hjm

LICENSE

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+MIT License
+
+Copyright (c) 2022 dineshssdn-867
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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-# FutureTEST
+# fish_species_detection

main.py

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+from flask import Flask, render_template, request, redirect, flash, url_for, render_template_string
+import urllib.request
+from werkzeug.utils import secure_filename
+from fishdetectionui.main_detect_fish import obj_model_predict
+from fishdetectionui.main_classify_fish import getPrediction
+import os
+import numpy as np
+import cv2
+import wikipedia
+
+fish_species_app = Flask(__name__)
+
+@fish_species_app.route("/")
+@fish_species_app.route("/home")
+def home():
+    return render_template("index.html")
+
+
+@fish_species_app.route('/predict_fish_form',  methods=['GET'])
+def predict_fish_form():
+    return render_template('case-single.html', predicted=False)
+
+@fish_species_app.route('/count_fish_form',  methods=['GET'])
+def count_fish_form():
+    return render_template('showcases.html', predicted=False)
+
+
+@fish_species_app.route('/classify_fish', methods=['POST'])
+def classify_fish():
+    print(request.url)
+    if request.method == 'POST':
+        if 'image' not in request.files:
+            flash('No file part')
+            return redirect(request.referrer)
+        file = request.files['image']
+        if file:
+            # convert string of image data to uint8
+            nparr = np.fromstring(request.files['image'].read(), np.uint8)
+            # decode image
+            img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+            label1, acc1 = getPrediction(img)
+            flash(label1)
+            flash(acc1)
+            summary = wikipedia.summary(label1)
+            return render_template("blog-single.html", label1=label1, acc1=acc1, summary=summary)
+
+
+@fish_species_app.route('/count_fish', methods=['POST'])
+def count_fish():
+    print(request.url)
+    if request.method == 'POST':
+        if 'image' not in request.files:
+            flash('No file part')
+            return redirect(request.referrer)
+        file = request.files['image']
+        if file:
+            # convert string of image data to uint8
+            nparr = np.fromstring(request.files['image'].read(), np.uint8)
+            # decode image
+            img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+            cv2.imwrite('fish.jpg', img)
+            filename, count = obj_model_predict('fish.jpg')
+            print(filename)
+            print(count)
+            return render_template("blog-double.html", filename=filename, count=count)
+
+
+if __name__ == "__main__":
+    fish_species_app.secret_key = 'super secret key'
+    fish_species_app.run(debug=True)

main_classify_fish.py

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+import cv2
+import numpy as np
+from tensorflow.keras.preprocessing.image import load_img
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Dense
+from tensorflow.keras.layers import GlobalAveragePooling2D 
+
+fish_classes = ['Polyprion americanus', 'Scomber japonicus',
+       'Trigloporus lastoviza', 'Dasyatis centroura',
+       'Tetrapturus belone', 'Anthias anthias', 'Coris julis',
+       'Solea solea', 'Boops boops', 'Chlorophthalmus agassizi',
+       'Squalus acanthias', 'Trachinus draco', 'Pseudocaranx dentex',
+       'Belone belone', 'Gobius niger', 'Mugil cephalus', 'Phycis phycis',
+       'Atherinomorus lacunosus', 'Rhinobatos cemiculus',
+       'Epinephelus caninus']
+
+
+def model_initalize():
+    # include_top = False means that we doesnt include fully connected top layer we will add them accordingly
+    mobilenetV2 = MobileNetV2(include_top = False, input_shape = (224,224,3), weights = 'imagenet')
+
+    # training of all the convolution is set to false
+    for layer in mobilenetV2.layers:
+        layer.trainable = False
+
+    x = GlobalAveragePooling2D()(mobilenetV2.output)    
+    predictions = Dense(20, activation='softmax')(x)
+    model_mobilenetV2 = Model(inputs = mobilenetV2.input, outputs = predictions)
+    model_mobilenetV2.load_weights('model_mobilenetV2.h5')
+    return model_mobilenetV2
+
+model = model_initalize()
+
+def getPrediction(image):
+    image = img_to_array(image)
+    image = cv2.resize(image, (224,224))
+    image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
+    image = preprocess_input(image)
+    probs = model.predict(image)
+    yhat = np.argmax(probs)
+    return str(fish_classes[yhat]), str(probs[0][yhat])

main_detect_fish.py

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+from roboflow import Roboflow
+import string
+import random
+import json
+import os
+import shutil
+
+rf = Roboflow(api_key="K57tUktyTD0l9DKKeQBE")
+workspace_ = rf.workspace()
+project = workspace_.project("detect-for-me")
+version = project.version(1)
+model = version.model
+
+def obj_model_predict(image):
+    prediction = model.predict(image) 
+    res = 'predictions_' + ''.join(random.choices(string.ascii_uppercase +
+                                string.digits, k=7)) + '.jpg'
+    prediction.save(output_path=res)
+    shutil.move('/workspace/fish_species_detection/'+res, "/workspace/fish_species_detection/static/predictions/"+res)
+    count = len(prediction.json()['predictions'])
+    return "/static/predictions/"+res, str(count)
+

requirements.txt

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+absl-py==1.2.0
+astunparse==1.6.3
+beautifulsoup4==4.11.1
+cachetools==5.2.0
+certifi==2021.5.30
+chardet==4.0.0
+charset-normalizer==2.1.0
+click==8.1.3
+cycler==0.10.0
+Flask==2.1.3
+flatbuffers==1.12
+fonttools==4.34.4
+gast==0.4.0
+glob2==0.7
+google-auth==2.9.1
+google-auth-oauthlib==0.4.6
+google-pasta==0.2.0
+grpcio==1.47.0
+h5py==3.7.0
+idna==2.10
+importlib-metadata==4.12.0
+itsdangerous==2.1.2
+Jinja2==3.1.2
+keras==2.9.0
+Keras-Preprocessing==1.1.2
+kiwisolver==1.3.1
+libclang==14.0.1
+Markdown==3.4.1
+MarkupSafe==2.1.1
+matplotlib==3.5.2
+numpy==1.23.1
+oauthlib==3.2.0
+opencv-python-headless==4.2.0.32
+opt-einsum==3.3.0
+packaging==21.3
+Pillow==9.0.1
+protobuf==3.19.4
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pyparsing==2.4.7
+python-dateutil==2.8.2
+python-dotenv==0.20.0
+PyYAML==6.0
+requests==2.28.1
+requests-oauthlib==1.3.1
+requests-toolbelt==0.9.1
+roboflow==0.2.11
+rsa==4.9
+six==1.16.0
+soupsieve==2.3.2.post1
+tensorboard==2.9.1
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+tensorflow==2.9.1
+tensorflow-estimator==2.9.0
+tensorflow-io-gcs-filesystem==0.26.0
+termcolor==1.1.0
+tqdm==4.64.0
+typing_extensions==4.3.0
+urllib3==1.26.6
+Werkzeug==2.1.2
+wget==3.2
+wikipedia==1.4.0
+wrapt==1.14.1
+zipp==3.8.1