def FasterRCNN():
st.title("FasterRCNN")
f = open("FasterRCNN.txt", "r")
st.markdown(f.readline(), unsafe_allow_html=True)
st.image("faster_show.png", use_column_width=True)
st.markdown(f.readline(), unsafe_allow_html=True)
FRCNN, session = frcnn("faster_rcnn_face_0010.h5")
class_names = ["BG", "Face"]
file = st.file_uploader("Upload image")
if file:
ImgNet = ImageNetwork(file, FRCNN, session)
r, image = fast_predict(ImgNet)
frcnn_visualize.display_instances(image,
r['rois'],
None,
r['class_ids'],
class_names,
r['scores'],
title="",
show_mask=False)
st.image(file, caption="Uploaded Image", use_column_width=True)
st.image("FasterRCNN_output.jpg",
caption="Faces Detected",
use_column_width=True)
# except:
# st.write("File Uploaded is not an image. Please upload am Image")
return
image_id = random.choice(dataset.image_ids)
image, image_meta, gt_class_id, gt_bbox =\
data.load_image_gt(dataset, config, image_id)
info = dataset.image_info[image_id]
print("image ID: {}.{} ({}) {}".format(info["source"], info["id"], image_id,
dataset.image_reference(image_id)))
# Run object detection
results = model.detect([image], verbose=1)
# Display results
ax = get_ax(1)
r = results[0]
visualize.display_instances(image,
r['rois'],
r['class_ids'],
dataset.class_names,
r['scores'],
ax=ax,
title="Predictions")
log("gt_class_id", gt_class_id)
log("gt_bbox", gt_bbox)
#%% Precision-Recall
# Draw precision-recall curve
AP, precisions, recalls, overlaps = utils.compute_ap(gt_bbox, gt_class_id,
r['rois'], r['class_ids'],
r['scores'])
visualize.plot_precision_recall(AP, precisions, recalls)
#%%
# Grid of ground truth objects and their predictions
# Load trained weights
model.load_weights(model_path, by_name=True)
#%% Test on a random image
image_id = random.choice(dataset_val.image_ids)
original_image, image_meta, gt_class_id, gt_bbox =\
data.load_image_gt(dataset_val, inference_config, image_id)
log("original_image", original_image)
log("image_meta", image_meta)
log("gt_class_id", gt_class_id)
log("gt_bbox", gt_bbox)
visualize.display_instances(original_image,
gt_bbox,
gt_class_id,
dataset_train.class_names,
ax=get_ax())
results = model.detect([original_image], verbose=1)
r = results[0]
visualize.display_instances(original_image,
r['rois'],
r['class_ids'],
dataset_val.class_names,
r['scores'],
ax=get_ax())
AP, precisions, recalls, overlaps =\
utils.compute_ap(gt_bbox, gt_class_id, r["rois"], r["class_ids"], r["scores"])
print(AP)