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adds image visualization

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This commit is contained in:
Raphael Maenle
2022-03-17 11:49:00 +01:00
parent d7ab431fd6
commit 4af63a8520
3 changed files with 132 additions and 137 deletions
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71
inference.py
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@ -175,7 +175,30 @@ class YoloProcessing:
return box_centers, box_scales, objness, class_pred
def postprocessing(self, endnodes):
def process_to_picture(self, endnodes, data):
logits = self.postprocessing(endnodes)
self.visualize_image(logits, data)
def visualize_image(self, logits, data):
labels = data.get_labels("data/daria_labels.json")
image = visualize_boxes_and_labels_on_image_array(
data.dataset[0],
logits['detection_boxes'].numpy()[0],
logits['detection_classes'][0],
logits['detection_scores'].numpy()[0],
labels,
use_normalized_coordinates=True,
max_boxes_to_draw=100,
min_score_thresh=.5,
agnostic_mode=False,
line_thickness=4)
Image.fromarray(np.uint8(image)).save('/home/maintenance/test.png')
print("Successfully saved image")
def postprocessing(self, endnodes, count):
"""
endnodes is a list of 3 output tensors:
endnodes[0] - stride 32 of input
@ -235,6 +258,7 @@ class YoloProcessing:
max_total_size=100)
# adding offset to the class prediction and cast to integer
def translate_coco_2017_to_2014(nmsed_classes):
return np.vectorize(COCO_17_14.get)(nmsed_classes).astype(np.int32)
@ -242,6 +266,8 @@ class YoloProcessing:
nmsed_classes = tf.cast(tf.add(nmsed_classes, labels_offset), tf.int16)
nmsed_classes = translate_coco_2017_to_2014(nmsed_classes)
print(count)
return {'detection_boxes': nmsed_boxes,
'detection_scores': nmsed_scores,
'detection_classes': nmsed_classes,
@ -336,53 +362,43 @@ def test_async_yolo5():
fps = 0
now = time.time()
for i in range(1000):
for i in range(100):
fps += 1
if now + 1 < time.time():
print(fps)
fps = 0
now = time.time()
hailo.hailo_input(data.dataset)
out = None
while(out == None):
time.sleep(0.01)
time.sleep(0.0001)
out = hailo.hailo_output()
Thread(target=processor.postprocessing, args=[out, i]).start()
hailo.stop_hailo_thread()
logits = processor.postprocessing(out)
labels = data.get_labels("data/daria_labels.json")
image = visualize_boxes_and_labels_on_image_array(
data.dataset[0],
logits['detection_boxes'].numpy()[0],
logits['detection_classes'][0],
logits['detection_scores'].numpy()[0],
labels,
use_normalized_coordinates=True,
max_boxes_to_draw=100,
min_score_thresh=.5,
agnostic_mode=False,
line_thickness=4)
Image.fromarray(np.uint8(image)).save('/home/maintenance/test.png')
print("Successfully saved image")
def test_process_yolo5():
imageMeta = ImageMeta(640, 640, 3)
processor = YoloProcessing(imageMeta, classes=3)
processor = YoloProcessing(imageMeta, classes=4)
data = DataHandler('./data', imageMeta)
data.load_data(processor.preproc)
hailo = HailoHandler('hef/yolov5m_daria.hef')
out = hailo.run_hailo(data.dataset)
logits = processor.postprocessing(out)
now = time.time()
fps = 0
for i in range(100):
fps += 1
if now + 1 < time.time():
print(fps)
fps = 0
now = time.time()
out = hailo.run_hailo(data.dataset)
logits = processor.postprocessing(out)
labels = data.get_labels("data/daria_labels.json")
@ -398,7 +414,6 @@ def test_process_yolo5():
agnostic_mode=False,
line_thickness=4)
Image.fromarray(np.uint8(image)).save('/home/maintenance/test.png')
print("Successfully saved image")
if __name__ == "__main__":