = values['_WEBCAM_']
|
|
||||||
args = values
|
|
||||||
|
|
||||||
win.Close()
|
|
||||||
|
|
||||||
|
|
||||||
# imgbytes = cv2.imencode('.png', image)[1].tobytes() # ditto
|
|
||||||
gui_confidence = args["confidence"]
|
|
||||||
gui_threshold = args["threshold"]
|
|
||||||
# load the COCO class labels our YOLO model was trained on
|
|
||||||
labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
|
|
||||||
LABELS = open(labelsPath).read().strip().split("\n")
|
|
||||||
|
|
||||||
# initialize a list of colors to represent each possible class label
|
|
||||||
np.random.seed(42)
|
|
||||||
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
|
|
||||||
dtype="uint8")
|
|
||||||
|
|
||||||
# derive the paths to the YOLO weights and model configuration
|
|
||||||
weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
|
|
||||||
configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])
|
|
||||||
|
|
||||||
# load our YOLO object detector trained on COCO dataset (80 classes)
|
|
||||||
# and determine only the *output* layer names that we need from YOLO
|
|
||||||
print("[INFO] loading YOLO from disk...")
|
|
||||||
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
|
|
||||||
ln = net.getLayerNames()
|
|
||||||
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
|
|
||||||
|
|
||||||
# initialize the video stream, pointer to output video file, and
|
|
||||||
# frame dimensions
|
|
||||||
vs = cv2.VideoCapture(args["input"])
|
|
||||||
writer = None
|
|
||||||
(W, H) = (None, None)
|
|
||||||
|
|
||||||
# try to determine the total number of frames in the video file
|
|
||||||
try:
|
|
||||||
prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \
|
|
||||||
else cv2.CAP_PROP_FRAME_COUNT
|
|
||||||
total = int(vs.get(prop))
|
|
||||||
print("[INFO] {} total frames in video".format(total))
|
|
||||||
|
|
||||||
# an error occurred while trying to determine the total
|
|
||||||
# number of frames in the video file
|
|
||||||
except:
|
|
||||||
print("[INFO] could not determine # of frames in video")
|
|
||||||
print("[INFO] no approx. completion time can be provided")
|
|
||||||
total = -1
|
|
||||||
|
|
||||||
# loop over frames from the video file stream
|
|
||||||
win_started = False
|
|
||||||
if use_webcam:
|
|
||||||
cap = cv2.VideoCapture(0)
|
|
||||||
while True:
|
|
||||||
# read the next frame from the file or webcam
|
|
||||||
if use_webcam:
|
|
||||||
grabbed, frame = cap.read()
|
|
||||||
else:
|
|
||||||
grabbed, frame = vs.read()
|
|
||||||
|
|
||||||
# if the frame was not grabbed, then we have reached the end
|
|
||||||
# of the stream
|
|
||||||
if not grabbed:
|
|
||||||
break
|
|
||||||
|
|
||||||
# if the frame dimensions are empty, grab them
|
|
||||||
if W is None or H is None:
|
|
||||||
(H, W) = frame.shape[:2]
|
|
||||||
|
|
||||||
# construct a blob from the input frame and then perform a forward
|
|
||||||
# pass of the YOLO object detector, giving us our bounding boxes
|
|
||||||
# and associated probabilities
|
|
||||||
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
|
|
||||||
swapRB=True, crop=False)
|
|
||||||
net.setInput(blob)
|
|
||||||
start = time.time()
|
|
||||||
layerOutputs = net.forward(ln)
|
|
||||||
end = time.time()
|
|
||||||
|
|
||||||
# initialize our lists of detected bounding boxes, confidences,
|
|
||||||
# and class IDs, respectively
|
|
||||||
boxes = []
|
|
||||||
confidences = []
|
|
||||||
classIDs = []
|
|
||||||
|
|
||||||
# loop over each of the layer outputs
|
|
||||||
for output in layerOutputs:
|
|
||||||
# loop over each of the detections
|
|
||||||
for detection in output:
|
|
||||||
# extract the class ID and confidence (i.e., probability)
|
|
||||||
# of the current object detection
|
|
||||||
scores = detection[5:]
|
|
||||||
classID = np.argmax(scores)
|
|
||||||
confidence = scores[classID]
|
|
||||||
|
|
||||||
# filter out weak predictions by ensuring the detected
|
|
||||||
# probability is greater than the minimum probability
|
|
||||||
if confidence > gui_confidence:
|
|
||||||
# scale the bounding box coordinates back relative to
|
|
||||||
# the size of the image, keeping in mind that YOLO
|
|
||||||
# actually returns the center (x, y)-coordinates of
|
|
||||||
# the bounding box followed by the boxes' width and
|
|
||||||
# height
|
|
||||||
box = detection[0:4] * np.array([W, H, W, H])
|
|
||||||
(centerX, centerY, width, height) = box.astype("int")
|
|
||||||
|
|
||||||
# use the center (x, y)-coordinates to derive the top
|
|
||||||
# and and left corner of the bounding box
|
|
||||||
x = int(centerX - (width / 2))
|
|
||||||
y = int(centerY - (height / 2))
|
|
||||||
|
|
||||||
# update our list of bounding box coordinates,
|
|
||||||
# confidences, and class IDs
|
|
||||||
boxes.append([x, y, int(width), int(height)])
|
|
||||||
confidences.append(float(confidence))
|
|
||||||
classIDs.append(classID)
|
|
||||||
|
|
||||||
# apply non-maxima suppression to suppress weak, overlapping
|
|
||||||
# bounding boxes
|
|
||||||
idxs = cv2.dnn.NMSBoxes(boxes, confidences, gui_confidence, gui_threshold)
|
|
||||||
|
|
||||||
# ensure at least one detection exists
|
|
||||||
if len(idxs) > 0:
|
|
||||||
# loop over the indexes we are keeping
|
|
||||||
for i in idxs.flatten():
|
|
||||||
# extract the bounding box coordinates
|
|
||||||
(x, y) = (boxes[i][0], boxes[i][1])
|
|
||||||
(w, h) = (boxes[i][2], boxes[i][3])
|
|
||||||
|
|
||||||
# draw a bounding box rectangle and label on the frame
|
|
||||||
color = [int(c) for c in COLORS[classIDs[i]]]
|
|
||||||
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
|
|
||||||
text = "{}: {:.4f}".format(LABELS[classIDs[i]],
|
|
||||||
confidences[i])
|
|
||||||
cv2.putText(frame, text, (x, y - 5),
|
|
||||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
|
|
||||||
if write_to_disk:
|
|
||||||
#check if the video writer is None
|
|
||||||
if writer is None:
|
|
||||||
# initialize our video writer
|
|
||||||
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
|
|
||||||
writer = cv2.VideoWriter(args["output"], fourcc, 30,
|
|
||||||
(frame.shape[1], frame.shape[0]), True)
|
|
||||||
|
|
||||||
# some information on processing single frame
|
|
||||||
if total > 0:
|
|
||||||
elap = (end - start)
|
|
||||||
print("[INFO] single frame took {:.4f} seconds".format(elap))
|
|
||||||
print("[INFO] estimated total time to finish: {:.4f}".format(
|
|
||||||
elap * total))
|
|
||||||
|
|
||||||
#write the output frame to disk
|
|
||||||
writer.write(frame)
|
|
||||||
imgbytes = cv2.imencode('.png', frame)[1].tobytes() # ditto
|
|
||||||
|
|
||||||
if not win_started:
|
|
||||||
win_started = True
|
|
||||||
layout = [
|
|
||||||
[sg.Text('Yolo Playback in PySimpleGUI Window', size=(30,1))],
|
|
||||||
[sg.Image(data=imgbytes, key='_IMAGE_')],
|
|
||||||
[sg.Text('Confidence'),
|
|
||||||
sg.Slider(range=(0, 1), orientation='h', resolution=.1, default_value=.5, size=(15, 15), key='confidence'),
|
|
||||||
sg.Text('Threshold'),
|
|
||||||
sg.Slider(range=(0, 1), orientation='h', resolution=.1, default_value=.3, size=(15, 15), key='threshold')],
|
|
||||||
[sg.Exit()]
|
|
||||||
]
|
|
||||||
win = sg.Window('YOLO Output',
|
|
||||||
default_element_size=(14, 1),
|
|
||||||
text_justification='right',
|
|
||||||
auto_size_text=False).Layout(layout).Finalize()
|
|
||||||
image_elem = win.FindElement('_IMAGE_')
|
|
||||||
else:
|
|
||||||
image_elem.Update(data=imgbytes)
|
|
||||||
|
|
||||||
event, values = win.Read(timeout=0)
|
|
||||||
if event is None or event == 'Exit':
|
|
||||||
break
|
|
||||||
gui_confidence = values['confidence']
|
|
||||||
gui_threshold = values['threshold']
|
|
||||||
|
|
||||||
|
|
||||||
win.Close()
|
|
||||||
|
|
||||||
# release the file pointers
|
|
||||||
print("[INFO] cleaning up...")
|
|
||||||
writer.release() if writer is not None else None
|
|
||||||
vs.release()
|
|
||||||
Loading…
Reference in New Issue