Added Chapter 9

Author: goodrahstar

Chapter 9/object_detection_ImageAI.py

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+from flask import Flask, request, jsonify, redirect
+import os , json
+from imageai.Detection import ObjectDetection
+
+model_path = os.getcwd()
+
+PRE_TRAINED_MODELS = ["resnet50_coco_best_v2.0.1.h5"]
+
+
+# Creating ImageAI objects and loading models
+
+object_detector = ObjectDetection()
+object_detector.setModelTypeAsRetinaNet()
+object_detector.setModelPath( os.path.join(model_path , PRE_TRAINED_MODELS[0]))
+object_detector.loadModel()
+object_detections = object_detector.detectObjectsFromImage(input_image='sample.jpg')
+
+# Define model paths and the allowed file extentions
+UPLOAD_FOLDER = model_path
+ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+
+def allowed_file(filename):
+    return '.' in filename and \
+           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+
+@app.route('/predict', methods=['POST'])
+def upload_file():
+    if request.method == 'POST':
+        # check if the post request has the file part
+        if 'file' not in request.files:
+            print('No file part')
+            return redirect(request.url)
+        file = request.files['file']
+        # if user does not select file, browser also
+        # submit a empty part without filename
+        if file.filename == '':
+            print('No selected file')
+            return redirect(request.url)
+        if file and allowed_file(file.filename):
+            filename = file.filename
+            file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) 
+            file.save(file_path) 
+
+    try:
+        object_detections = object_detector.detectObjectsFromImage(input_image=file_path)
+    except Exception as ex:
+        return jsonify(str(ex))
+    resp = []
+    for eachObject in object_detections :
+        resp.append([eachObject["name"],
+                     round(eachObject["percentage_probability"],3)
+                     ]
+                    )
+
+
+    return json.dumps(dict(enumerate(resp)))
+    
+if __name__ == "__main__":
+    app.run(host='0.0.0.0', port=4445)
+    

Chapter 9/object_detection_OpenCV.py

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+
+import cv2
+import matplotlib
+from matplotlib import colors
+from matplotlib import pyplot as plt
+import numpy as np
+from __future__ import division
+
+
+def show(image):
+    # Figure size in inches
+    plt.figure(figsize=(15, 15))
+    
+    # Show image, with nearest neighbour interpolation
+    plt.imshow(image, interpolation='nearest')
+    
+def show_hsv(hsv):
+    rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
+    show(rgb)
+    
+def show_mask(mask):
+    plt.figure(figsize=(10, 10))
+    plt.imshow(mask, cmap='gray')
+    
+def overlay_mask(mask, image):
+    rgb_mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB)
+    img = cv2.addWeighted(rgb_mask, 0.5, image, 0.5, 0)
+    show(img)
+
+
+def find_biggest_contour(image):
+    image = image.copy()
+    im2,contours, hierarchy = cv2.findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+
+    contour_sizes = [(cv2.contourArea(contour), contour) for contour in contours]
+    biggest_contour = max(contour_sizes, key=lambda x: x[0])[1]
+ 
+    mask = np.zeros(image.shape, np.uint8)
+    cv2.drawContours(mask, [biggest_contour], -1, 255, -1)
+    return biggest_contour, mask
+
+def circle_countour(image, countour):
+    image_with_ellipse = image.copy()
+    ellipse = cv2.fitEllipse(countour)
+
+    cv2.ellipse(image_with_ellipse, ellipse, (0,255,0), 2)
+    return image_with_ellipse
+
+
+image = cv2.imread('./Downloads/ferrari.jpg')
+
+
+
+image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+max_dimension = max(image.shape)
+scale = 700/max_dimension
+image = cv2.resize(image, None, fx=scale,fy=scale)
+
+ # Convert from RGB to HSV
+image_blur = cv2.GaussianBlur(image, (7, 7), 0)
+image_blur_hsv = cv2.cvtColor(image_blur, cv2.COLOR_RGB2HSV)
+
+# filter by color
+min_red = np.array([0, 100, 80])
+max_red = np.array([10, 256, 256])
+mask1 = cv2.inRange(image_blur_hsv, min_red, max_red)
+
+# filter by brigness
+min_red = np.array([170, 100, 80])
+max_red = np.array([180, 256, 256])
+mask2 = cv2.inRange(image_blur_hsv, min_red, max_red)
+
+mask = mask1 + mask2
+
+
+
+
+
+kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15, 15))
+mask_closed = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+mask_clean = cv2.morphologyEx(mask_closed, cv2.MORPH_OPEN, kernel)
+
+
+
+
+big_contour, red_mask = find_biggest_contour(mask_clean)
+
+overlay = overlay_mask(red_mask, image)
+
+circled = circle_countour(overlay, big_contour)
+
+show(circled)
+
+bgr = cv2.cvtColor(circled, cv2.COLOR_RGB2BGR)
+
+