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WIP: debug images

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blakeblackshear 2019-02-17 07:27:11 -06:00
parent f7c8b742e8
commit 7d4cfe43ad
1 changed files with 25 additions and 4 deletions
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29
detect_objects.py
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@ -43,7 +43,7 @@ categories = label_map_util.convert_label_map_to_categories(label_map, max_num_c
use_display_name=True) use_display_name=True)
category_index = label_map_util.create_category_index(categories) category_index = label_map_util.create_category_index(categories)
def detect_objects(cropped_frame, sess, detection_graph, region_size, region_x_offset, region_y_offset): def detect_objects(cropped_frame, sess, detection_graph, region_size, region_x_offset, region_y_offset, debug):
# Expand dimensions since the model expects images to have shape: [1, None, None, 3] # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(cropped_frame, axis=0) image_np_expanded = np.expand_dims(cropped_frame, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
@ -62,6 +62,19 @@ def detect_objects(cropped_frame, sess, detection_graph, region_size, region_x_o
[boxes, scores, classes, num_detections], [boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded}) feed_dict={image_tensor: image_np_expanded})
if debug:
if len([category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] > 0.5]) > 0:
vis_util.visualize_boxes_and_labels_on_image_array(
cropped_frame,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=4)
cv2.imwrite("/lab/debug/obj-{}-{}-{}.jpg".format(region_x_offset, region_y_offset, datetime.datetime.now().timestamp()), cropped_frame)
# build an array of detected objects # build an array of detected objects
objects = [] objects = []
for index, value in enumerate(classes[0]): for index, value in enumerate(classes[0]):
@ -212,7 +225,8 @@ def main():
region['motion_detected'], region['motion_detected'],
frame_shape, frame_shape,
region['size'], region['x_offset'], region['y_offset'], region['size'], region['x_offset'], region['y_offset'],
region['min_object_size'])) region['min_object_size'],
True))
motion_process.daemon = True motion_process.daemon = True
motion_processes.append(motion_process) motion_processes.append(motion_process)
@ -330,6 +344,7 @@ def fetch_frames(shared_arr, shared_frame_time, ready_for_frame_flags, frame_sha
# do the actual object detection # do the actual object detection
def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_motion, frame_shape, region_size, region_x_offset, region_y_offset): def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_motion, frame_shape, region_size, region_x_offset, region_y_offset):
debug = True
# shape shared input array into frame for processing # shape shared input array into frame for processing
arr = tonumpyarray(shared_arr).reshape(frame_shape) arr = tonumpyarray(shared_arr).reshape(frame_shape)
@ -383,12 +398,12 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_moti
# convert to RGB # convert to RGB
cropped_frame_rgb = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2RGB) cropped_frame_rgb = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2RGB)
# do the object detection # do the object detection
objects = detect_objects(cropped_frame_rgb, sess, detection_graph, region_size, region_x_offset, region_y_offset) objects = detect_objects(cropped_frame_rgb, sess, detection_graph, region_size, region_x_offset, region_y_offset, True)
# copy the detected objects to the output array, filling the array when needed # copy the detected objects to the output array, filling the array when needed
shared_output_arr[:] = objects + [0.0] * (60-len(objects)) shared_output_arr[:] = objects + [0.0] * (60-len(objects))
# do the actual motion detection # do the actual motion detection
def detect_motion(shared_arr, shared_frame_time, ready_for_frame, shared_motion, frame_shape, region_size, region_x_offset, region_y_offset, min_motion_area): def detect_motion(shared_arr, shared_frame_time, ready_for_frame, shared_motion, frame_shape, region_size, region_x_offset, region_y_offset, min_motion_area, debug):
# shape shared input array into frame for processing # shape shared input array into frame for processing
arr = tonumpyarray(shared_arr).reshape(frame_shape) arr = tonumpyarray(shared_arr).reshape(frame_shape)
@ -463,6 +478,10 @@ def detect_motion(shared_arr, shared_frame_time, ready_for_frame, shared_motion,
# if the contour is big enough, count it as motion # if the contour is big enough, count it as motion
contour_area = cv2.contourArea(c) contour_area = cv2.contourArea(c)
if contour_area > min_motion_area: if contour_area > min_motion_area:
if debug:
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(thresh, (x, y), (x + w, y + h), (0, 255, 0), 2)
motion_frames += 1 motion_frames += 1
# if there have been enough consecutive motion frames, report motion # if there have been enough consecutive motion frames, report motion
if motion_frames >= 3: if motion_frames >= 3:
@ -470,6 +489,8 @@ def detect_motion(shared_arr, shared_frame_time, ready_for_frame, shared_motion,
last_motion = now last_motion = now
break break
motion_frames = 0 motion_frames = 0
if debug and motion_frames > 0:
cv2.imwrite("/lab/debug/motion-{}-{}-{}.jpg".format(region_x_offset, region_y_offset, datetime.datetime.now().timestamp()), thresh)
if __name__ == '__main__': if __name__ == '__main__':
mp.freeze_support() mp.freeze_support()