add body detector, mod pull sheet

1 files changed, 65 insertions, 0 deletions

diff --git a/megapixels/app/processors/person_detector.py b/megapixels/app/processors/person_detector.py
new file mode 100644
index 00000000..6daa8c40
--- /dev/null
+++ b/megapixels/app/processors/person_detector.py
@@ -0,0 +1,65 @@
+import sys
+import os
+from os.path import join
+from pathlib import Path
+
+import cv2 as cv
+import numpy as np
+import imutils
+import operator
+
+from app.utils import im_utils, logger_utils
+from app.models.bbox import BBox
+from app.settings import app_cfg as cfg
+from app.settings import types
+
+
+class DetectorCVDNN:
+
+  # MobileNet SSD
+  dnn_scale = 0.007843  # fixed
+  dnn_mean = (127.5, 127.5, 127.5)  # fixed
+  dnn_crop = False  # crop or force resize
+  blob_size = (300, 300)
+  conf = 0.95
+
+  # detect
+  CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
+    "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
+    "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
+    "sofa", "train", "tvmonitor"]
+  
+  def __init__(self):
+    self.log = logger_utils.Logger.getLogger()
+    fp_prototxt = join(cfg.DIR_MODELS_CAFFE, 'mobilenet_ssd', 'MobileNetSSD_deploy.prototxt')
+    fp_model = join(cfg.DIR_MODELS_CAFFE, 'mobilenet_ssd', 'MobileNetSSD_deploy.caffemodel')
+    self.net = cv.dnn.readNet(fp_prototxt, fp_model)
+    self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
+    self.net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
+
+  def detect(self, im, conf=None, largest=False, pyramids=None, zone=False, blob_size=None):
+    """Detects bodies and returns (list) of (BBox)"""
+    conf = self.conf if conf is None else conf
+    blob_size = self.blob_size if blob_size is None else blob_size
+    im = cv.resize(im, blob_size)
+    dim = im.shape[:2][::-1]
+    blob = cv.dnn.blobFromImage(im, self.dnn_scale, dim, self.dnn_mean)
+    self.net.setInput(blob)
+    net_outputs = self.net.forward()
+
+    bboxes = []
+    for i in range(0, net_outputs.shape[2]):
+      det_conf = float(net_outputs[0, 0, i, 2])
+      bounds = np.array(net_outputs[0, 0, i, 3:7])  # bug: ensure all x,y within 1.0 ?
+      if det_conf > conf and np.all(bounds < 1):
+        idx = int(net_outputs[0, 0, i, 1])
+        if self.CLASSES[idx] == "person":
+          rect_norm = net_outputs[0, 0, i, 3:7]
+          bboxes.append(BBox(*rect_norm))
+
+    if largest and len(bboxes) > 1:
+      # only keep largest
+      bboxes.sort(key=operator.attrgetter('area'), reverse=True)
+      bboxes = [bboxes[0]]
+
+    return bboxes
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