Merge branch 'IT18216974' into 'master'

merge

See merge request !2

api/boundary-line-api/Procfile

+web: gunicorn app:app
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api/boundary-line-api/app.py

+import cv2
+import numpy as np
+from flask import Flask, jsonify, request
+import os
+
+app = Flask(__name__)
+
+@app.route("/")
+def indexfun():
+  return "welcome"
+
+@app.route("/predict", methods=['POST'])
+def predictfun():
+  net = cv2.dnn.readNet("yolov4-custom_2000.weights", "yolov4-custom.cfg")
+
+  file = request.files['file']
+  image_path = "./images/" + file.filename
+  file.save(image_path)
+
+  value_x = 0
+  value_y = 0
+  value_w = 0
+  value_h = 0
+
+  img = cv2.imread(image_path)
+  height, width, _ = img.shape
+
+  blob = cv2.dnn.blobFromImage(img, 1/255, (416,416), (0,0,0), swapRB=True, crop=False)
+  net.setInput(blob)
+
+  output_layers_names = net.getUnconnectedOutLayersNames()
+  layerOutputs = net.forward(output_layers_names)
+
+  boxes = []
+  class_ids = []
+  confidences = []
+
+  for output in layerOutputs:
+    for detection in output:
+      scores = detection[5:]
+      class_id = np.argmax(scores)
+      confidence = scores[class_id]
+
+      if confidence > 0.5:
+        center_x = int(detection[0]*width)
+        center_y = int(detection[1]*height)
+        w = int(detection[2]*width)
+        h = int(detection[3]*height)
+
+        x = int(center_x - w/2)
+        y = int(center_y - h/2)
+
+        boxes.append([x, y, w, h])
+        confidences.append((float(confidence)))
+        class_ids.append(class_id)
+
+  indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
+
+  if(len(boxes)>0):
+
+    for i in indexes.flatten():
+      x, y, w, h = boxes[i]
+      #[70, 69, 116, 194]
+
+      value_x = x
+      value_y = y
+      value_w = x+w
+      value_h = y+h
+
+    os.remove(image_path)
+
+    return jsonify({
+      'detected' : 1,
+      'x': value_x,
+      'w': value_w,
+      'y': value_y,
+      'h': value_h,
+    })
+
+  else:
+    os.remove(image_path)
+    return jsonify({
+      'detected' : 0,
+      'x': 0,
+      'w': 0,
+      'y': 0,
+      'h': 0,
+    })
+
+if __name__ == "__main__":
+  app.run(debug=True)

api/boundary-line-api/obj.names

+staircase
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api/boundary-line-api/requirements.txt

+click==8.0.1
+colorama==0.4.4
+Flask==2.0.1
+gunicorn==20.1.0
+itsdangerous==2.0.1
+Jinja2==3.0.1
+MarkupSafe==2.0.1
+Werkzeug==2.0.1
+opencv-contrib-python-headless
+numpy
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api/send-file-api/boundary-app.py

+import requests
+
+API_URL = "https://boundary-detection.herokuapp.com/predict"; 
+
+files = {'file': ("images.jpg", open('images.jpg', 'rb'))} 
+resp = requests.post(API_URL, files=files) 
+
+data = resp.json()
+print(resp.status_code)
+print(data)
+print(data['x'])
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api/send-file-api/detect-staircase-app.py

+import requests
+
+API_URL = "https://staircase-detection.herokuapp.com/predict"; 
+
+files = {'file': ("1.png", open('1.png', 'rb'))} 
+resp = requests.post(API_URL, files=files) 
+
+data = resp.json()
+print(resp.status_code) 
+print(data)
+print(data['type'])
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api/send-file-api/step-count-app.py

+import requests
+import cv2
+
+#get x,y,w,h
+API_URL = "https://boundary-detection.herokuapp.com/predict"; 
+files = {'file': ("images.jpg", open('images.jpg', 'rb'))} 
+resp = requests.post(API_URL, files=files) 
+data = resp.json()
+
+if(resp.status_code == 200):
+    x = int(data['x'])
+    y = int(data['y'])
+    w = int(data['w'])
+    h = int(data['h'])
+
+    img = cv2.imread('images.jpg')
+    img = img[y:h,x:w]
+    cv2.imwrite('cropped.jpg',img)
+
+    API_URL = "https://step-count.herokuapp.com/predict"; 
+    files = {'file': ('cropped.jpg',open("cropped.jpg", 'rb'))} 
+    resp = requests.post(API_URL, files=files) 
+    
+    data = resp.json()
+    print(resp.status_code) 
+    print(data)
+    print(data['steps'])
+else:
+    print("No staircase")
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api/staircase-detection/Procfile

+web: gunicorn app:app
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api/staircase-detection/app.py

+from flask import Flask, request, jsonify
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import  load_img
+import numpy as np
+import os
+
+app = Flask(__name__)
+
+
+@app.route('/')
+def indexfn():
+    return "<h1> Welcome </h1>"
+
+@app.route('/predict', methods = ['POST'])
+def predictfn():
+    class_mask = ['Staircase not detected', 'Staircase Detected']
+    file = request.files['file']
+    image_path = "./images/" + file.filename
+    file.save(image_path)
+
+    try:
+        img = load_img(image_path, target_size=(224,224))
+        img = np.asarray(img)
+        img = np.expand_dims(img, axis=0)
+        saved_model = load_model('vgg16_1.h5')
+        output = saved_model.predict(img)
+        pred = class_mask[np.argmax(output)]
+        os.remove(path=image_path)
+        return jsonify({'type': pred})
+
+    except BaseException as err:
+        return jsonify({'error': 'error at prediction'})
+
+if __name__ == "__main__":
+    app.run(debug=True)
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api/staircase-detection/requirements.txt

+click==8.0.1
+colorama==0.4.4
+Flask==2.0.1
+gunicorn==20.1.0
+itsdangerous==2.0.1
+Jinja2==3.0.1
+MarkupSafe==2.0.1
+Werkzeug==2.0.1
+Keras==2.4.3
+tensorboard==2.5.0
+tensorflow-cpu==2.5.0
+numpy
+Pillow==8.2.0
+# PILL
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api/staircase-detection/runtime.txt

+python-3.9.6

api/step-count/Procfile

+web: gunicorn app:app
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api/step-count/app.py

+from flask import Flask, request, jsonify
+import math
+import cv2
+import os
+
+app = Flask(__name__)
+
+@app.route("/")
+def indexfun():
+    return "welcome"
+
+@app.route("/predict",  methods = ['POST'])
+def getStepCount():
+    file = request.files['file']
+    image_path = "./images/" + file.filename
+    file.save(image_path)
+
+    image = cv2.imread(image_path)
+    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+
+    gaussian_image = cv2.GaussianBlur(gray_image,(9,9),0,0)
+
+    canny_image = cv2.Canny(gaussian_image,80,240,3)
+    # canny_image = cv2.Canny(gaussian_image,50,120)
+
+    output_image = cv2.cvtColor(canny_image, cv2.COLOR_GRAY2BGR)
+    output_image2 = cv2.cvtColor(canny_image, cv2.COLOR_GRAY2BGR)
+    output_image3 = cv2.cvtColor(canny_image, cv2.COLOR_GRAY2BGR)
+
+    lines = list()
+
+    lines = cv2.HoughLinesP(canny_image,1,math.pi/180,30,40,5)
+
+    for i in range(0,len(lines)):
+        line = lines[i][0]
+        cv2.line(output_image,(line[0],line[1]),(line[2],line[3]),(0,0,255),3,5)
+
+
+    y_values = list()
+    x_values = list()
+
+    # print(lines)
+    # print("################")
+    # print(lines[0][0]) #[ 25 189 167 189]
+    # print(lines.shape)
+
+    # remove vertical lines
+    for i in range(0, len(lines)):
+        line = lines[i][0]
+        #line -> [ 25 189 167 189]
+        #line -> y1-[1] -> 189
+        #line -> y2-[3] -> 189
+
+        if (abs(line[1] - line[3])) < 5:
+            y_values.append([line[1],line[3]])
+            x_values.append([line[0],line[2]])
+        
+        #line -> x1-[0] -> 25
+        #line -> x2-[2] -> 167
+        if (abs(line[0] - line[2])) > 100:
+            y_values.append([line[1],line[3]])
+            x_values.append([line[0],line[2]])
+
+    # print(y_values)
+    # print("################")
+    # print(y_values[0]) #[189, 189]
+
+    for i in range(0, len(y_values)):
+        # y_ax -> [189, 189]
+        y_ax = y_values[i]
+        x_ax = x_values[i]
+
+        cv2.line(output_image2,(x_ax[0],y_ax[0]),(x_ax[1],y_ax[1]),(0,0,255),3,5)
+
+    # remove unwanted horizontal lines
+
+    # print(y_values)
+    # print("################")
+    # print(y_values[0][0]) #189
+    # # y_ax -> 189
+
+    y_ax = list()
+    y_ax.append(y_values[0][0])
+    stair_count = 0
+    flag = 1
+
+    for i in range(0, len(y_values)):
+        line = y_values[i]
+        #line -> [189, 189]
+
+        for j in range(0, len(y_ax)):
+            # y_ax -> 189
+            # This value will calculate with help of step height and width (15,40,60)
+
+            step_heigth_mean = 15
+
+            if (abs(y_ax[j] - line[1]) < step_heigth_mean):
+                flag = 0
+
+        if flag:
+            cv2.line(output_image3,(0,line[1]),(output_image3.shape[1],line[1]),(0,0,255),3,5)
+            stair_count = stair_count + 1
+            y_ax.append(line[1])
+
+        flag = 1
+
+
+    print("Stair count :"+ str(stair_count))
+    os.remove(image_path)
+    return jsonify({"steps": str(stair_count)})
+if __name__ == "__main__":
+    app.run(debug=True)
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api/step-count/requirements.txt

+click==8.0.1
+colorama==0.4.4
+Flask==2.0.1
+gunicorn==20.1.0
+itsdangerous==2.0.1
+Jinja2==3.0.1
+MarkupSafe==2.0.1
+Werkzeug==2.0.1
+opencv-contrib-python-headless
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api/step-count/runtime.txt

+python-3.9.6