Upload 12 files

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

.idea/T24.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N803" />
+          <option value="N802" />
+          <option value="N806" />
+          <option value="N801" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/T24.iml" filepath="$PROJECT_DIR$/.idea/T24.iml" />
+    </modules>
+  </component>
+</project>

.idea/workspace.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="AutoImportSettings">
+    <option name="autoReloadType" value="SELECTIVE" />
+  </component>
+  <component name="ChangeListManager">
+    <list default="true" id="15e16f15-f7a0-47bd-8da7-9e3e2bf12e47" name="Changes" comment="" />
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="MarkdownSettingsMigration">
+    <option name="stateVersion" value="1" />
+  </component>
+  <component name="ProjectId" id="2l1D3W1d2Nr45X1uRVePz8Qnu8H" />
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showLibraryContents" value="true" />
+  </component>
+  <component name="PropertiesComponent">{
+  &quot;keyToString&quot;: {
+    &quot;RunOnceActivity.OpenProjectViewOnStart&quot;: &quot;true&quot;,
+    &quot;RunOnceActivity.ShowReadmeOnStart&quot;: &quot;true&quot;,
+    &quot;last_opened_file_path&quot;: &quot;D:/SEBASTIAN GEORGE/S5/f/EEg&quot;,
+    &quot;settings.editor.selected.configurable&quot;: &quot;com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable&quot;
+  }
+}</component>
+  <component name="RunManager">
+    <configuration name="dataCollection" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true">
+      <module name="T24" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <option name="SCRIPT_NAME" value="D:\SEBASTIAN GEORGE\S5\f\T24\dataCollection.py" />
+      <option name="PARAMETERS" value="" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
+  </component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
+  <component name="SvnConfiguration">
+    <configuration>C:\Users\user\AppData\Roaming\Subversion</configuration>
+  </component>
+  <component name="TaskManager">
+    <task active="true" id="Default" summary="Default task">
+      <changelist id="15e16f15-f7a0-47bd-8da7-9e3e2bf12e47" name="Changes" comment="" />
+      <created>1724338491973</created>
+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1724338491973</updated>
+    </task>
+    <servers />
+  </component>
+</project>

.ipynb_checkpoints/test-checkpoint.py

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+import cv2
+from cvzone.HandTrackingModule import HandDetector
+from cvzone.ClassificationModule import Classifier
+import numpy as np
+import math
+import pyttsx3
+from collections import deque
+
+# Initialize the text-to-speech engine
+engine = pyttsx3.init()
+voices = engine.getProperty('voices')
+engine.setProperty('voice', voices[1].id)
+rate = engine.getProperty('rate')
+engine.setProperty('rate', 125)
+
+cap = cv2.VideoCapture(0)
+detector = HandDetector(maxHands=1)
+classifier = Classifier("Model/keras_model.h5", "Model/labels.txt")
+offset = 20
+imgSize = 300
+labels = ["A", "B", "C"]
+
+# Deque to store the last few predictions
+prediction_history = deque(maxlen=3)
+last_prediction = None
+
+while True:
+    try:
+        success, img = cap.read()
+        if not success:
+            print("Failed to capture image")
+            continue
+
+        imgOutput = img.copy()
+        hands, img = detector.findHands(img)
+        if hands:
+            hand = hands[0]
+            x, y, w, h = hand['bbox']
+            imgWhite = np.ones((imgSize, imgSize, 3), np.uint8) * 255
+            try:
+                imgCrop = img[y - offset:y + h + offset, x - offset:x + w + offset]
+                if imgCrop.size == 0:
+                    raise ValueError("Empty image crop detected")
+
+                imgCropShape = imgCrop.shape
+                aspectRatio = h / w
+
+                if aspectRatio > 1:
+                    k = imgSize / h
+                    wCal = math.ceil(k * w)
+                    imgResize = cv2.resize(imgCrop, (wCal, imgSize))
+                    imgResizeShape = imgResize.shape
+                    wGap = math.ceil((imgSize - wCal) / 2)
+                    imgWhite[:, wGap:wCal + wGap] = imgResize
+                else:
+                    k = imgSize / w
+                    hCal = math.ceil(k * h)
+                    imgResize = cv2.resize(imgCrop, (imgSize, hCal))
+                    imgResizeShape = imgResize.shape
+                    hGap = math.ceil((imgSize - hCal) / 2)
+                    imgWhite[hGap:hCal + hGap, :] = imgResize
+
+                try:
+                    prediction, index = classifier.getPrediction(imgWhite, draw=False)
+                    print(prediction, index)
+
+                    prediction_history.append(labels[index])
+
+                    if len(prediction_history) == 3 and len(set(prediction_history)) == 1 and prediction_history[1]!=last_prediction:
+                        last_prediction=labels[index]
+                        engine.say(labels[index])
+                        engine.runAndWait()
+                        prediction_history = deque(maxlen=3)
+
+                except Exception as e:
+                    print("Error in classifier prediction:", e)
+
+            except cv2.error as e:
+                print("OpenCV error:", e)
+            except ValueError as e:
+                print(e)
+
+            cv2.rectangle(imgOutput, (x - offset, y - offset - 50),
+                          (x - offset + 90, y - offset - 50 + 50), (255, 0, 255), cv2.FILLED)
+            cv2.putText(imgOutput, labels[index], (x, y - 26), cv2.FONT_HERSHEY_COMPLEX, 1.7, (255, 255, 255), 2)
+            cv2.rectangle(imgOutput, (x - offset, y - offset),
+                          (x + w + offset, y + h + offset), (255, 0, 255), 4)
+            cv2.imshow("ImageCrop", imgCrop)
+            cv2.imshow("ImageWhite", imgWhite)
+
+        cv2.imshow("Image", imgOutput)
+        cv2.waitKey(1)
+
+    except Exception as e:
+        print("Unexpected error:", e)

Model/keras_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:69f7c8e1dcb153a962e7b6048597b74f2fe8b559f2bb9b970b2c50bccc92312a
+size 2453432

Model/labels.txt

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+0 A
+1 B
+2 C

dataCollection.py

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+import cv2
+from cvzone.HandTrackingModule import HandDetector
+import numpy as np
+import math
+import time
+
+cap = cv2.VideoCapture(0)
+detector = HandDetector(maxHands=1)
+offset = 20
+imgSize = 300
+folder = "Data/Hi"
+counter = 0
+
+while True:
+    success, img = cap.read()
+    if not success:
+        print("Failed to capture image")
+        continue
+
+    hands, img = detector.findHands(img)
+    if hands:
+        hand = hands[0]
+        x, y, w, h = hand['bbox']
+        imgWhite = np.ones((imgSize, imgSize, 3), np.uint8) * 255
+        try:
+            imgCrop = img[y - offset:y + h + offset, x - offset:x + w + offset]
+            if imgCrop.size == 0:
+                raise ValueError("Empty image crop detected")
+
+            imgCropShape = imgCrop.shape
+            aspectRatio = h / w
+            if aspectRatio > 1:
+                k = imgSize / h
+                wCal = math.ceil(k * w)
+                imgResize = cv2.resize(imgCrop, (wCal, imgSize))
+                imgResizeShape = imgResize.shape
+                wGap = math.ceil((imgSize - wCal) / 2)
+                imgWhite[:, wGap:wCal + wGap] = imgResize
+            else:
+                k = imgSize / w
+                hCal = math.ceil(k * h)
+                imgResize = cv2.resize(imgCrop, (imgSize, hCal))
+                imgResizeShape = imgResize.shape
+                hGap = math.ceil((imgSize - hCal) / 2)
+                imgWhite[hGap:hCal + hGap, :] = imgResize
+
+            cv2.imshow("ImageCrop", imgCrop)
+            cv2.imshow("ImageWhite", imgWhite)
+
+        except cv2.error as e:
+            print("OpenCV error:", e)
+        except ValueError as e:
+            print(e)
+
+    cv2.imshow("Image", img)
+    key = cv2.waitKey(1)
+    if key == ord("s"):
+        if imgCrop.size == 0:
+            continue
+        counter += 1
+        cv2.imwrite(f'{folder}/Image_{time.time()}.jpg', imgWhite)
+        print(counter)

test.py

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+import cv2
+from cvzone.HandTrackingModule import HandDetector
+from cvzone.ClassificationModule import Classifier
+import numpy as np
+import math
+import pyttsx3
+from collections import deque
+
+# Initialize the text-to-speech engine
+engine = pyttsx3.init()
+voices = engine.getProperty('voices')
+engine.setProperty('voice', voices[1].id)
+rate = engine.getProperty('rate')
+engine.setProperty('rate', 125)
+
+cap = cv2.VideoCapture(0)
+detector = HandDetector(maxHands=1)
+classifier = Classifier("Model/keras_model.h5", "Model/labels.txt")
+offset = 20
+imgSize = 300
+labels = ["A", "B", "C"]
+
+# Deque to store the last few predictions
+prediction_history = deque(maxlen=3)
+last_prediction = None
+
+while True:
+    try:
+        success, img = cap.read()
+        if not success:
+            print("Failed to capture image")
+            continue
+
+        imgOutput = img.copy()
+        hands, img = detector.findHands(img)
+        if hands:
+            hand = hands[0]
+            x, y, w, h = hand['bbox']
+            imgWhite = np.ones((imgSize, imgSize, 3), np.uint8) * 255
+            try:
+                imgCrop = img[y - offset:y + h + offset, x - offset:x + w + offset]
+                if imgCrop.size == 0:
+                    raise ValueError("Empty image crop detected")
+
+                imgCropShape = imgCrop.shape
+                aspectRatio = h / w
+
+                if aspectRatio > 1:
+                    k = imgSize / h
+                    wCal = math.ceil(k * w)
+                    imgResize = cv2.resize(imgCrop, (wCal, imgSize))
+                    imgResizeShape = imgResize.shape
+                    wGap = math.ceil((imgSize - wCal) / 2)
+                    imgWhite[:, wGap:wCal + wGap] = imgResize
+                else:
+                    k = imgSize / w
+                    hCal = math.ceil(k * h)
+                    imgResize = cv2.resize(imgCrop, (imgSize, hCal))
+                    imgResizeShape = imgResize.shape
+                    hGap = math.ceil((imgSize - hCal) / 2)
+                    imgWhite[hGap:hCal + hGap, :] = imgResize
+
+                try:
+                    prediction, index = classifier.getPrediction(imgWhite, draw=False)
+                    print(prediction, index)
+
+                    prediction_history.append(labels[index])
+
+                    if len(prediction_history) == 3 and len(set(prediction_history)) == 1 and prediction_history[1]!=last_prediction:
+                        last_prediction=labels[index]
+                        engine.say(labels[index])
+                        engine.runAndWait()
+                        prediction_history = deque(maxlen=3)
+
+                except Exception as e:
+                    print("Error in classifier prediction:", e)
+
+            except cv2.error as e:
+                print("OpenCV error:", e)
+            except ValueError as e:
+                print(e)
+
+            cv2.rectangle(imgOutput, (x - offset, y - offset - 50),
+                          (x - offset + 90, y - offset - 50 + 50), (255, 0, 255), cv2.FILLED)
+            cv2.putText(imgOutput, labels[index], (x, y - 26), cv2.FONT_HERSHEY_COMPLEX, 1.7, (255, 255, 255), 2)
+            cv2.rectangle(imgOutput, (x - offset, y - offset),
+                          (x + w + offset, y + h + offset), (255, 0, 255), 4)
+            cv2.imshow("ImageCrop", imgCrop)
+            cv2.imshow("ImageWhite", imgWhite)
+
+        cv2.imshow("Image", imgOutput)
+        cv2.waitKey(1)
+
+    except Exception as e:
+        print("Unexpected error:", e)