phase2

.gitignore

@@ -3,3 +3,5 @@ db.sqlite3
 *.pyc
 detect_app/static/detect_app/images/input.jpg
 detect_app/static/detect_app/images/output.jpg
+model/frozen_inference_graph.pb
+model/graph.pbtxt

detect_app/detect.py

@@ -1,4 +1,50 @@
-import shutil
+import cv2
 
 def detect(input, output):
-    shutil.copy2(input, output)
+    classes = {0: 'background',
+                1: 'person', 2: 'bicycle', 3: 'car', 4: 'motorcycle', 5: 'airplane', 6: 'bus',
+                7: 'train', 8: 'truck', 9: 'boat', 10: 'traffic light', 11: 'fire hydrant',
+                13: 'stop sign', 14: 'parking meter', 15: 'bench', 16: 'bird', 17: 'cat',
+                18: 'dog', 19: 'horse', 20: 'sheep', 21: 'cow', 22: 'elephant', 23: 'bear',
+                24: 'zebra', 25: 'giraffe', 27: 'backpack', 28: 'umbrella', 31: 'handbag',
+                32: 'tie', 33: 'suitcase', 34: 'frisbee', 35: 'skis', 36: 'snowboard',
+                37: 'sports ball', 38: 'kite', 39: 'baseball bat', 40: 'baseball glove',
+                41: 'skateboard', 42: 'surfboard', 43: 'tennis racket', 44: 'bottle',
+                46: 'wine glass', 47: 'cup', 48: 'fork', 49: 'knife', 50: 'spoon',
+                51: 'bowl', 52: 'banana', 53: 'apple', 54: 'sandwich', 55: 'orange',
+                56: 'broccoli', 57: 'carrot', 58: 'hot dog', 59: 'pizza', 60: 'donut',
+                61: 'cake', 62: 'chair', 63: 'couch', 64: 'potted plant', 65: 'bed',
+                67: 'dining table', 70: 'toilet', 72: 'tv', 73: 'laptop', 74: 'mouse',
+                75: 'remote', 76: 'keyboard', 77: 'cell phone', 78: 'microwave', 79: 'oven',
+                80: 'toaster', 81: 'sink', 82: 'refrigerator', 84: 'book', 85: 'clock',
+                86: 'vase', 87: 'scissors', 88: 'teddy bear', 89: 'hair drier', 90: 'toothbrush'}
+
+    # Load a model imported from Tensorflow
+    tensorflowNet = cv2.dnn.readNetFromTensorflow('./model/frozen_inference_graph.pb', './model/graph.pbtxt')
+
+    # Input image
+    img = cv2.imread(input)
+    rows, cols, channels = img.shape
+
+    # Use the given image as input, which needs to be blob(s).
+    tensorflowNet.setInput(cv2.dnn.blobFromImage(img, size=(300, 300), swapRB=True, crop=False))
+
+    # Runs a forward pass to compute the net output
+    networkOutput = tensorflowNet.forward()
+
+    # Loop on the outputs
+    for detection in networkOutput[0,0]:
+        score = float(detection[2])
+        if score > 0.2:
+            left = detection[3] * cols
+            top = detection[4] * rows
+            right = detection[5] * cols
+            bottom = detection[6] * rows
+    
+            #draw a red rectangle around detected objects
+            cv2.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (0, 0, 255), thickness=2)
+            #draw category name in top left of rectangle
+            cv2.putText(img, classes[int(detection[1])], (int(left), int(top-4)), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2, 8)
+
+    cv2.imwrite(output, img)
+    # Show the image with a rectagle surrounding the detected objects 

download.sh

@@ -0,0 +1,4 @@
+#!/bin/sh
+mkdir -p model
+wget -O - 'http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz' | tar xvfzO - ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb > model/frozen_inference_graph.pb
+wget -O - 'https://raw.githubusercontent.com/opencv/opencv_extra/master/testdata/dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt' > model/graph.pbtxt

requirements.txt

@@ -1,5 +1,17 @@
 asgiref==3.2.3
+astroid==2.3.3
+autopep8==1.5
 Django==3.0.3
+isort==4.3.21
+lazy-object-proxy==1.4.3
+mccabe==0.6.1
+numpy==1.18.1
+opencv-python==4.2.0.32
 pkg-resources==0.0.0
+pycodestyle==2.5.0
+pylint==2.4.4
 pytz==2019.3
+six==1.14.0
 sqlparse==0.3.0
+typed-ast==1.4.1
+wrapt==1.11.2