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"""USAGE
python examples/yolo_obj_detector.py \
-c ~/dev/obj-tracking/yolov3.cfg \
-w ~/dev/obj-tracking/yolov3.weights \
-cl ~/dev/obj-tracking/yolo/darknet/data/coco.names \
-i ~/dev/obj-tracking/person.jpg
python examples/yolo_obj_detector.py \
-c ~/syncthing/dropbox/tracking-obj/mytrain.cfg \
-w ~/syncthing/dropbox/tracking-obj/mytrain_final.weights \
-cl ~/syncthing/dropbox/tracking-obj/mytrain.names \
-i /media/sipp11/500BUP/handai_photos/test/6294.jpg
"""
import cv2
import argparse
import numpy as np
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="path to input image")
ap.add_argument("-c", "--config", required=True, help="path to yolo config file")
ap.add_argument(
"-w", "--weights", required=True, help="path to yolo pre-trained weights"
)
ap.add_argument(
"-cl", "--classes", required=True, help="path to text file containing class names"
)
args = ap.parse_args()
def get_output_layers(net):
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
return output_layers
def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
label = str(classes[class_id])
color = COLORS[class_id]
cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)
cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
image = cv2.imread(args.image)
Width = image.shape[1]
Height = image.shape[0]
scale = 0.00392
classes = None
with open(args.classes, "r") as f:
classes = [line.strip() for line in f.readlines()]
COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
net = cv2.dnn.readNet(args.weights, args.config)
blob = cv2.dnn.blobFromImage(image, scale, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(get_output_layers(net))
class_ids = []
confidences = []
boxes = []
conf_threshold = 0.5
nms_threshold = 0.4
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
center_x = int(detection[0] * Width)
center_y = int(detection[1] * Height)
w = int(detection[2] * Width)
h = int(detection[3] * Height)
x = center_x - w / 2
y = center_y - h / 2
class_ids.append(class_id)
confidences.append(float(confidence))
boxes.append([x, y, w, h])
indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
for i in indices:
i = i[0]
box = boxes[i]
x = box[0]
y = box[1]
w = box[2]
h = box[3]
draw_prediction(
image,
class_ids[i],
confidences[i],
round(x),
round(y),
round(x + w),
round(y + h),
)
cv2.imshow("object detection", image)
cv2.waitKey()
cv2.imwrite("object-detection.jpg", image)
cv2.destroyAllWindows()