import cv2
import numpy as np
import sys

weights = 'models/detection.weights'
cfg = 'models/detection.cfg'
img = sys.argv[1]

net = cv2.dnn.readNet(weights, cfg)
blob = cv2.dnn.blobFromImage(cv2.imread(img), 0.00392, (416,416), (0,0,0), True, crop=False)
net.setInput(blob)

layer_names = net.getLayerNames()
unconnected = net.getUnconnectedOutLayers()
indices = []
if isinstance(unconnected, np.ndarray):
    indices = unconnected.flatten()
else:
    try:
        indices = [u[0] if hasattr(u, '__iter__') else u for u in unconnected]
    except Exception:
        indices = list(unconnected)

output_layers = [layer_names[int(i) - 1] for i in indices]
print('output_layers=', output_layers)

outs = net.forward(output_layers)
print('outs len:', len(outs))
for i, om in enumerate(outs):
    print(i, 'rows', om.shape[0], 'cols', om.shape[1])
    for r in range(min(3, om.shape[0])):
        print(' row', r, om[r,:10])