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104 lines
2.8 KiB
104 lines
2.8 KiB
5 years ago
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"""USAGE
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5 years ago
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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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5 years ago
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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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