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103 lines
2.8 KiB
103 lines
2.8 KiB
"""USAGE |
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python examples/yolo_img_obj_detector.py \ |
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-c ~/syncthing/dropbox/handai/obj_tracking/mytrain.cfg \ |
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-w ~/syncthing/dropbox/handai/obj_tracking/mytrain_final.weights \ |
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-cl ~/syncthing/dropbox/handai/obj_tracking/mytrain.names \ |
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-i ~/syncthing/dropbox/handai/obj_tracking/person.jpg |
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""" |
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import cv2 |
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import argparse |
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import numpy as np |
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ap = argparse.ArgumentParser() |
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ap.add_argument("-i", "--image", required=True, help="path to input image") |
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ap.add_argument("-c", "--config", required=True, help="path to yolo config file") |
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ap.add_argument( |
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"-w", "--weights", required=True, help="path to yolo pre-trained weights" |
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) |
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ap.add_argument( |
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"-cl", "--classes", required=True, help="path to text file containing class names" |
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) |
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args = ap.parse_args() |
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def get_output_layers(net): |
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layer_names = net.getLayerNames() |
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output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()] |
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return output_layers |
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def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h): |
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label = str(classes[class_id]) |
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color = COLORS[class_id] |
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cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2) |
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cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) |
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image = cv2.imread(args.image) |
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Width = image.shape[1] |
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Height = image.shape[0] |
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scale = 0.00392 |
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classes = None |
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with open(args.classes, "r") as f: |
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classes = [line.strip() for line in f.readlines()] |
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COLORS = np.random.uniform(0, 255, size=(len(classes), 3)) |
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net = cv2.dnn.readNet(args.weights, args.config) |
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blob = cv2.dnn.blobFromImage(image, scale, (416, 416), (0, 0, 0), True, crop=False) |
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net.setInput(blob) |
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outs = net.forward(get_output_layers(net)) |
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class_ids = [] |
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confidences = [] |
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boxes = [] |
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conf_threshold = 0.5 |
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nms_threshold = 0.4 |
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for out in outs: |
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for detection in out: |
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scores = detection[5:] |
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class_id = np.argmax(scores) |
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confidence = scores[class_id] |
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if confidence > 0.5: |
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center_x = int(detection[0] * Width) |
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center_y = int(detection[1] * Height) |
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w = int(detection[2] * Width) |
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h = int(detection[3] * Height) |
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x = center_x - w / 2 |
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y = center_y - h / 2 |
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class_ids.append(class_id) |
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confidences.append(float(confidence)) |
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boxes.append([x, y, w, h]) |
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indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold) |
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for i in indices: |
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i = i[0] |
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box = boxes[i] |
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x = box[0] |
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y = box[1] |
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w = box[2] |
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h = box[3] |
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draw_prediction( |
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image, |
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class_ids[i], |
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confidences[i], |
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round(x), |
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round(y), |
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round(x + w), |
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round(y + h), |
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) |
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cv2.imshow("object detection", image) |
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cv2.waitKey() |
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cv2.imwrite("object-detection.jpg", image) |
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cv2.destroyAllWindows()
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